Personal arXiv HTML Archive

The Anticipatory Transformer: Geometry-Steered Attention for Trajectory-Aware Reasoning

Standard transformers attend based on learned position encodings (sinusoidal, RoPE, ALiBi) that encode *where* tokens are in a sequence but not *what the sequence is doing* as a geometric process. I introduce the Anticipatory Transformer, a modified transformer architecture where seven geometric scalars derived from Anticipation Geometry (commitment, uncertainty, transition pressure, recovery margin, phase stiffness, novelty, stability) steer the multi-head attention mechanism via additive bias. The trajectory bias

Anticipation Geometry: Domain-General Trajectory Characterization with Knowledge Graph-Grounded Rewards

I present Anticipation Geometry, a mathematical framework that characterizes trajectories through arbitrary state spaces using seven geometric scalars: commitment, uncertainty, transition pressure, recovery margin, phase stiffness, novelty, and stability. These scalars are domain-general, operating on any sequence of vectors in a metric space equipped with a differentiable time parameter. I combine this framework with knowledge graph path-derived reward signals to create a unified system for both trajectory analysi

Cognitive Twin: Personality Transfer via Small-Model LoRA with Runtime Knowledge Graph Augmentation

We present the Cognitive Twin architecture, a three-component system that produces a faithful digital replica of a human operator's conversational persona without baking volatile domain knowledge into model weights. The architecture separates personality (a LoRA adapter trained on the operator's historical responses), knowledge (a live knowledge graph queried at inference time), and trajectory awareness (geometric scalars characterizing conversation dynamics). We find that a Qwen2.5-3B model with LoRA adapters on a

Cognitive Twin Synthesis: A Recursive Polymodal Framework for Autonomous Agent Identity from Conversational Corpora

We present a framework for constructing autonomous cognitive twins from large-scale conversational corpora. Building on the Recursive Polymodal Synthesis (RPS) framework, which fuses heterogeneous sensor modalities through Lipschitz-constrained fixed-point iteration, we extend cross-modal coherence theory from physical signals (accelerometer, gyroscope, heart rate) to cognitive modalities: linguistic style ($\mathcal{V}_L$), decision patterns ($\mathcal{V}_D$), domain knowledge ($\mathcal{V}_K$), value alignment ($

Cognitive Twin Synthesis: Theorems, Proofs, and Derivations

This document presents the mathematical foundations for constructing a cognitive twin using Recursive Polymodal Synthesis (RPS). We extend the original RPS framework from sensor modalities (motion, heart rate, audio) to cognitive modalities (linguistic style, decision patterns, knowledge, values, temporal behavior). We prove convergence of the cognitive synthesis operator, derive the coherence energy functional, establish bounds on identity drift, and formalize the autonomy ratchet protocol. All results build on th

Enhanced Topological Preference Optimization with Spatial Intelligence: A Unified Framework for Conversation Analysis

We present an enhanced Topological Preference Optimization (TPO) system that integrates spatial intelligence and cross-conversation consolidation for advanced conversation analysis. Our unified framework combines the topological structure analysis of TPO with the spatial coordinate systems and ring topology of Ring Contextual Propagation (RCP), creating a comprehensive system for modeling conversation dynamics and generating preference datasets. The system employs 4D spatial coordinates (x, y, z, t) to represent hi

Enhanced Topological Preference Optimization with Spatial Intelligence: A Unified Framework for Conversation Analysis

We present an enhanced Topological Preference Optimization (TPO) system that integrates spatial intelligence and cross-conversation consolidation for advanced conversation analysis. Our unified framework combines the topological structure analysis of TPO with the spatial coordinate systems and ring topology of Ring Contextual Propagation (RCP), creating a comprehensive system for modeling conversation dynamics and generating preference datasets. The system employs 4D spatial coordinates (x, y, z, t) to represent hi

CC-MotionGen: Audio-Conditioned Latent Motion Diffusion with Validation-Based Candidate Selection

CC-MotionGen is a diffusion-based generative system that produces time-indexed motion trajectories conditioned on audio features and optional high-level context. The system targets phrase-level generation: it consumes precomputed audio feature tensors and precomputed motion latents, trains a temporal one-dimensional U-Net denoiser under a Gaussian diffusion process, and performs inference by sampling multiple candidate futures and selecting the best output using a two-stage validation pipeline. The validation pipel

RAG++: Memory-Conditioned Candidate Selection with Trajectory-Aware Attention

Retrieval-Augmented Generation (RAG) systems typically treat retrieved context as a flat collection of documents, ignoring the structural and temporal relationships between conversation turns. We present RAG++, a trajectory-aware retrieval system that positions memories in a 5-dimensional coordinate space (depth, sibling order, homogeneity, temporal position, and complexity) and enforces context admissibility through cryptographically-verified slicing. Our system introduces three key innovations: (1) **Inverse Ring

CognitiveTwin: Architectural Foundations and Empirical Evaluation of Personalized Language Model Adaptation Through Trajectory-Aware Fine-Tuning

The construction of personalized language model instances capable of reproducing individual cognitive patterns, stylistic signatures, and domain-specific conceptual frameworks represents a significant advancement in the development of AI systems that function as cognitive extensions rather than generic tools. This paper presents the CognitiveTwin framework, a comprehensive architecture for creating personalized language model instances through trajectory-aware supervised fine-tuning on conversational interaction hi

Deterministic Provenance Engines for Autonomous Agent Systems: Architecture, Implementation, and Evaluation of the Graph Kernel

> **Manuscript Type:** Full Research Paper (V2 — Post-Audit Definitive Edition) > **Track:** AI Systems & Knowledge Infrastructure > **Date:** July 2026 > **Revision:** 2.0 — Incorporates DEP Audit findings, Evo³ roadmap, and implemented improvements

Policy-Governed Context Slicing for Autonomous Agent Systems: A Lightweight Knowledge Graph Approach

Autonomous AI agent systems face a fundamental challenge: constructing reproducible, trustworthy context windows from large conversational histories while enforcing governance policies over what information may influence downstream decisions. We present the **Graph Kernel**, a deterministic context slicing engine implemented as a single Rust binary (~15 KLOC) that combines a lightweight knowledge graph triple store with cryptographically-signed, policy-governed context window construction. Unlike general-purpose gr

Deterministic Provenance Engines for Autonomous Agent Systems: Architecture, Implementation, and Evaluation of the Graph Kernel

Autonomous AI agents making consequential decisions require infrastructure that ensures every reasoning step is traceable, reproducible, and verifiable. We present the \textbf{Graph Kernel}, a deterministic provenance engine implemented as a single Rust binary (${\sim}15$~KLOC) that produces cryptographically-signed, policy-governed context windows---termed \emph{admissible evidence bundles}---for autonomous agent reasoning. Unlike general-purpose graph databases, vector stores, or RAG pipelines, the Graph Kernel i

Policy-Governed Context Slicing for Autonomous Agent Systems: A Lightweight Knowledge Graph Approach

Autonomous AI agent systems face a fundamental challenge: constructing reproducible, trustworthy context windows from large conversational histories while enforcing governance policies over what information may influence downstream decisions. We present the \textbf{Graph Kernel}, a deterministic context slicing engine implemented as a single Rust binary (${\sim}15$ KLOC) that combines a lightweight knowledge graph triple store with cryptographically-signed, policy-governed context window construction. Unlike genera

Graph-Augmented Recursive Language Models for Personal Knowledge Systems

% ============================================================ We present \textbf{Cog-RLM}, a graph-augmented recursive language model architecture for personal knowledge systems that achieves 90.3\% accuracy on a comprehensive 103-question evaluation spanning ten cognitive dimensions, using a stock 3-billion parameter model with zero fine-tuning and zero inference cost. Our system extends the Recursive Language Model (RLM) paradigm~\citep{zhang2025rlm} with three novel contributions: (1)~a local knowledge graph pr

Anticipation Geometry: Domain-General Trajectory Characterization with Knowledge Graph-Grounded Rewards

We present Anticipation Geometry, a mathematical framework that characterizes trajectories through arbitrary state spaces using seven geometric scalars: commitment, uncertainty, transition pressure, recovery margin, phase stiffness, novelty, and stability. Originally developed for physical motion capture in the Comp-Core system, we prove these scalars are domain-general, operating on any sequence of vectors in a metric space equipped with a differentiable time parameter. We combine this framework with knowledge gra

Computational Choreography: Deterministic Motion-to-Audio Synthesis via Geometric Anticipation Signals

We present Computational Choreography, a deterministic pipeline that transforms heterogeneous sensor input -- phone accelerometer, smartwatch heart rate, full-body IMU skeleton -- into real-time audio synthesis through geometric anticipation signals. The system guarantees deterministic replay: identical sensor input always produces identical audio output. The key innovation is the Anticipation Kernel, which computes seven geometric scalars (commitment, uncertainty, transition pressure, recovery margin, phase stiffn

Live Knowledge Graphs: Runtime Graph Integration for Continuous Domain Adaptation in Language Agents

Recent work on Domain-Specific Superintelligence (Belova et al., 2026) demonstrates that knowledge graph-derived training curricula produce domain specialists that outperform models 400x their size. However, this approach treats knowledge graphs as static training scaffolding: constructed once, used for fine-tuning, then discarded at inference. We present an alternative: runtime knowledge graph integration, where the graph is queried live during inference with provenance-tracked context slicing, real-time entity re

KARL: Advantage-Weighted Training from Full Agent Session Traces

Standard supervised fine-tuning (SFT) for language model agents operates on input-output pairs: a prompt and the response the model should produce. This format captures *what* an agent said but discards *why* it made specific decisions. We present KARL (Knowledge-Augmented Reinforcement Learning), a trajectory intelligence system that trains language model agents from full session traces rather than isolated completions. A trajectory in KARL records every tool call, file read, code edit, bash command, success signa

Inscription-Conditioned Cognitive Twin: N'Ko Sigil Encoding as Semantic Compression for Long-Context Personality Models

Context window limitations constrain the fidelity of small personality models. A 4B parameter model with a 32K token context can hold roughly 8,000 words of conversation history before truncation begins discarding information critical to persona coherence. We present the Inscription-Conditioned Cognitive Twin (ICCT), an architecture that addresses this bottleneck by encoding conversation history as N'Ko inscriptions rather than English prose. The encoding uses 10 N'Ko sigils, each a single Unicode character derived

KARL-Edge: Multi-Signal Reinforcement Learning for Software Engineering Agents on Commodity Hardware

We present KARL-Edge, an adaptation of the Knowledge Agents via Reinforcement Learning (KARL) framework to multi-tool software engineering agents running on commodity Apple Silicon hardware. Where the original KARL system (Chang et al., 2026) trains enterprise search agents using full off-policy RL with binary reward signals, our system introduces three architectural contributions: (1) a 5-signal composite reward function that decomposes trajectory quality into outcome, process, efficiency, verification, and consis

Trajectory Memory Ledger

We present the Trajectory Memory Ledger, implemented in KARL, a schema-normalized experience replay system for improving AI coding agent performance through closed-loop feedback. The ledger records complete tool-use sequences during real coding sessions, normalizes them into an append-only schema, scores them using a six-signal composite reward function (outcome, process, efficiency, verification, consistency, and wasted motion), and uses the highest-scoring trajectories to generate advantage-weighted supervised fi

Live Knowledge Graphs: Runtime Graph Integration for Continuous Domain Adaptation in Language Agents

Recent work on Domain-Specific Superintelligence (Belova et al., 2026) demonstrates that knowledge graph-derived training curricula produce domain specialists that outperform models 400x their size. However, this approach treats knowledge graphs as static training scaffolding: constructed once, used for fine-tuning, then discarded at inference. We present an alternative: runtime knowledge graph integration, where the graph is queried live during inference with provenance-tracked context slicing, real-time entity re

Reading Tone from the Signal: Featural Acoustic Coding for Tone Resolution in N'Ko Speech Recognition

% Reading Tone from the Signal: % Featural Acoustic Coding for Tone Resolution in N'Ko Speech Recognition % Compiles with pdflatex (MacTeX). N'Ko shown via Unicode codepoints + % transliteration; IPA via tipa; architecture figure via TikZ.

The Script That Machines Can't Read: Adapting Large Language Models for N'Ko

We present a systematic study of how large language models process N'Ko (\texttt{U+07C0--U+07FF}), an alphabetic script used by over 40 million Manding-language speakers in West Africa. Through activation profiling (``brain scanning'') of Qwen3-8B before and after fine-tuning, we demonstrate that: (1) fine-tuning concentrates N'Ko adaptation in the top 8 transformer layers, reducing activation magnitudes in reasoning layers while amplifying output confidence; (2) a three-stage training pipeline---continued pre-trai

From Dead Circuits to Living Speech: Activation Profiling and Script-Native ASR for N'Ko

N'Ko is an alphabetic script serving over 40 million Manding-language speakers across West Africa, engineered by Solomana Kant\'{e} in 1949 with a strict 1:1 phoneme-to-character mapping, explicit tonal diacritics, and zero spelling exceptions. We present a dual-thread investigation into why large language models (LLMs) fail on N'Ko and how to build audio-to-N'Ko speech recognition that bypasses LLMs entirely. \textbf{Thread 1 (Diagnostic):} We perform activation profiling---a ``brain scan''---of Qwen2-72B-Instruct

The Script That Machines Can't Read: Adapting Large Language Models for N'Ko

We present a systematic study of how large language models process N'Ko (\texttt{U+07C0--U+07FF}), an alphabetic script used by over 40 million Manding-language speakers in West Africa. Through activation profiling (``brain scanning'') of Qwen3-8B before and after fine-tuning, we demonstrate that: (1) fine-tuning concentrates N'Ko adaptation in the top 8 transformer layers, reducing activation magnitudes in reasoning layers while amplifying output confidence; (2) a three-stage training pipeline---continued pre-trai

From Dead Circuits to Living Speech: Activation Profiling, Script-Native Architecture Search, and Finite-State Phonotactics for N'Ko Automatic Speech Recognition

\nko{} is an alphabetic script serving over forty million Manding-language speakers across West Africa, engineered by Solomana Kant\'e in 1949 with a strict one-to-one phoneme-to-character mapping, explicit tonal diacritics, and zero spelling exceptions. We present a dual-thread investigation into why large language models fail on \nko{} and how to construct audio-to-\nko{} speech recognition that bypasses such models entirely. In the diagnostic thread, we perform activation profiling of Qwen2-72B-Instruct (4-bit N

Theorems, Proofs, and Derivations for N'Ko Script-Native ASR

This document collects the formal mathematical results underlying the N'Ko Brain Scanner and ASR system. We present five main theorems with proofs, three derivations of key quantities, and two corollaries that connect the LLM diagnostic thread to the ASR construction thread. The results establish: (1) a phonetic transparency advantage for CTC decoding on bijective scripts, (2) bounds on the translation tax in under-represented scripts, (3) completeness and soundness of the FSM phonotactic validator, (4) a circuit d

N'Ko as an Extensible Phonemic Substrate for Governed Low-Resource Speech

Low-resource speech systems usually fail twice: first because there is not enough audio/text data, and second because the available evaluation scripts do not preserve the phonemic structure of the language being measured. This paper argues that N'Ko offers a different path. Because N'Ko is a phonetic, right-to-left script designed for Manding languages and equipped with tone, nasalization, and documented foreign-sound diacritics, it can function as an extensible phonemic substrate: a deterministic sound-code for co

N'Ko as Computational Infrastructure: Script-Native Speech Recognition, a Phonemically Interpretable Error Metric, and Admissible Tone Correction

\usepackage[margin=1in]{geometry} \usepackage{booktabs} \usepackage{array} \usepackage{amsmath} \usepackage{amssymb} \usepackage{graphicx} \usepackage{hyperref} \usepackage[numbers]{natbib} \usepackage{xcolor} \usepackage{longtable} \usepackage{caption} \usepackage{seqsplit}

Dead Circuits: Activation Profiling and Script Invisibility in Large Language Models

Large language models achieve remarkable performance on languages written in Latin, Cyrillic, CJK, and Arabic scripts. We ask what happens when these models encounter a script that is absent from their pre-training data. We perform activation profiling---a per-layer ``brain scan''---of Qwen3-8B processing 100 parallel English/N'Ko sentence pairs. N'Ko is an alphabetic script serving over 40 million Manding-language speakers across West Africa, engineered in 1949 with a strict phoneme-to-grapheme bijection, explicit

Living Speech: Script-Native Automatic Speech Recognition for N'Ko

\documentclass[11pt]{article} \usepackage{acl} \usepackage{times} \usepackage{latexsym} \usepackage{graphicx} \usepackage{booktabs} \usepackage{amsmath} \usepackage{amssymb} \usepackage{hyperref} \usepackage{multirow} \usepackage{xcolor} \usepackage{enumitem} \usepackage{tipa}

Script Invisibility Is Structural: Activation Profiling Across Three LLM Families

A prior study demonstrated that Qwen3-8B processes N'Ko text with severely diminished neural activation compared to English, a phenomenon termed \emph{script invisibility}. That finding left an open question: is the deficit specific to one model, or is it a structural property of all models trained on corpora where N'Ko is absent? We answer this by performing identical activation profiling---per-layer extraction of L2 norm, Shannon entropy, sparsity, and kurtosis---on three architecturally distinct models: Qwen3-8B

Does Script Design Matter? Phonetic Transparency and CTC Decoding for N'Ko Automatic Speech Recognition

% Does Script Design Matter? Phonetic Transparency and CTC Decoding for N'Ko ASR % Target: Interspeech 2026 / ICASSP 2027

Beyond Controlled Comparison: Deployment Properties of Script-Aware ASR for N'Ko

Controlled experiments show that phonetically transparent scripts yield lower CER for CTC-based ASR. But ASR systems are not evaluated in controlled conditions---they encounter unseen vocabulary, new speakers, and domain shift. This paper assembles deployment-relevant evidence for Bambara ASR systems using N'Ko (bijective script) and Latin (many-to-many script), anchored by the verified 20.57\% N'Ko trajectory checkpoint but drawing on both current and historical experiments. First, \textbf{compositional generaliza

Dead Circuits: Script Invisibility and Representation Failure for N'Ko in Large Language Models

This paper studies \emph{script invisibility}: the condition in which a large language model accepts a writing system as valid Unicode while allocating little functional internal representation to it. The test case is \nko{}, the script designed by Solomana Kante for Manding languages. \nko{} is not a noisy informal encoding of Bambara, Maninka, or Dioula. It is a dedicated alphabetic system in the Unicode block U+07C0--U+07FF, with a close mapping between Manding phonology and written symbols, explicit diacritic m

Against WER: Phonemic Evaluation, Orthographic Transparency, and the Script Advantage for Manding ASR

Automatic speech recognition for Manding languages is usually reported through Latin-script word error rate. This paper argues that the metric is scientifically weak for the research question at hand. If the goal is to evaluate whether an ASR system recognizes Bambara, Maninka, Dioula, or related Manding speech, then the scoring units should preserve the acoustic-phonemic distinctions carried by the language. Latin Bambara orthography is useful and socially real, but it is not a lossless measurement interface: it u

Script-Native ASR for N'Ko: Anticipatory Transformer CTC Decoding and the CER Anchor

This paper preserves the technical ASR center of the \nko{} research program: an archived script-native trajectory checkpoint reporting \anchorcer{} character error rate on a \corpusn{}-pair Bambara corpus snapshot. The model uses frozen Whisper large-v3 acoustic features, a trainable Transformer CTC decoder, and a compact trajectory state that biases attention with speech-dynamic information. The result is the strongest retained ASR artifact in the project and is the correct way to discuss the phrase ``20 CER'' pu

Anticipation Geometry Partition: Row-Level Governance for Script-Native N'Ko ASR Deployment

This paper defines the deployment layer of the \nko{} ASR project: Anticipation Geometry Partition (AGP). AGP is not the acoustic model that produced the archived 20.57\% CER anchor. It begins after ASR. Its role is to convert trajectory and uncertainty signals into row-level decisions about correction, provenance, corpus admission, and deployment eligibility. The motivation is simple: a scalar CER number is not enough to build a trustworthy transcript corpus or a production speech system. A model can make local mi

Recursive Polymodal Synthesis: A Framework for Real-Time Computational Choreography Through Multi-Modal Sensor Fusion

We present Recursive Polymodal Synthesis (RPS), a framework for real-time computational choreography that achieves robust multi-modal sensor fusion through iterative proximal updates with spectral norm constraints, and couples that embodied state to a phrase-conditioned spectrogram diffusion backend for audio generation. The system integrates kinematic, physiological, and rhythmic data streams into a unified embodied representation that drives either smooth control signals or direct audio synthesis in real time. Ou

RESEARCH PAPER TECHNICAL LATEX

We present a mathematically rigorous framework for multi-modal sensor fusion in real-time embodied interaction systems. Our approach, \emph{Recursive Polymodal Synthesis} (RPS), fuses heterogeneous modalities with disparate sampling rates and noise statistics into a coherent latent representation suitable for generative control. The core mechanism is a proximal fixed-point iteration using spectral-norm-constrained relational operators, yielding contraction guarantees and a unique fixed point. We prove geometric con

Recursive Polymodal Synthesis for Real-Time Embodied Interaction: A Contraction-Based Framework with Provable Convergence

We present a mathematically rigorous framework for multi-modal sensor fusion in real-time embodied interaction systems, coupled to a phrase-conditioned spectrogram diffusion backend for direct audio generation. Our approach, termed Recursive Polymodal Synthesis (RPS), addresses the fundamental challenge of fusing heterogeneous sensor modalities with different noise characteristics, sampling rates, and semantic meanings into a coherent internal representation suitable for generative control. The key innovation is a

Semantic Kernel for N'Ko Language Processing: A Schema-Locked Approach to Low-Resource Vocabulary Construction

We present a schema-locked, replayable semantic kernel for constructing and validating vocabulary in low-resource languages, with specific application to N'Ko, the indigenous script of the Manding language family. Our system introduces a 7-operator semantic algebra with formal legality grammar, a morphological compiler producing content-addressable forms with stable signatures, and an evidence-driven lifecycle model for vocabulary promotion. The evaluation methodology employs stress-profile-based adversarial testin

From Dead Circuits to Living Speech: Activation Profiling and Script-Native ASR for N'Ko

N'Ko is an alphabetic script serving over 40 million Manding-language speakers across West Africa, engineered by Solomana Kanté in 1949 with a strict 1:1 phoneme-to-character mapping, explicit tonal diacritics, and zero spelling exceptions. We present a dual-thread investigation into why large language models (LLMs) fail on N'Ko and how to build audio-to-N'Ko speech recognition that bypasses LLMs entirely. **Thread 1 (Diagnostic):** We perform activation profiling — a "brain scan" — of Qwen2-72B-Instruct (4-bit NF4

Recursive Polymodal Synthesis for Real-Time Embodied Interaction: A Contraction-Based Framework with Provable Convergence

We present a mathematically rigorous framework for multi-modal sensor fusion in real-time embodied interaction systems. Our approach, termed Recursive Polymodal Synthesis (RPS), addresses the fundamental challenge of fusing heterogeneous sensor modalities with different noise characteristics, sampling rates, and semantic meanings into a coherent internal representation suitable for generative control. The key innovation is a proximal fixed-point iteration scheme that enforces cross-modal coherence through spectral-

Recursive Polymodal Synthesis: A Framework for Real-Time Computational Choreography Through Multi-Modal Sensor Fusion

We present Recursive Polymodal Synthesis (RPS), a framework for real-time computational choreography that achieves robust multi-modal sensor fusion through iterative proximal updates with spectral norm constraints, and couples that embodied state to a phrase-conditioned spectrogram diffusion backend for audio generation. The system integrates kinematic, physiological, and rhythmic data streams into a unified embodied representation that drives either smooth control signals or direct audio synthesis in real time. Ou

Topological Preference Optimization (TPO): A Novel Training Strategy for Conversational AI

We introduce **Topological Preference Optimization (TPO)**, a novel training methodology that leverages conversation topology and spatial-temporal coordinates to generate preference datasets for language model training. Unlike traditional Direct Preference Optimization (DPO) which relies on human annotations or simple heuristics, TPO extracts preference signals directly from the structural properties of conversation graphs, incorporating hindsight knowledge and topological awareness to create more accurate and cont

Topological Preference Optimization (TPO): A Novel Training Strategy for Conversational AI

We introduce **Topological Preference Optimization (TPO)**, a novel training methodology that leverages conversation topology and spatial-temporal coordinates to generate preference datasets for language model training. Unlike traditional Direct Preference Optimization (DPO) which relies on human annotations or simple heuristics, TPO extracts preference signals directly from the structural properties of conversation graphs, incorporating hindsight knowledge and topological awareness to create more accurate and cont

Memory-Augmented Equilibrium Control (MAEC)

This document formalizes **Memory-Augmented Equilibrium Control (MAEC)**, a control-theoretic framework for real-time embodied creative systems. MAEC addresses a class of problems where traditional control theory and reinforcement learning fail: continuous, non-episodic systems that must maintain expressive viability while generating novel outputs. Unlike RL, MAEC has no scalar reward function, no policy optimization loop, and no episodic resets. Instead, it preserves dynamic equilibrium through memory-conditioned

Retrieval-Centric ASR for N'Ko: Exploiting Script Structure to Beat Sequence-to-Sequence

We present a retrieval-centric automatic speech recognition (ASR) architecture for Bambara, targeting N'Ko script output directly rather than routing through Latin transcription. The central insight is structural: N'Ko enforces a strict 1:1 phoneme-to-grapheme mapping, explicit tonal diacritics, and a mathematically complete syllable inventory of 3,024 entries (all V, VN, CV, and CVN patterns across five tones). This finite, well-structured output space makes retrieval a better fit than sequence-to-sequence decodin

RAG++: State-Based Retrieval for Life Trajectory Optimization

We present RAG++ (Retrieval-Augmented Generation Plus Plus), a novel retrieval paradigm that extends traditional RAG from semantic text retrieval to **state-space transition retrieval**. Instead of retrieving "relevant documents," RAG++ retrieves **successful state transitions** from a user's personal history and recommends actions based on what worked in similar dynamical regimes. We demonstrate this approach in TrajectoryOS, a life physics modeling system that treats human life as a dynamical system with measurab

Graph-Augmented Recursive Language Models for Personal Knowledge Systems

We present Cog-RLM, a graph-augmented recursive language model architecture for personal knowledge systems that achieves 90.3% accuracy on a comprehensive 103-question multi-dimensional evaluation using a stock 3-billion parameter model with zero fine-tuning and zero inference cost. Our system extends the Recursive Language Model (RLM) paradigm (Zhang et al., 2025) with three novel contributions: (1) a local knowledge graph providing relationship-aware context retrieval, (2) a hybrid decomposition classifier that s

Geometric Motifs for Selecting and Routing Coding-Agent Training Data

We present a method for compactly annotating coding agent sessions with behavioral motifs and geometric features, then conditioning training data generation on these annotations. From 834 real multi-project coding sessions spanning 4,633 turn-level records across 50+ applications, we extract 10-category symbolic labels (inscriptions) and 5 continuous geometric scalars. We show that: (1) transition pressure predicts session convergence at 71.8% accuracy (z = 2.72, p < 0.007), (2) advantage-weighted training using th

Enhanced Topological Preference Optimization: A Unified Framework for Multi-Dimensional Conversation Analysis with Spatial Intelligence and Cross-Conversation Consolidation

We present a comprehensive enhancement to Topological Preference Optimization (TPO) that integrates spatial intelligence, cross-conversation consolidation, and advanced pattern recognition for conversation analysis. Our unified framework processes hierarchical conversation structures through a four-dimensional spatial coordinate system, implements adaptive clustering algorithms for pattern detection, and employs sophisticated natural language processing techniques for knowledge consolidation across conversation bou

Enhanced Topological Preference Optimization: A Unified Framework for Multi-Dimensional Conversation Analysis with Spatial Intelligence and Cross-Conversation Consolidation

We present a comprehensive enhancement to Topological Preference Optimization (TPO) that integrates spatial intelligence, cross-conversation consolidation, and advanced pattern recognition for conversation analysis. Our unified framework processes hierarchical conversation structures through a four-dimensional spatial coordinate system, implements adaptive clustering algorithms for pattern detection, and employs sophisticated natural language processing techniques for knowledge consolidation across conversation bou

Mixture of Anticipatory Orthogonal Experts for N'Ko ASR

MAOE-N'Ko, the Mixture of Anticipatory Orthogonal Experts for N'Ko ASR, is a modular speech-language correction architecture that keeps the acoustic model sovereign while allowing language-prior intelligence to act only where it is admissible. The system begins with a verified N'Ko trajectory CTC acoustic model, currently anchored by the Paper 4 reproduction checkpoint with 20.57 percent CER on the locked N'Ko run. Instead of replacing that model with a monolithic audio-language system, MAOE-N'Ko routes each ASR ch

Organic Vocabulary Acquisition for Low-Resource African Languages: A Video-First Approach to N'Ko and Manding Language Processing

This document presents a novel approach to building state-of-the-art natural language processing systems for N'Ko, Bambara, and related Manding languages spoken by approximately forty million people across West Africa. Unlike traditional corpus-driven methodologies that depend on pre-existing parallel texts such as Bible translations or government documents, we introduce a video-first organic vocabulary discovery system that extracts training data directly from educational YouTube content. The system processes vide

The First Reference-Backed Proof: How Narrow Repairs Validate the AGP Bridge Architecture

On 2026-04-21, the AGP bridge architecture achieved its first non-synthetic, reference-backed Character Error Rate (CER) improvement: a reduction from 0.7604 to 0.7512 on a curated slice of archived ASR evaluation data. This result, while numerically modest, constitutes a critical architectural validation. It demonstrates that a reference-leakage-free gating system—operating exclusively on hypothesis-side telemetry—can safely admit edits that improve supervised metrics. The improvement was not achieved through broa

arXiv Submission: Graph-Augmented Recursive Language Models for Personal Knowledge Systems

We present Cog-RLM, a graph-augmented recursive language model architecture for personal knowledge systems that achieves 90.3% accuracy on a comprehensive 103-question evaluation spanning ten cognitive dimensions, using a stock 3-billion parameter model with zero fine-tuning and zero inference cost. Our system extends the Recursive Language Model (RLM) paradigm with three novel contributions: (1) a local knowledge graph providing relationship-aware context retrieval via breadth-first traversal, (2) a hybrid decompo

research — docs

This paper presents a retrieval-centric architecture for voice-controlled DJ performance that adapts the Speech-to-Order (S2O) streaming pipeline to the domain of professional DJ software, specifically Rekordbox. Instead of parsing transcribed text into intents via a conventional automatic speech recognition (ASR) and natural language understanding stack, the system learns a direct mapping between spoken commands and a catalog of DJ actions derived from Rekordbox’s performance preset mappings. The design combines a

The Measure-Theoretic Foundation of Inverse Ring Contextual Propagation

We present Inverse Ring Contextual Propagation (I-RCP), a novel mathematical framework for modeling individual conversation dynamics through inverse mapping of response patterns. Unlike traditional approaches that optimize AI responses to match human preferences, I-RCP inverts the learning objective from P(v|u) to P(u|v), creating a direct model of individual response patterns within a rigorous mathematical structure. The framework introduces a three-dimensional coordinate system (x,y,z) that uniquely captures the

The Measure-Theoretic Foundation of Inverse Ring Contextual Propagation

We present Inverse Ring Contextual Propagation (I-RCP), a novel mathematical framework for modeling individual conversation dynamics through inverse mapping of response patterns. Unlike traditional approaches that optimize AI responses to match human preferences, I-RCP inverts the learning objective from P(v|u) to P(u|v), creating a direct model of individual response patterns within a rigorous mathematical structure. The framework introduces a three-dimensional coordinate system (x,y,z) that uniquely captures the

RAG++ Specification

RAG++ is a high-performance retrieval engine that provides **statistical priors** from outcome-annotated trajectories. Unlike traditional RAG systems that retrieve text for language model context, RAG++ retrieves structured execution traces and computes distributional statistics for downstream policy conditioning. **Key Insight**: Past execution outcomes encode implicit knowledge about action feasibility, timing, and context-dependent success rates. RAG++ surfaces this knowledge as queryable priors.

cognitive twin research paper

% Options for packages loaded elsewhere \PassOptionsToPackage{unicode}{hyperref} \PassOptionsToPackage{hyphens}{url} \documentclass[ 11pt, ]{article} \usepackage{xcolor} \usepackage[margin=1in]{geometry} \usepackage{amsmath,amssymb} \setcounter{secnumdepth}{-\maxdimen} % remove section numbering \usepackage{iftex} \ifPDFTeX \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} \usepackage{textcomp} % provide euro and other symbols \else % if luatex or xetex \usepackage{unicode-math} % this also loads fontspec \defau

Recursive Language Model Integration: Technical Specification

This document specifies the architecture and implementation of the Recursive Language Model integration within the cc-orchestrator-agent module. The system provides an inference strategy enabling language models to process unbounded-length input contexts through recursive decomposition, treating context as a programmable variable rather than a monolithic prompt payload. This specification covers the theoretical foundation, architectural design, execution semantics, and integration with Graph Kernel memory systems a

Computational Choreography: A Dense Manuscript for the LUME Stack

Computational choreography is the name for the layer of LUME that interprets a performer's body as a live compositional instrument. It is not a synonym for motion capture, gesture recognition, depth rendering, or visual reactivity, although it depends on all of them. It is the discipline of deciding what the machine believes about the body, how that belief changes over time, how movement becomes intention, and how intention becomes a bounded visual or musical event. The current LUME stack already contains the physi

BWB Kiosk — Voice Ordering Architecture

> *"Break every component down to its grills... define a subsection and a sub-subsection that further builds upon the previous section, then expands it in a recursive manner."*

Obsidian Vault Integration — Architecture & Operations Guide

| Store | Type | Weakness | |-------|------|----------| | `memory/*.md` files | Flat Markdown | No linking, manual curation, linear | | Kimi SQLite DB (`kimi_memory.db`) | Structured tables | Queryable but invisible, no graph | | Supabase | Cloud relational | API-only access, no browsing | | Orbit | Semantic memory | Black-box embeddings, no human navigation | | Discord threads | Chat messages | Ephemeral, unsearchable after scroll | | Plan files (`.claude/plans/`) | Task-scoped Markdown | Die when plans complete |

Skill Entity Architecture (SEA) — DEP + Evo-Cubed Analysis

> **Deprecation note (2026-05-13):** Mac3 was the Tier 2 worker host at the time this design doc was authored. Mac3 has since been retired. Forward-looking references to Mac3 (worker pool, async queue, circuit breaker) should be read as **Mac4:8100** (cognitive twin host) in any current/future implementation. The Mac3-era hardware-assignment sections (§2, Step 6, stress-test §🔴 Mac3 Async Worker Reliability) are kept for historical accuracy but are **obsolete for v1.1 onward**. See SOOP-2 launch memory for migrati

The N'Ko Compute Network

Every existing compute network, from Bitcoin to Akash to Render, treats workers as interchangeable machines. The worker's identity, language, and culture are irrelevant to the protocol. This paper proposes a fundamentally different architecture: a compute network where the worker's linguistic and cultural competence IS the valuable computation, and the protocol pays for it in STX on Bitcoin's Layer 2. The N'Ko Compute Network combines three production systems into a single protocol: (1) the EPOCH Protocol, eight Cl

Inverse Ring Contextual Propagation: A Mathematical Framework for Learning Individual Response Patterns in Conversational Dynamics

We present Inverse Ring Contextual Propagation (IRCP), a novel mathematical framework for modeling individual conversation dynamics through inverse mapping of response patterns. Unlike traditional approaches that optimize AI responses to match human preferences, IRCP inverts the learning objective from P(v|u) to P(u|v), creating a direct model of individual response patterns within a rigorous mathematical structure. The framework introduces a four-dimensional coordinate system (x,y,z,t) that uniquely captures the d

Architecture Document 23: Anticipatory Transformer Architecture

**Status**: Research Proposal (Revised) **Created**: 2026-01-04 **Revised**: 2026-01-04 (Incorporated engineering feedback) **Dependencies**: DELL Theory (19), Graph Kernel (15), Computational Choreography (01), TrajectoryOS (02)

Computational Choreography - Complete System Architecture

The Computational Choreography (CC) system transforms raw motion sensor data into musically-coherent audio control signals. The architecture follows a strict **bottom-up dependency graph** where lower layers know nothing about higher layers.

Cross-Pollination Architecture Specification

1. [Overview](#1-overview) 2. [Prediction Engine](#2-prediction-engine) 3. [Safety Rails](#3-safety-rails) 4. [ACC Integration — Swipeable Prediction Cards](#4-acc-integration--swipeable-prediction-cards) 5. [Autonomous Mode](#5-autonomous-mode) 6. [Push Notifications](#6-push-notifications) 7. [Feedback Loop & DPO Training](#7-feedback-loop--dpo-training) 8. [API Reference](#8-api-reference) 9. [Data Models](#9-data-models) 10. [Deployment & Configuration](#10-deployment--configuration)

ARCHITECTURE

3 > **Document Purpose**: Comprehensive operational map of all entities, projects, capabilities, and systems under host management. > > **Last Updated**: 2026-01-18 > > **Document Type**: Living reference — update as entities evolve

nko asr paper v2

% Options for packages loaded elsewhere \PassOptionsToPackage{unicode}{hyperref} \PassOptionsToPackage{hyphens}{url} \documentclass[ ]{article} \usepackage{xcolor} \usepackage{amsmath,amssymb} \setcounter{secnumdepth}{-\maxdimen} % remove section numbering \usepackage{iftex} \ifPDFTeX \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} \usepackage{textcomp} % provide euro and other symbols \else % if luatex or xetex \usepackage{unicode-math} % this also loads fontspec \defaultfontfeatures{Scale=MatchLowercase} \de

Voice Ordering Pipeline Architecture

> **Purpose**: Comprehensive technical documentation for the voice ordering system refactoring. > **Last Updated**: December 26, 2025 > **Status**: ✅ Complete (10/10 Steps Done)

DLM Integration Pipeline: IRCP, RCP, and TPO

This document defines the complete pipeline for integrating **IRCP** (Inverse Ring Contextual Propagation), **RCP** (Ring Contextual Propagation), and **TPO** (Topological Preference Optimization) directly within the **DLM** (Divergent Language Matrix) framework.

Graph Kernel Technical Architecture

**OpenClaw CompCore — cc-graph-kernel** **Version:** 1.0.0 · **Schema:** 1.0.0 **Date:** 2026-02-13 **Author:** Mohamed Diomande

NKo Brain Scanner — Unified Architecture

1. **No thin wrappers.** `nko_core/__init__.py` handles all imports from `Desktop/NKo/` via `sys.path`. No separate `phonetics.py`, `transliterate.py`, `morphology.py` wrapper files. If `from nko_core import phonetics` works, no wrapper is needed. 2. **No premature release.** HuggingFace upload happens AFTER mode collapse is fixed and the model generates coherent N'Ko text. Not before. 3. **Architecture matches disk.** Every file listed below exists. Every number is current. If reality changes, this doc gets update

The Architecture of Gemini Live Voice Control for Rekordbox: A Technical Essay

The Gemini Live voice control system for Rekordbox represents a sophisticated orchestration of modern machine learning services, real-time audio processing, and command dispatch mechanisms. At its highest level, this system transforms the ephemeral quality of human speech into precise digital instructions that control professional DJ software. The architecture embodies a philosophy of delegation, where each component performs a specialized role in service of a singular purpose: to translate the DJ's vocal intent in

Unified Agent OS — Architecture Document

The Unified Agent OS (UAOS) merges three autonomous systems — **Pulse** (development), **Heartbeat** (monitoring), and **Dream Weaver / Noosphere** (incubation) — into a single coherent platform. Today these systems share the filesystem implicitly and bridge state through ad-hoc scripts (`noosphere_bridge.py`, `cadence_bridge.py`). The UAOS replaces those stitches with a unified state bus, a single lifecycle model, and formalized handoff protocols.

CognitiveHire: Creative Architecture

CognitiveHire sits at the collision point of five distinct forces. None of them are new. The combination is unprecedented.

nko monograph

% --- Hyperlinks --- \usepackage[colorlinks=true,linkcolor=blue!60!black,citecolor=blue!60!black,urlcolor=blue!60!black]{hyperref}

Overview

Perfect — here’s a rewritten abstract and overview with the modular breakdown and explicit mention of bidirectional translations across English, French, N’ko, and Bambara.

HUB-2: Threaded Messaging Architecture

Replace Discord's channel model with a threaded architecture tailored to OpenClaw: - **Threaded, not channel-based** — every conversation is a thread with a parent category - **Quad-inspired layout** — 4 concurrent contexts visible (like the terminal quad) - **Feed integration** — 33 Prefect flows post directly to threads (no Discord webhooks) - **Agent-native** — threads can be owned by agents, not just humans - **Voice-first** — every thread supports voice input/output - **Offline-capable** — SwiftData persistenc

DJ Voice Control: Retrieval-Centric Architecture

The DJ Voice Control system adapts the speech-to-order retrieval-centric paradigm for real-time DJ performance control. Instead of matching spoken orders to menu items, we match spoken commands to DJ actions and keyboard shortcuts. This approach provides superior accuracy compared to traditional ASR + NLU pipelines by learning a direct semantic mapping between audio utterances and command intents.

Data Models

```prisma model User { id String @id @default(uuid()) email String @unique name String? createdAt DateTime @default(now()) updatedAt DateTime @updatedAt

Motion Accountability Platform — Architecture

``` Tier 3: Commitment Protocol (Swift + Supabase) ├── Declare intentions, verify against observed motion ├── Social feed: commitments met or visibly not └── Notification loop: 30-min push-up reminder → detection → confirmation

Unified RAG++ Architecture

1. [System Overview](#1-system-overview) 2. [Layer Architecture](#2-layer-architecture) 3. [Foundation Layer: Rust Core](#3-foundation-layer-rust-core) 4. [Data Layer: Supabase Schema](#4-data-layer-supabase-schema) 5. [Ingestion Layer: Prompt Pipeline](#5-ingestion-layer-prompt-pipeline) 6. [ML Layer: CognitiveTwin](#6-ml-layer-cognitivetwin) 7. [Orchestration Layer: Orbit](#7-orchestration-layer-orbit) 8. [Integration Layer: Prompt Logger](#8-integration-layer-prompt-logger) 9. [API Layer: Endpoints Reference](#9

Graph Kernel

**Version**: 1.1.0 **Last Updated**: 2026-01-03 **Status**: Production **Parent**: [02-TRAJECTORY_OS.md](02-TRAJECTORY_OS.md) **Related**: [08-RAG_PLUS_PLUS.md](08-RAG_PLUS_PLUS.md), [09-ORBIT.md](09-ORBIT.md), [17-AGENT_SDK.md](17-AGENT_SDK.md) **Crate (Rust)**: `core/cc-graph-kernel/` **Service**: Cloud Run `graph-kernel` **Tests**: 140+ passing **Schema Version**: 1.0.0

Cognitive Twin Architecture V2 — Decoupled RLM + Qwen 3.5 Migration

The Cognitive Twin is Mo's personal AI delegate — a model that knows his projects, preferences, reasoning patterns, and history. V1 used Llama 3.2:3B locally with a tightly-coupled RAG+Graph+RLM stack. V2 decouples every layer, swaps the base model to Qwen 3.5, and creates a clean evaluation pipeline.

DEP — Pipeline Protocol Architecture Audit

The Pipeline Protocol is a 3-table Supabase schema (`pipeline_definitions`, `pipeline_runs`, `pipeline_step_logs`) with 2 VIEWs, 1 trigger, and a shared TypeScript module consumed by 3 edge functions. It bridges to Nexus observability via a Prometheus exporter, Grafana dashboard, Prefect watcher, and a Next.js portal page.

Server Architecture Integration — Aura

Aura currently has a flat thread model: `HubThread` objects live in `hub_threads`, categorized by `ThreadCategory` and `ThreadType`, with no parent container. The Discord ecosystem, however, operates on three distinct architectural patterns — each representing an evolution in how the Clawdbot gateway handles task dispatch and message delivery. This document describes those three patterns in abstract form and specifies how they integrate into Aura as a **secluded feature** that does not interfere with the existing t

Computational Choreography - Complete System Architecture

The Computational Choreography (CC) system transforms raw motion sensor data into musically-coherent audio control signals. The architecture follows a strict **bottom-up dependency graph** where lower layers know nothing about higher layers.

Tier 3: Medium-Term Architectural Enhancements - Implementation Plan

Tier 3 introduces **5 advanced architectural features** that significantly enhance the voice control system's robustness, intelligence, and user experience.

Inscription Architecture Map

This document maps every file, specification, and implementation related to the N'Ko inscription system, sigils, tokenization, the EPOCH protocol, Stacks/Clarity contracts, PsiChain, the cognitive twin, and anticipation geometry. It traces how they interconnect to form a single pipeline that encodes a life's computational dynamics as hash-chained N'Ko inscriptions settled on Bitcoin.

DEP Integration Report — Skill Entity Architecture (SEA)

**Date:** 2026-02-18 **Evaluator:** Subagent DEP (Deep Evaluation Pass) **Scope:** Full 4-layer integration verification **Overall Verdict:** ⚠️ **PARTIALLY INTEGRATED**

RAG++ Architecture Analysis & Deployment Readiness

The RAG++ system has a **solid foundation** with no glaring architectural issues. The evaluation framework revealed data/tuning needs (not architecture flaws). You can safely proceed to frontend integration and deployment.

CRP-2.1: Section 3 Architecture Deep-Dive — COMPLETE

Expanded Section 3 (Architecture) from ~158 lines to ~340 lines of LaTeX, more than doubling its content to 3+ pages in NeurIPS format.

cc-inscription — Full Intelligence Synthesis

cc-inscription is a Rust crate at `[home]/Desktop/Comp-Core/core/semantic/cc-inscription/` that turns a stream of "embodied dynamics" (the `z`-trajectory of latent vectors coming out of the motion brain, called DELL) into discrete, typed, hash-witnessed statements written in N'Ko script. There are exactly ten claim types — Stabilize, Disperse, Transition, Return, Dwell, Oscillate, Recover, Novel, Place-Shift, Echo — each with a locked N'Ko sigil. A `ClaimDetector` watches the trajectory; when dynamics become constr

Gesture Control Architecture - Motion-Based DJ Interface

Transform your phone into a **motion-controlled DJ remote** using: - **Gemini Live Video**: Visual gesture interpretation - **Sensor Logger**: High-precision IMU data (accelerometer, gyroscope, magnetometer) - **Fusion Engine**: Combines both streams for robust recognition - **Training UI**: Practice and refine gestures for accuracy

KARL V6 — Cognitive Twin Architecture

**Version**: 6.0.0-design **Date**: 2026-04-01 **Status**: Architecture (pre-implementation) **Supersedes**: V5 `twin_session_driver.py`

LUME Full System Architecture -- K11 as Central Hub

_Generated 2026-04-27 via Evo3 (4-stage recursive creative evolution)._ _Full evolution output: Desktop/evo-cube-output/lume-full-system-architecture/_

Milkmen Delivery - Shared Agent CRM Architecture Plan

-- Many-to-many: agents can share territories CREATE TABLE territory_agents ( territory_id uuid REFERENCES territories(id), agent_id uuid REFERENCES agents(id), role text NOT NULL CHECK (role IN ('seeder', 'closer', 'both')), visit_order integer NOT NULL, -- 1 = visits first, 2 = visits second active boolean DEFAULT true, PRIMARY KEY (territory_id, agent_id) ); ```

Stage 4: FORGE -- Final Creative Architecture

FirstDate is a production-first reality dating series that treats transparency as its format, not its liability. Three asymmetric roles (Host, Applicant, Viewer) orbit a 10-week seasonal arc set in Miami, where every consent ritual, every sponsor deal, every casting decision is designed to be seen. The app is not a dating platform with a show bolted on. It is a show management system whose public membrane happens to look like a dating app. Swiping is a personality quiz, not a match engine. The episode is the produc

Computational Studio Implementation Roadmap

**Total Tasks**: 103 **Estimated Timeline**: 10-12 weeks **Critical Path**: Setup → Utils → Equilibria → Runtime → UI → Tests

BrewsWithBeats - Three-App Architecture Implementation Plan

This document outlines the comprehensive plan to restructure BrewsWithBeats from a unified app into three separate components:

Motion Training Data Pipeline Protocol

> **Version**: 1.0 > **Status**: DRAFT > **Scope**: End-to-end data architecture for motion capture, processing, and training

Complete System Data Flow Diagram

```mermaid flowchart TB subgraph Sensors["📡 Physical Sensors"] Mocopi[Mocopi Sensor<br/>50Hz UDP<br/>26 bones + quaternions] Kinect[Kinect v2<br/>30Hz USB<br/>25 joints + depth] end

Koatji City Capture Architecture

**A city is captured when 10 accounts in a single zip code are on Odeko auto-reorder and two of them have referred at least one other account.**

SOOP-2 Architecture

This document is the authoritative architecture reference for SOOP-2. It describes what exists today (SEA at ~40% shipped), what SOOP-2 adds, and exactly how the pieces connect. Every section maps to at least one acceptance criterion from the launch checkpoint.

ELP-2 — Survivor Architecture

Three of four scrutiny layers returned a convergent verdict: ELP-1 as written cannot ship. The CRITICAL findings are structural — a /inject format mismatch that breaks the primary dispatch path, a concurrent SKILL.md write race enabled by a 5-minute claim TTL, and a Syncthing-backed filesystem fallback that is architecturally described but physically unprovisioned. These are not tuning problems; they are root-cause failures.

Cross-Pollination Architecture Specification

**Cross-pollination** is PULSE's proactive intelligence layer. It predicts what you need *before you ask* by synthesizing signals from your calendar, tasks, location, time patterns, and conversation history — then delivers contextual predictions to the right device at the right moment.

Beyond -- Anticipation Geometry for NUMU FARE

Beyond is a NUMU FARE package (`numu-beyond`) that uses anticipation geometry to orchestrate three AI paradigms through a single geometric coordination signal. Instead of each paradigm implementing its own convergence detection, retry logic, and stall recovery, Beyond provides a universal orchestration loop driven by four mathematical scalars computed over the trajectory of bus events.

CALC: Cross-Agent Live Collaboration

CALC connects three AI coding agents from three different companies into a unified collaboration system. The agents share context, route tasks by model strength, and communicate through five transport layers.

Service Architecture

**Status Legend**: ✅ **Live** (production-ready) | 🚧 **Beta** (functional, incomplete) | 🔮 **Planned** (designed but not built)

CC AI Pipeline - Complete Implementation

- **335 conversations** from 5 data sources - **9,572 messages** (user + assistant) - **2,158 notes** from personal records - **Auto-categorized** by topic: - music_production: 76 conversations - machine_learning: 47 conversations - personal: 38 conversations - business: 32 conversations - computational_choreography: 23 conversations

✅ COMPLETE IRCP + TPO INTEGRATION: NO SIMPLIFIED SOLUTIONS

You were absolutely right - the initial implementation was only 513 lines and contained simplified placeholder solutions. I have now created a **complete, mathematically rigorous implementation** with **1,373 lines of full code** and **zero simplified solutions**.

DLM Codebase Audit - Week 1

**Date:** 2025-12-07 **Auditor:** Claude (Sonnet 4.5) **Scope:** Complete audit of DLM, IRCP, and TPO packages for production-grade rebuild

IRCP-DLM Fusion Strategy - Complete Analysis

✅ **dlm/models/** - Pydantic models with `ChainCoordinate` (x, y, z, t, n_parts) ✅ **dlm/engine/** - Processing engines including `ircp_embedder.py` (exists!) ✅ **dlm/inference/** - Conversation and prompt managers ✅ **dlm/response/** - Recently refactored with production-grade utilities

TPO Implementation Summary

I have successfully created a comprehensive implementation of **Topological Preference Optimization (TPO)** - the novel training strategy we developed based on your groundbreaking insight about conversation topology.

TPO Mathematical Supplement: Detailed Formulations and Proofs

**Definition 1.1** (Conversation Graph): A conversation graph $G = (V, E, \mathbf{C}, \mathbf{M})$ where: - $V = \{v_1, v_2, ..., v_n\}$ is the set of message nodes - $E \subseteq V \times V$ is the set of directed edges representing reply relationships - $\mathbf{C}: V \rightarrow \mathbb{R}^5$ maps each node to its DLM coordinates - $\mathbf{M}: V \rightarrow \Sigma^*$ maps each node to its message content

✅ Enhanced TPO System - Complete Implementation

We have successfully **audited and enhanced the entire consolidated TPO system**, ensuring that every component has advanced, production-ready implementations with no placeholders, simplified functions, or stub code.

🎉 IRCP + TPO Integration: Complete Architecture & Implementation Plan

The integration of **Inverse Ring Contextual Propagation (IRCP)** on top of **Topological Preference Optimization (TPO)** creates a revolutionary two-layer architecture that combines:

🔗 IRCP + TPO Integration: Detailed Architecture Plan

The integration of Inverse Ring Contextual Propagation (IRCP) on top of Topological Preference Optimization (TPO) creates a powerful two-layer architecture:

🔧 Preference Generation Fix Summary

The RCP-enhanced TPO system was generating preference pairs where `chosen` and `rejected` responses were **identical**. This occurred specifically in:

✅ RCP to TPO Migration Complete

We have successfully **deconstructed RCP and consolidated all its best components directly into TPO**, creating a unified, more powerful conversation optimization system.

TPO-RCP Consolidation Summary

We have successfully **deconstructed and consolidated the best of RCP directly into TPO**, creating a unified, more powerful conversation optimization system.

🎯 **Where IRCP + TPO Fits in Model Training Stack**

``` 📚 YOUR DATA (277 conversations, 60K+ messages) ↓ 🧮 IRCP + TPO INTEGRATION ← YOU ARE HERE (advanced_tpo_ircp_bridge.py - 1,373 lines) ↓ 📊 ENHANCED DATASET (17,051 validated preference pairs) ↓ 🎯 MODEL TRAINING (DPO/RLHF/Constitutional AI) ↓ 🤖 PERSONALIZED AI MODEL ↓ 🚀 DEPLOYMENT ```

🎉 IRCP + TPO Integration: Complete Architecture & Implementation Plan

The integration of **Inverse Ring Contextual Propagation (IRCP)** on top of **Topological Preference Optimization (TPO)** creates a revolutionary two-layer architecture that combines:

🔗 IRCP + TPO Integration: Detailed Architecture Plan

The integration of Inverse Ring Contextual Propagation (IRCP) on top of Topological Preference Optimization (TPO) creates a powerful two-layer architecture:

Unified TrajectoryOS Architecture Plan

TrajectoryOS was originally designed as **"an AI that interviews you like a Stanford professor, models your strengths like a McKinsey consultant, and tracks your life-trajectory like NASA mission control."**

CognitiveTwin V3: Project Overview

The fundamental goal of CognitiveTwin V3 is to train a model that executes on directive prompts without asking for unnecessary confirmation.

LUME Music Direction — Conclusive Architecture Decision

> Research report, 2026-05-20. Author: research-engine. > Question settled: how should the LUME bar produce music that responds to body > motion AND sounds genuinely good (venue quality)? > > **Decision: stem-based interactive playback.** Body motion controls real, > professionally produced 4-stem Demucs sets — layering, crossfading, filtering, > FX, beat-synced triggering — instead of synthesizing music from scratch.

RAG++ Architecture

RAG++ is a trajectory-aware retrieval-augmented generation system that maintains a unified knowledge fabric across conversations, ideas, code, and motion data. It powers the CognitiveTwin — a personalized AI that learns user-specific reasoning patterns.

CognitiveTwin V3: Project Overview

The fundamental goal of CognitiveTwin V3 is to train a model that executes on directive prompts without asking for unnecessary confirmation.

Comp-Core Architecture Overview

**Version**: 2.4.0 **Last Updated**: 2026-01-03 **Schema Version**: 1.0.0 **Implementation Status**: Production Ready **Maintainer**: Computational Choreography Team

TrajectoryOS

**Version**: 1.2.0 **Last Updated**: 2026-01-03 **Status**: Production **Parent**: [00-OVERVIEW.md](00-OVERVIEW.md) **Complement**: [03-ECHELON.md](03-ECHELON.md) (real-time engine)

DELL (Dual-Equilibrium Latent Learning) Architecture Diagrams

```mermaid graph TB subgraph Input["Input Layer"] X[Motion State x_t<br/>6-dim:<br/>• Left hand velocity<br/>• Right hand velocity<br/>• Torso orientation] C[Context c_t<br/>1-dim:<br/>• Commitment scalar] end

LUME / DEMON Architecture Comparison

DEMON is a real-time controllable music diffusion runtime. It turns source audio, text prompts, LoRAs, references, and live control curves into generated or transformed music.

MiniMax Fleet — Multi-Instance Agent Architecture

**Designed by:** Claw (the agent who'll be using it) **Date:** Feb 16, 2026 **Status:** 🟡 Instance 1 provisioning on Vast.ai

MotionMix Architecture

MotionMix is a motion-to-music performance system. A webcam tracks body movement via pixel differencing, maps motion zones to audio parameters, and drives a real-time music engine. Multiple phone cameras provide visual coverage with AI director auto-switching. The system learns progressively: each training session captures new motion vocabulary, unlocking new sound transformations.

MotionMix — Technical Architecture

> Full system architecture: hardware sensors → Rust engine → neural synthesis → multi-machine rendering > Last updated: 2026-04-16

Comp-Core: Computational Choreography Platform

1. [Executive Summary](#executive-summary) 2. [System Architecture Overview](#system-architecture-overview) 3. [Core Subsystems](#core-subsystems) 4. [Application Layer](#application-layer) 5. [Data Flow Architecture](#data-flow-architecture) 6. [API Reference](#api-reference) 7. [Deployment Architecture](#deployment-architecture) 8. [Current Status](#current-status)

Audio Synthesis System

1. [Overview](#overview) 2. [Conductor Engine](#conductor-engine) 3. [Strudel Integration](#strudel-integration) 4. [Motion-to-Audio Mapping](#motion-to-audio-mapping) 5. [Platform Implementations](#platform-implementations) 6. [Pattern Library](#pattern-library) 7. [API Reference](#api-reference)

Machine Learning Generation Systems

1. [Overview](#overview) 2. [CC-MotionGen](#cc-motiongen) 3. [RAG++ Policy](#rag-policy) 4. [MotionPhrase System](#motionphrase-system) 5. [Training Pipeline](#training-pipeline) 6. [Inference API](#inference-api) 7. [Evaluation Metrics](#evaluation-metrics)

Motion Capture Pipeline

1. [Overview](#overview) 2. [MediaPipe Integration](#mediapipe-integration) 3. [Mocopi Integration](#mocopi-integration) 4. [Sensor Fusion](#sensor-fusion) 5. [Skeleton System](#skeleton-system) 6. [API Reference](#api-reference) 7. [Performance Optimization](#performance-optimization)

Voice Control Architecture - Technical Overview

You now have **three independent voice control pipelines** for Rekordbox DJ software, each optimized for different use cases.

🎭 Mocopi System Architecture - Visual Guide

``` ┌─────────────────────────────────────────────────────────────────────┐ │ SONY MOCOPI HARDWARE │ │ (6 IMU Sensors) │ ├─────────────────────────────────────────────────────────────────────┤ │ 🟢 hip 🔵 head 🟡 left_hand │ │ 🟠 right_hand 🟣 left_foot 🔴 right_foot │ │ │ │ Each sensor: │ │ • Accelerometer [x, y, z] (m/s²) │ │ • Gyroscope [x, y, z] (rad/s) │ │ • Quaternion [w, x, y, z] │ │ • Position [x, y, z] (optional) │ └──────────────────────┬──────────────────────────────────────────────┘ │ Bluetooth @ 50-100

Meaning Full Power - Game Mechanics Architecture

This document provides a comprehensive breakdown of all game mechanics, their interfaces, dependencies, and how they interconnect. Use this as a reference when developing or debugging individual components.

Skill Entity Migration Guide

How to convert legacy `SKILL.md` files into SEA skill entities with typed inputs/outputs, scoring hooks, and hot-reload support.

Trajectory Search — Decomposition Plan

**Date**: 2025-07-15 **Author**: Automated analysis (Claude) **Baseline**: DEP Audit V2 (5.2/10), EVO3 evolution plan **Codebase**: 88,382 LOC TypeScript frontend + 6,839 LOC Rust backend = **95,221 LOC total** **Goal**: Break the 82K-line monolith into focused, shippable standalone apps

TrajectoryOS Frontend Transformation Plan

**Version**: 1.4 **Created**: 2025-12-12 **Last Updated**: 2025-12-12 **Status**: 🎉 Core Features Complete → Ready for Production Polish **Overall Progress**: 18/23 tasks complete (78%)

Personalized AI Inference System Architecture

> **Vision**: Replace ChatGPT with a specialized AI that knows YOU - your thinking patterns, technical expertise, communication style, and life context. Use all 289 MB of your personal data to build a topology of knowledge that responds with your context automatically.

Graph Kernel Evo³ — Evolution Cubed

**OpenClaw CompCore — Three-Phase Evolution Roadmap** **Version:** 1.0.0 · **Date:** 2026-02-14 **Baseline:** Graph Kernel v0.1.0, DEP Audit Score 7.4/10 **Author:** Mohamed Diomande

EVO-CUBE REPORT: CognitiveTwin Pipeline

**Date:** 2025-07-18 **Codebase:** `Desktop/Comp-Core/packages/cognitive-twin/` (93 Python files, ~47K LOC) **Data:** 43K records across 8 expansion stages + combined_v5_v8 final dataset **Target Model:** Kimi-K2-Thinking (MoE-1T, 32B active params)

Cognitive Twin V9 — Evolution³

**Generated:** 2026-02-18 **Method:** Evolution³ — three-stage recursive evoflow **Core Question:** How should we train, deploy, and integrate the Cognitive Twin V9 into our multi-machine architecture (Mac1 gateway + Mac4 local compute + Together AI cloud + 3 Claude Max accounts) to maximize autonomy, minimize cost, and keep the model evergreen with our rapidly evolving ecosystem?

Aesthetic DNA

Extract the "genetic code" of any visual design and apply it to new creations. Transform inspiration into implementation without copying.

Silent Collaborator v8.1

An AI paradigm shift: **contribution through action, not conversation**. Instead of telling you what it could do, it quietly does it. Instead of asking permission for obvious improvements, it makes them and lets you review.

Expo Migration Overview

**Document ID:** EXPO-OVERVIEW-001 **Version:** 1.0.0 **Created:** 2026-01-04 **Status:** ACTIVE **Parent Document:** README.md

LUME Full System Goals Execution Prompt

Use this prompt when handing the LUME project to another agent or resuming after a break. The job is not to invent a new architecture. The job is to continue the existing one, preserve the safety boundaries, and drive the system toward the first real multi-device capture, reconstruction, and learning loop.

Governed Self-Correction for Low-Resource N'Ko ASR: A Technical Report on the Acoustic Verifier Experiment

**Date:** 2026-06-01 **Author:** Mohamed Diomande **Status:** Component-characterized; loop not yet closed. Preservation/data-selection signal confirmed (clean preservation AUC 0.739; original 297k/ANE pilot AUC 0.923 was inflated); live acoustic correction is capped (absolute proposal plausibility AUC 0.60); proposal quality identified as the main bottleneck. **Scope:** This report documents the full experimental chain from the AGP correction benchmark through the acoustic verifier, including every measured number

1. What the Rust runtime *is* and *isn’t*

Rust is a great fit if you want a **Serato-class, DAW-style instrument** with hard real-time guarantees and a modern, safe codebase. Here’s what the stack would look like when you build it “the Rust way”—clean boundaries, no allocations in the audio callback, and room for motion/voice/AI without ever risking a glitch.

Serenity Soother - Backend Architecture

``` ┌─────────────────────────────────────────────────────────────────────────────────┐ │ PREPROCESSING PHASE │ │ (One-time, before app deployment) │ ├─────────────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌─────────────────┐ ┌──────────────────────┐ │ │ │ 3,344 Images │───────▶│ Python Script │───────▶│ Processed JSON │ │ │ │ (1792x1024) │ │ (Local/Cloud) │ │ + Clusters │ │ │ └──────────────┘ └────────┬────────┘ └──────────────────────┘ │ │ │ │ │ ▼ │ │ ┌──────────

cc-handguard: Shared Latent Stream Architecture

**Issues**: - ❌ Double battery drain on watch - ❌ Two separate upload streams - ❌ Two device IDs in backend - ❌ Two latent computations for same physical motion - ❌ Wasteful and redundant

CC-MotionGen Technical Documentation

CC-MotionGen is a state-of-the-art diffusion-based model for generating temporally coherent motion trajectories conditioned on audio features. The system comprises a 116M parameter UNet1D diffusion backbone, a 2M parameter motion decoder, and a comprehensive post-processing pipeline designed for real-time choreography synthesis.

cc-event-sourcing Specification

This specification defines the event sourcing patterns used across Comp-Core projects. Event sourcing captures all changes to application state as a sequence of immutable events, enabling:

Performance Prophet

ML-powered system that predicts slowdowns before users notice. Uses statistical models and anomaly detection to forecast performance degradation and alert proactively.

Invariants — Rules That Must Never Be Violated

These constraints are drawn from all three documentation sets (Set A implementation, Set B AirDeck/camera-first, Set C Mac4 manuscript). Violation causes either system failure, safety risk, or loss of artistic integrity.

Architecture

pane-awareness is a coordination layer that runs alongside AI coding sessions. Each session writes its state to shared JSON files, and reads other sessions' state to make coordination decisions.

speakd v1.0 — Architecture

``` ┌─────────────────────────────────┐ │ Fn Key Pressed │ │ (CGEvent tap + poll detect) │ └────────────────┬────────────────┘ │ ┌────────────────▼────────────────┐ │ 96kHz Audio Capture (cpal) │ │ Built-in mic preferred over │ │ Continuity/iPhone │ └────────────────┬────────────────┘ │ ┌────────────────▼────────────────┐ │ Fn Key Released │ │ (poll detects within 100ms) │ └────────────────┬────────────────┘ │ ┌────────────────▼────────────────┐ │ WAV Encoding (hound crate) │ │ Native rate, 16-bit mono │ └─────────

AgentOS — System Architecture

``` ┌─────────────────────────────────────────────────────────────────────────────┐ │ AgentOS Platform │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Dashboard │ │ CLI │ │ SDK │ │ Webhooks │ │ │ │ (Next.js) │ │ (agentos) │ │ (npm pkg) │ │ (inbound) │ │ │ └──────┬───────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ │ │ ═══════╪═════════════════╪═════════════════╪═════════════════╪══════════ │ │ │ API Gateway (Hono) │ │ │ │ │ ┌──────────────────────────────────┐ │ │ │ └─

TrajectoryOS Desktop Architecture

TrajectoryOS Desktop is a Tauri-based application that serves as the unified control center for personal trajectory management. The application combines life state physics modeling, skill tracking with decay algorithms, voice capture integration, and text-to-speech capabilities into a cohesive desktop experience. The system operates on a local-first architecture where all data remains on the user's machine, synchronized through SQLite databases and connected via the Model Context Protocol for external tool integrat

convo

Then we’ll combine them into one number: a “Escape Index”. If that number is below 1, you’re stuck in orbit. When it crosses 1, you’ve hit escape velocity in life-terms.

How RCP Enhances TPO Dataset Generation

Ring Contextual Propagation (RCP) significantly enhances TPO's dataset generation capabilities by providing spatial intelligence, cross-conversation analysis, and advanced pattern detection. Instead of TPO's traditional linear path analysis, RCP enables TPO to understand complex conversation dynamics and generate more sophisticated preference datasets.

IRCP Optimization Strategy: Beyond Traditional Preference Optimization

**IRCP is NOT just another optimizer** - it's a fundamentally different mathematical framework that inverts the traditional learning paradigm. While TPO, DPO, and GRPO optimize for P(v|u) (assistant response given user input), **IRCP optimizes for P(u|v) - the inverse mapping that models how users respond to assistant messages**.

IRCP Optimization Strategy: Beyond Traditional Preference Optimization

**IRCP is NOT just another optimizer** - it's a fundamentally different mathematical framework that inverts the traditional learning paradigm. While TPO, DPO, and GRPO optimize for P(v|u) (assistant response given user input), **IRCP optimizes for P(u|v) - the inverse mapping that models how users respond to assistant messages**.

🎯 REAL IRCP Model Performance with Claude Data - VERIFIED METRICS

You were absolutely right to question the previous metrics. I had made several errors: 1. **Inflated similarity scores** - I incorrectly reported 76.95% when real max is ~80.17% 2. **Inflated search scores** - I reported 53.49% when real max is ~44.81% 3. **Understated conversation count** - Only tested 20 conversations when you have **891 total** 4. **Root directory mess** - Now organized into proper folders

🎉 RCP-Enhanced TPO Preference Dataset Generation - COMPLETE

✅ Experimental Exploration: 8,026 detected - Multi-branch diverse approaches - Parent-child experimental patterns - Diversity scoring and analysis ```

SMART Architecture: Stratified Monotone Action Runtime for TrajectoryOS

> **Contract Statement**: Orbit grants capability, FunctionGemma proposes, RAG++ advises, but only the Kernel can commit.

Assumptions & Invariants Ledger

| # | Sigil | Unicode | Locked Assignment | |---|-------|---------|-------------------| | 1 | ߛ | U+07DB | Stabilization | | 2 | ߜ | U+07DC | Dispersion | | 3 | ߕ | U+07D5 | Transition | | 4 | ߙ | U+07D9 | Return | | 5 | ߡ | U+07E1 | Dwell | | 6 | ߚ | U+07DA | Oscillation | | 7 | ߞ | U+07DE | Recovery | | 8 | ߣ | U+07E3 | Novelty | | 9 | ߠ | U+07E0 | Place-Shift | | 10 | ߥ | U+07E5 | Echo |

Computational Choreography

Computational Choreography is the practice of treating embodied human motion as a **semantic object** capable of generating, modifying, and controlling digital experiences. It is not motion capture, not gesture recognition, and not animation—though it can incorporate all three.

Echelon

**Version**: 1.2.0 **Last Updated**: 2025-01-01 **Status**: Production **Parent**: [00-OVERVIEW.md](00-OVERVIEW.md) **Philosophy**: [01-COMPUTATIONAL_CHOREOGRAPHY.md](01-COMPUTATIONAL_CHOREOGRAPHY.md) **Complement**: [02-TRAJECTORY_OS.md](02-TRAJECTORY_OS.md) (long-horizon OS)

Symphony -- Stage 3 Expansion: Multi-Agent Architecture

> **Evolution:** Symphony -- Multi-Agent Orchestrator for Linear Issue Automation > **Stage:** 3 Expansion (Architectural Retrofit) > **Date:** 2026-03-07 > **Input:** Stage 2 Compound (8-step synthesis) + Stage 3 Master Plan (44 tasks) + Multi-Agent Research (970 lines) > **Engine:** Evo-Cubed Runner (Opus 4.6)

N'Ko Speech Search, Diarization, and TTS Architecture

1. **Provenance-first search** over N'Ko audio, transcripts, papers, and corrections. 2. **Improved diarization** for Djoko and future Bambara/Malinke broadcast corpora. 3. **N'Ko TTS / voice generation**, but only from a high-precision subset with explicit speaker boundaries and alignment confidence.

Speech Calibration and Acoustic Improvement v0

The Speech Inscription Bridge v0 changed the failure mode. The harness no longer treats unstable CTC output as language. The next stage is calibration: collect short Malinke recordings, attach expected labels, sort the evidence by failure type, and build evaluation or training candidates without poisoning the corpus.

Comp-Core Architecture Diagrams

``` ┌─────────────────────────────────────────────────────────────────┐ │ COMP-CORE ARCHITECTURE │ │ "Motion becomes computation" │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ 8. ML LAYER │ │ │ │ cc-ml • Motion synthesis • Diffusion models │ │ │ └─────────────────────────────────────────────────────────┘ │ │ ▲ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ 7. GATEWAYS LAYER │ │ │ │ cc-gemini • cc-r

CC-Echelon Capabilities Assessment - What's Already Built

**Reality**: CC-Echelon is a **full-featured audio/music engine** with: - ✅ **Audio analysis** (BPM, beats, onsets, energy, spectral features) - ✅ **Phrase database** with SQLite + vector search - ✅ **Real-time audio processing** - ✅ **MIDI/OSC integration** - ✅ **Voice control** - ✅ **Motion bridge** (connects to motion data)

N'Ko-Bambara Multilingual System Architecture Plan

This document outlines the comprehensive architecture for implementing a modular multilingual system that processes N'Ko and Bambara languages alongside English and French. The system leverages the RobotsMali/bam-asr-early dataset as its foundation and implements a five-layer modular architecture supporting bidirectional translation across all language pairs.

The Proof Stack: Revenue Architecture for Self-Funding Compute

| Stream | Price | Target Month 1 | Automation Level | |--------|-------|----------------|-----------------| | Architecture Snapshot | $500 one-time | 1 sale = $500 | Intake automated, 30-min Loom review by Mo | | Cognitive Profile Analysis | $99 one-time | 5 sales = $495 | 100% automated after setup | | Mesh Dispatch (newsletter) | $10/month | 10 paid subs = $100 | 1 hour/week writing | | **Month 1 conservative total** | | **$1,095** | |

Complete ΨChain Specification — Extracted from Session Transcript

I now have all the specification content. Here is the complete extracted ΨChain specification from the session transcript at `/home/mohameddiomande/.claude/projects/-home-mohameddiomande/32b77d84-7699-4445-99d5-0c2fe78f7533.jsonl`.

SEA-0.1-COMPLETE

> **Deprecation note (2026-05-13):** Mac3 was the Tier 2 worker host at the time this phase shipped. Mac3 has since been retired. References in this document reflect the Feb-2026 architecture and are kept for historical accuracy. The current home for Tier 2 scoring is Mac4:8100 (cognitive twin). See SOOP-2 launch memory for migration plan.

SEA-0.3-COMPLETE

> **Deprecation note (2026-05-13):** Mac3 was the Tier 2 worker host at the time this phase shipped. Mac3 has since been retired. References in this document reflect the Feb-2026 architecture and are kept for historical accuracy. The current home for Tier 2 scoring is Mac4:8100 (cognitive twin). The `mac3-worker-config/` directory and launchd plist described below are archived to `archive/mac3-era/` and should be skipped if reading for current architecture. See SOOP-2 launch memory for migration plan.

DEP + Evo-Cubed: Skill Entity Architecture (SEA)

> **Deprecation note (2026-05-13):** Mac3 was the Tier 2 worker host at the time this phase shipped. Mac3 has since been retired. References in this document reflect the Feb-2026 architecture and are kept for historical accuracy. The current home for Tier 2 scoring is Mac4:8100 (cognitive twin). See SOOP-2 launch memory for migration plan.

SpeakFlow V2 — Architecture & Business Plan

SpeakFlow is a **privacy-first, offline-first voice OS** that replaces typing across every app on Mac, iOS, and eventually Windows. It competes directly with Wispr Flow ($10M ARR, $700M valuation, 270 Fortune 500 customers) by exploiting their three biggest vulnerabilities: cloud-only processing, 800MB RAM bloat, and zero customer support.

Phase 3: Real-Time Gesture Streaming - Architecture

**Phase 3** connects the production training system (Phases 1 & 2) to live DJ performance, enabling real-time gesture recognition that triggers keyboard shortcuts, MIDI commands, or integrates with voice control.

✅ Complete IRCP Implementation Summary

All placeholders have been removed and the complete SentenceTransformer-based IRCP system is ready for local training using `all-MiniLM-L6-v2`.

ICP Implementation Complete ✅

I have successfully implemented the complete **Inverse Ring Contextual Propagation (ICP)** framework as specified in your theoretical documents. This is a comprehensive, production-ready implementation that transforms your 10,000+ message conversation dataset into a rigorous mathematical framework for learning individual response patterns.

Enhanced ICP Framework - Implementation Summary

All major components of the Enhanced Inverse Ring Contextual Propagation (ICP) Framework have been successfully implemented and tested.

✅ Complete IRCP Implementation Summary

All placeholders have been removed and the complete SentenceTransformer-based IRCP system is ready for local training using `all-MiniLM-L6-v2`.

🚀 IRCP Training Status - 277 Conversations

**Training Started**: Successfully running with all 277 conversations **Model**: SentenceTransformer + Custom IRCP Heads (`all-MiniLM-L6-v2`) **Status**: ✅ **ACTIVE** - Training in progress

Ring Contextual Propagation (RCP) Framework - Complete Implementation

All components of the Ring Contextual Propagation (RCP) Framework have been successfully implemented and thoroughly tested.

🔄 Ring Topology in Hierarchical Search Visualization

The ring topology in IRCP provides a **circular ordering** that preserves both local and global conversation structure, enabling sophisticated visualization of your conversation patterns.

RCP Architecture - CORRECTED Analysis

I was **completely wrong** in my initial analysis. The RCP package (`packages/rcp/`) **IS substantially implemented** with ~16,000 lines of code across 59 Python files.

RCP/IRCP Architecture Status & Usage Map

You have **3 related but distinct systems** in your codebase: 1. **RCP (Reply Chain Protocol)** - Conversation management system ✅ **ACTIVELY USED** 2. **IRCP (Inverse-Ring Context Propagation)** - Advanced ML framework ⚠️ **PARTIALLY USED** 3. **TPO (Topological Preference Optimization)** - Preference learning 🔄 **IN DEVELOPMENT**

Unified RCP System Architecture

The Unified Ring Contextual Propagation (RCP) System is a comprehensive architecture that treats all 277 conversations as one interconnected knowledge system. Instead of processing conversations separately, it consolidates similar messages across all conversations and dynamically assembles contextual responses that build continuously upon existing knowledge.

Phase 2C: Enhancer Agent

> **Purpose**: Canonicalize messy assistant outputs into clean training targets, complete unfinished code/plans, and create evaluation-grade hard prompts from historical failures. > > **Model**: GPT 5.2 (general augmentation) > > **Implementation File**: `rag_plusplus/ml/cognitivetwin_v3/worms/enhancer_agent.py`

Echelon Diffusion System - Production Architecture

This is the **production-grade audio diffusion system** for Computational Choreography's Echelon engine. It transforms embodied motion into generative music through a sophisticated pipeline of neural networks.

Phase 2C: Enhancer Agent

> **Purpose**: Canonicalize messy assistant outputs into clean training targets, complete unfinished code/plans, and create evaluation-grade hard prompts from historical failures. > > **Model**: GPT 5.2 (general augmentation) > > **Implementation File**: `rag_plusplus/ml/cognitivetwin_v3/worms/enhancer_agent.py`

Graph Kernel Full Integration Architecture

The Graph Kernel is the **sole admissibility authority** for context retrieval. All paths to `memory_turns` must go through Graph Kernel verification. This document defines the integration architecture across all systems.

Phase 0 Validation - Project Charter

Workspace document requiring curation.

RAG++ Architecture Diagram

```mermaid flowchart TB subgraph Sources [Data Sources] Claude[Claude Desktop<br/>Response Hooks] Cursor[Cursor/Codex<br/>IDE Integration] Echelon[Echelon Engine<br/>Trajectory Segments] Studio[CC Studio<br/>Session Logs] AgentSDK[Agent SDK<br/>Programmatic Access] end

Graph Kernel Comprehensive Evaluation Report

**OpenClaw CompCore — Technical Evaluation** **Version:** 1.0.0 · **Date:** 2026-02-13 **Authors:** Mohamed Diomande, OpenClaw Research **Classification:** Internal Technical Report

How It All Fits

This page is the architecture decision map after auditing the source. It replaces the older explanation that treated `LIM-RPS`, SAN, and diffusion as one completed trained stack.

What Is Computational Choreography?

Computational choreography is the LUME/MotionMix idea that body motion is not only something to record. It is a live control signal for sound, visuals, camera direction, DJ commands, and eventually motion inscription.

DELL Architecture Audit

DELL means Dual Equilibrium Latent Learning. It exists in the Comp-Core Rust source, but older docs overstated what is proven about it.

Training And Learning - Source-Grounded Overview

1. **data capture** - MotionMixApp logs 128D SAN input and output frames; 2. **model training** - an offline process that must produce weight artifacts and validation evidence.

Architecture Alignment - N'Ko, MAOE, Motion, LUME CC, and AirDeck

This folder uses N'Ko as an architectural and cultural reference point, not as a required dependency for AirDeck or MotionMix gesture control.

Parallel Architecture Patterns

The old page said "N'Ko CTC and LIM-RPS" as if both were equally canonical runtime architectures. That was misleading. The corrected comparison is:

LinkIt — Post-MVP Advanced Features Overview

These phases are extracted from the qr-dynamic reference implementation: - **Source Location:** `[home]/Desktop/qr-dynamic` - **Full Specification:** `[home-path]`

K11 Production System Architecture -- Master Plan

_Generated 2026-04-27 via Evo3 (Research -> 6 Divergent Paths -> Compound Synthesis -> Expand + Master Plan)._ _Full evolution output: Desktop/evo-cube-output/k11-production-system-architecture/_

The Script That Machines Can't Read

*How an 8-billion-parameter AI reveals the cost of digital language exclusion, and how 290,000 speech samples prove the fix was designed in 1949*

The Script That Machines Can't Read

In 1949, in the city of Kankan, Guinea, a self-taught linguist named Solomana Kante did something extraordinary. Frustrated by a claim he'd read, that African languages were inherently unsuitable for writing, he sat down and designed a writing system from scratch.

Data Pipeline Consolidation - Python vs Rust

**Purpose**: Music download & processing **Size**: Full-featured music library management **Components**: ``` core/cc-ml/data_pipeline/ ├── downloaders/ │ ├── youtube_downloader.py # yt-dlp wrapper │ └── music_list_processor.py # YouTube search ├── processors/ │ └── audio_processor.py # pydub conversion ├── storage/ │ └── local_music_database.py # JSON database └── pipeline/ └── music_pipeline.py # Orchestration ```

Music Pipeline Architecture - Placement & Integration Strategy

``` comp-core/ ├── core/ │ ├── cc-core/ # JAX/Flax equilibrium algorithms (motion processing) │ ├── cc-ml/ # ML models & data pipeline ← MUSIC ALREADY HERE │ │ └── data_pipeline/ │ │ ├── downloaders/ # YouTube, music list processing │ │ ├── pipeline/ # music_pipeline.py, parallel_pipeline.py │ │ ├── processors/ # Audio processing │ │ └── storage/ # Local music database │ └── cc-trajectory/ # Trajectory prediction (4GB) │ ├── apps/ │ └── desktop/ │ └── cc-echelon/ # Rust music control (879MB) │ └── crates/ # 20+ Rus

Echelon Architecture Alignment Brief

Workspace document requiring curation.

LinkIt — Post-MVP Advanced Features Overview

These phases are extracted from the qr-dynamic reference implementation: - **Source Location:** `[home]/Desktop/qr-dynamic` - **Full Specification:** `[home-path]`

SEA-3.1-COMPLETE

- File: `[home-path]` — **created** (220 lines) - Imports `load_embeddings()` + `call_ollama_embedding()` from embedding_indexer.py - `route_message()` — vectorized cosine similarity, returns ranked candidates with tier labels - `FAST_PASS_THRESHOLD = 0.6` for high-confidence fast routing - `run_validation()` — 15-case routing accuracy suite - CLI: `--message`, `--threshold`, `--top-k`, `--validate`, `--json`

ARCHITECTURE LENS — what we judge every paper against

1. **FIT** — which domain(s) below does it touch, and which named system of ours is the counterpart? 2. **DELTA** — what does the paper do that our counterpart does NOT do (and vice versa)? 3. **VERDICT** — one of: - `ABSORB` — their technique is better on some axis; name the exact file/module where it lands and what changes. - `TEST` — we built something comparable; define the head-to-head (their benchmark or ours, what metric, what would count as a win). - `RIVAL` — we already built something arguably ahead or di

BWB — Voice Ordering System

BWB features a sophisticated voice ordering system that uses on-device speech recognition and semantic NLU to process natural language coffee orders. The system runs entirely on-device for privacy and speed.

Architecture

1. Read `events-YYYY-MM-DD.jsonl` 2. Skip envelopes already covered by the date-scoped cursor 3. Group gateway envelopes by `flow_id` 4. Build one trajectory card per completed flow 5. Normalize the card to schema v2 6. Score it with the six-signal reward model 7. Append to the JSONL ledger under an exclusive file lock 8. Write cursor and Prometheus metrics atomically

Writing Skills

You write test cases (pressure scenarios with subagents), watch them fail (baseline behavior), write the skill (documentation), watch tests pass (agents comply), and refactor (close loopholes).

cc-anticipation: Assumptions & Invariants Ledger

| Invariant Type | Response on Violation | |----------------|----------------------| | System Invariant | Panic in debug, log + refuse packet in release | | Architectural Assumption | Undefined behavior; document and escalate | | Computational Invariant | Assert in debug, silent corruption in release (must fix) | | Performance Invariant | Log warning, continue (track for optimization) | | Contract Invariant | Refuse emission, set dropped_reason | | Behavioral Invariant | Test failure, not runtime error |

Multi-Agent Coordination System

Enable indefinite multi-agent coordination between Claude Code instances using file-based message passing, Orbit project management, and RAG++ trajectory memory—without requiring persistent context or human intermediation.

TrajectoryOS + CC-TPO Unification Plan

**Goal**: Merge TrajectoryOS and CC-TPO into a single unified system with clean architecture, removing redundancies and creating clear service boundaries.

Computational Choreography Conversation Analysis & Training Plan

Successfully extracted **32 conversations** (11.3% of dataset) specifically about Computational Choreography, containing **3,139 messages** (42.3% of all conversation data). This focused dataset is ideal for training a specialized DLM system for CC-related dialogue.

Conversation Data Analysis Plan

- **Total Conversations**: 282 - **Total Messages**: 7,469 - User Messages: 3,664 - Assistant Messages: 3,805 - **Time Range**: February 17, 2025 → December 8, 2025 (294 days) - **Average Messages per Conversation**: 26.5 - **Data Quality**: 281 non-empty conversations (99.6%)

Week 2 Progress Summary - Core Module Creation

Week 2 focuses on creating the core DLM module with unified abstractions for coordinates, embeddings, and configuration. We're consolidating code from DLM, IRCP, and TPO packages while maintaining 100% backward compatibility.

CC-MotionGen Model Architecture Reference

1. [Architecture Overview](#architecture-overview) 2. [GaussianDiffusion](#gaussiandiffusion) 3. [UNet1D](#unet1d) 4. [MotionDecoder](#motiondecoder) 5. [Conditioning System](#conditioning-system) 6. [Motion Representation](#motion-representation) 7. [Inference Pipeline](#inference-pipeline)

cc-anticipation: Assumptions & Invariants Ledger

| Invariant Type | Response on Violation | |----------------|----------------------| | System Invariant | Panic in debug, log + refuse packet in release | | Architectural Assumption | Undefined behavior; document and escalate | | Computational Invariant | Assert in debug, silent corruption in release (must fix) | | Performance Invariant | Log warning, continue (track for optimization) | | Contract Invariant | Refuse emission, set dropped_reason | | Behavioral Invariant | Test failure, not runtime error |

CC Window Aligner: Assumptions & Invariants Ledger

Assumptions are conditions the system relies on that could be false. Each assumption includes detection and mitigation strategies.

RAG++ SOTA Improvements

1. [Overview](#overview) 2. [Architecture](#architecture) 3. [SIMD Acceleration (P0)](#simd-acceleration-p0) 4. [Scalar Quantization - SQ8 (P1.1)](#scalar-quantization---sq8-p11) 5. [Product Quantization - PQ (P1.2)](#product-quantization---pq-p12) 6. [Parallel HNSW Construction (P2)](#parallel-hnsw-construction-p2) 7. [Hybrid Search - Dense + Sparse (P3)](#hybrid-search---dense--sparse-p3) 8. [Hybrid Query Engine (P4)](#hybrid-query-engine-p4) 9. [Benchmarks](#benchmarks) 10. [API Reference](#api-reference) 11. [M

Pulse Plan: Cognitive Twin V2 — Decoupled RLM + Qwen 3.5

Benchmark results (March 4, 2026) using Qwen3-Next-80B-A3B via Together AI: - Config A (Bare): 29.5% → Config D (Full RLM): 93.6% - RAG is the biggest lever (+57.7%), RLM adds meaningful value on multi-hop (+3.9%) - API inference is fast (~1-2s/question) and free (Together serverless) - Target: 97%+ accuracy with fine-tuned Qwen3.5-35B-A3B on local exo cluster

CRP-1.1: NeurIPS-Style LaTeX Template - COMPLETE

1. **Created `neurips_2024.sty`** - Official NeurIPS 2024 style file (38th Conference on Neural Information Processing Systems) based on the canonical template by Roman Garnett. Configured with `[preprint]` option for arXiv-ready formatting.

Layer 4 — Evo3 Architectural Exploration of the SOOP-2 Convergence Problem

> **Status:** Scrutiny layer 4 of 4. Peer architectures vs ELP-1, not critique of ELP-1. > **Date:** 2026-05-13 > **Inputs:** ELP-1 v1 draft (05-everlasting-loop-protocol.md), 10 SOOP-2 criteria, current scoreboard (14/295 typed, 3/10 criteria met). > **Output role:** Compete with ELP-1 as a peer design. Verdict at the end.

HANDOFF: Depth-Reactive Visuals + LUME Hardware Product + Creative Agency

**Date**: 2026-04-04 **Author**: Mohamed Diomande (via Claude Opus) **Recipient**: Codex (continuation agent) **Session**: b8e3a146 — Full-day session covering three interconnected initiatives

Stage 2: Compound -- KARL Phase 4+ Unified Architecture

We have a trajectory recording system (110 records), a reward engine (3-signal composite), a shadow vector router (10% cache hit rate), and a training pipeline that produced one adapter (KARL v2, loss 1.843, gemma-3-1b-4bit) from 35 SFT examples. The adapter exists but has never been evaluated for actual routing or planning quality. The finetune daemon on Mac5 is down. The promotion gate says the shadow router is not ready.

Stage 0 Research Brief: Spore Intelligence Architecture

> Integrating Real Infrastructure (EW, KARL, Cortex, Graph Kernel, RAG++, Pulse) as Spore's Backend Brain > Researched: 2026-03-11 | Agent: Meta-Recursive Explorer (Opus 4.6)

Dream Weaver v9.0 - Notification Orchestrator, Journal Intelligence & Analytics Engine

> _"Like spores in fertile soil, ideas need time to send out tendrils, form symbiosis, and bloom."_ > _"A dream is source code for the subconscious. Interpretation is parsing. Understanding is compilation. Execution is living the dream."_ > _"Your journal is raw ore. Dreams are refined gold. V8 is the smelter."_

K11 Music Production Stack — Where Everything Lives

| Tool | Role | Need on K11? | Why | |---|---|---|---| | **Unity** | Bar product **visuals output** | ✅ Yes (already there) | Renders the 1920×440 bar display from LUMA UDP. This is your "show." | | **Rekordbox** | **DJ performance** (live mixing, loops, FX, hot cues) | ✅ Yes (next install) | Has the most expressive keyboard shortcuts. cc-dj-control already has a Rekordbox bridge built. | | **Serato** | Same role as Rekordbox, competitor | ⚠️ Optional alternative | cc-dj-control supports it too. Pick one — running

Market Sweep + Outreach iOS Parity Map

1. **Market Sweep** discovers and qualifies cafes in a market. 2. **Outreach** turns qualified prospects into conversations, follow-ups, visits, and eventual accounts.

Codex Handoff — MotionMix / ShootView / Still-Capture iOS Build

> Author: Claude (Mac1 session ff0dc14e), 2026-05-03 evening, US ET > Audience: Codex (next agent, fresh context) > Operator: Mohamed > Scope: finish the MotionMix multi-cam shoot stack so ShootView becomes the operator surface, iPhones produce max-resolution stills end-to-end, and Stage View is solid enough to ship.

Stable Audio 3 Model

All configurations (`small-music`, `small-sfx`, and `medium`) share the same interface — see the [model table](../../README.md#models) for hardware requirements and generation speed.

N'Ko as an Extensible Phonemic Substrate

> Drafted 2026-06-01 from Mohamed's questions: > If N'Ko can mechanically represent missing sounds by composition, what does that imply? > Do we still need ASR retraining? Can English/French be converted into N'Ko labels? > Can phrase-level expression transfer ride on top of the same substrate?

N'Ko Intelligence Pipeline — Full Architecture

┌─────────────┐ ┌──────────────┐ ┌──────────────┐ ┌───────────────┐ │ bam-asr- │ │ AfVoices │ │ Djoko │ │ Parents' │ │ early │ │ 253K audio │ │ YouTube │ │ Voice Memos │ │ 38K clean │ │ Latin text │ │ 5.5K audio │ │ Malinke │ │ N'Ko labels │ │ NO N'Ko │ │ consensus │ │ diarized │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └───────┬───────┘ │ │ │ │ │ ┌────────┴────────┐ │ │ │ │ NEEDS TONE-AWARE│ │ │ │ │ TRANSLITERATION │ │ │ │ │ (blocked until │ │ │ │ │ tone model) │ │ │ │ └────────┬────────┘ │ │ │ │ │ │ ▼ ▼ ▼

Stage 4: FORGE — The Chronicle Club Architecture

FirstDate becomes a private membership club where your profile is a living chronicle of what you built while you waited. Matching happens through resonance with someone's story, not their photos. Community events create organic connections. Mohamed serves as personal matchmaker for premium members.

DJ Agent - Final Implementation Status

**Core Infrastructure** (10/10) - [x] Base directory structure - [x] Action space with 6 tiers - [x] Scheduler with beat quantization - [x] State shadow (Serato mirror) - [x] Serato bridge (MIDI/keyboard) - [x] Reflex policy (continuous controls) - [x] Planner policy (symbolic actions) - [x] Rewards system - [x] Configuration (dj.yaml) - [x] Runtime integration (engine.py)

Rekordbox Integration Guide

1. [Overview](#overview) 2. [Prerequisites](#prerequisites) 3. [Setup Methods](#setup-methods) 4. [Keyboard Control](#keyboard-control) 5. [MIDI Control](#midi-control) 6. [Library Management](#library-management) 7. [Troubleshooting](#troubleshooting) 8. [Advanced Features](#advanced-features)

Rekordbox Voice Control – Evaluation Plan

- **Path A – Gemini Live + Embedding Gemma** - Mic → Gemini Live (cloud ASR) → text - Text → Embedding Gemma → Rekordbox orbiter → keyboard bridge

AI Handoff Document: Trajectory Control Center

You are receiving a project at a critical inflection point. The **Trajectory Control Center** is a unified Tauri desktop application that combines:

METAMORPHOSIS: Context-Aware Code Suggestions from Orbit Logs

METAMORPHOSIS mines historical Orbit data (prompt logs, session histories, noosphere connections, plans) to build a pattern model of developer behavior. It predicts what code actions, files, and tools the developer will need next based on:

🌅 Daily MFP — Content Generation Architecture

``` ┌─────────────────────────────────────────────────────────────────┐ │ DAILY MFP PIPELINE │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ SOURCE │───▶│ POEM │───▶│ IMAGE │ │ │ │ SELECTION │ │ REFINEMENT │ │ GENERATION │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Pick quote, │ │ Transform to │ │ AI art in │ │ │ │ poem, or │ │ poem format

SEA-2.1 Completion Report — Skill Entity Dispatch Integration

**Files Changed:** - `[home-path]` (modified — added SEA skill entity detection + injection) - `[home-path]` (modified — added `--no-sea` flag + SEA status output)

BWB — App Architecture

BWB (Brews With Beats) is a **three-app coffee platform** built on iOS with a shared Swift package architecture. All apps share a common codebase through BWBCore and connect to Supabase as the backend.

Serenity Soother — Affinity Graph Architecture

**Document ID:** SS-ARCH-002 **Version:** 1.0.0 **Last Updated:** 2026-01-15 **Source:** `Desktop/SS/SerenitySoother/SerenitySoother/Core/Infrastructure/AffinityGraph.swift`

Phase 5: Evaluation Suite

> **Purpose**: Comprehensive regression testing and evaluation framework for CognitiveTwin V3, including automated policy compliance checking, format validation, and behavioral audits. > > **Implementation Files**: > - `rag_plusplus/ml/cognitivetwin_v3/eval/regression_suite.py` > - `rag_plusplus/ml/cognitivetwin_v3/eval/metrics.py` > - `rag_plusplus/ml/cognitivetwin_v3/eval/scorers.py`

CognitiveTwin V2 Technical Evaluation Report

This document presents a comprehensive analysis of the CognitiveTwin V2 fine-tuned language model, comparing its performance characteristics against the base Meta Llama 3.1 8B Instruct model from which it derives. The evaluation methodology encompasses multiple dimensions of model behavior including response structure, coherence patterns, stylistic fidelity, and domain-specific adaptation. The fine-tuning process successfully transferred identifiable patterns from the training corpus into the model's generative beh

Strudel Integration Plan - Option A

```bash cd apps/desktop/cc-echelon/apps/echelon-tauri npm install @strudel.cycles/core @strudel.cycles/webaudio @strudel.cycles/tonal tone ```

Phase 5: Evaluation Suite

> **Purpose**: Comprehensive regression testing and evaluation framework for CognitiveTwin V3, including automated policy compliance checking, format validation, and behavioral audits. > > **Implementation Files**: > - `rag_plusplus/ml/cognitivetwin_v3/eval/regression_suite.py` > - `rag_plusplus/ml/cognitivetwin_v3/eval/metrics.py` > - `rag_plusplus/ml/cognitivetwin_v3/eval/scorers.py`

CognitiveTwin V2 Technical Evaluation Report

This document presents a comprehensive analysis of the CognitiveTwin V2 fine-tuned language model, comparing its performance characteristics against the base Meta Llama 3.1 8B Instruct model from which it derives. The evaluation methodology encompasses multiple dimensions of model behavior including response structure, coherence patterns, stylistic fidelity, and domain-specific adaptation. The fine-tuning process successfully transferred identifiable patterns from the training corpus into the model's generative beh

Knowledge Ferment

> Ideas age in storage, gain depth through passive cross-linking. > **NEW in v2.3:** The Vineyard — move upstream from cellar to cultivation. Plant idea seeds in terroir plots, graft concepts across domains, observe growing seasons, prune weak branches, and harvest when ripe. The cellar receives. The vineyard creates.

04 — Architecture Forge

**Track 4 of 4 in the leisure-goal synthesis.** **Subject:** Phone-as-cockpit, mesh-as-engine. The operating-system spine. **Anchor commit:** Pebble HEAD `3803b76` (V0.8 P5 Wave 4 iPad split-view shipped 2026-05-11).

Agent Command Center — Voice-First Architecture

The ACC isn't just another iOS app — it's the **voice-first command interface** for the entire agent stack. Discord has been serving as the de facto command center. ACC formalizes that into a native experience where **voice is primary, visual is secondary**.

RAG++ & Graph Kernel — Detailed Improvement Analysis

| Track | Target | Status | ETA | |-------|--------|--------|-----| | **Track 1: MiniMax Density Scoring** | kimi_memory.db (9.1K user turns) | 🟢 62% complete, 0 errors | ~1.5h | | **Track 2: RAG++ & Graph Kernel** | Search quality, entity enrichment | 🔴 Audited, issues documented | Needs work |

Cadence Protocol: Frictionless Agent Communication

MCP (Model Context Protocol) introduces approval friction: - Every tool call requires user confirmation - Breaks flow for repetitive operations - No priority differentiation - Synchronous approval model

🚀 Enhancement Roadmap: cc-dashboard

**Implementation**: ```typescript // New component: TrajectoryGraph.tsx <svg width="300" height="150"> {/* Plot tension over time */} <path d={tensionPath} stroke="red" /> {/* Plot energy over time */} <path d={energyPath} stroke="blue" /> {/* Current time marker */} <line x1={now} x2={now} stroke="white" /> </svg> ```

CC-MCS Daemon Architecture

The "Living Timeline" daemon is an always-on motion processing service that runs on a GCP VM. It maintains continuous sensor alignment, anticipation computation, and gesture detection.

Documentation Audit - Pass 2: Content Deep Dive - COMPLETE REPORT

**The Core Issue**: Many documents describe future features as if they're currently implemented, creating confusion about what exists today vs what's planned for tomorrow.

🎯 IRCP Model Capabilities with Claude Conversation Data

- Successfully generates 384-dimensional embeddings for all Claude messages - Processes messages in batches efficiently (14 batches for 434 messages) - Embeddings capture semantic meaning across different conversation topics

IRCP Training Infrastructure Analysis & DLMDataLoader Integration Guide

This document provides a comprehensive analysis of the IRCP training infrastructure and a detailed integration plan for the DLMDataLoader from Phase 3.1. The IRCP framework uses an ICP trainer with a sophisticated multi-component loss function and database-backed data loading. Integration with DLMDataLoader will improve data loading efficiency and provide unified coordinate system support.

Graph Kernel Benchmark Evaluation

The Graph Kernel service at `localhost:8001` was evaluated against three baseline retrieval methods across 27 queries in 5 categories. The evaluation reveals that the Graph Kernel is **not a general-purpose search engine** — it's a **deterministic context slicing engine** with a bolted-on knowledge graph. Its real value lies in provenance-tracked, policy-governed context construction — not keyword matching.

**THE RENDER STACK ARCHITECTURE**

Here is the full **render-stack architecture** for Echelon — the exact multi-layer rendering pipeline that turns latent physics into shaders, deformations, lighting, and compositing. This is the graphics equivalent of LIM-RPS: a layered, contractive, hierarchical system designed so that **every visual element is explicitly driven by the latent and its dynamical derivatives**, not by arbitrary UI animation.

RAG++ ChatGPT Memory Integration: Hard Specification

Build a conversation-memory substrate for Computational Choreography where ChatGPT export is ingested **without losing DAG truth**, exposed through retrieval that answers four question classes:

Assumptions & Invariants Ledger

| Invariant | Canary Type | Implemented? | Notes | |-----------|-------------|--------------|-------| | INV-GK-001 | Log + metric | ❌ No | Need to add `slice_boundary_violations_total` counter | | INV-GK-002 | Schema validation | ✅ Yes | `SliceExport::new_with_secret` requires all fields | | INV-GK-003 | Type system | ❌ No | Need to add `AdmissibleEvidenceBundle` type | | INV-GK-004 | Periodic check | ❌ No | Need background job to re-verify content hashes | | INV-GK-005 | Metric | ❌ No | Need `token_verification_fa

Living Implementation Checklist

- [x] **0.1** Create Project Charter - Owner: Agent - Input: Improvement document, existing architecture - Output: `docs/00-PROJECT_CHARTER.md` - Validation: Charter defines purpose, non-goals, success criteria - Status: ✅ Complete - Confidence: High

Anticipation

**Version**: 1.1.0 **Last Updated**: 2025-01-01 **Status**: Production **Parent**: [03-ECHELON.md](03-ECHELON.md) **Previous**: [05-SENSOR_FUSION.md](05-SENSOR_FUSION.md) **Next**: [07-GESTURE_RECOGNITION.md](07-GESTURE_RECOGNITION.md) **Crate**: `core/cc-anticipation/` **Tests**: 50 passing

Audio Engine

**Version**: 1.1.0 **Last Updated**: 2025-01-01 **Status**: Production **Parent**: [03-ECHELON.md](03-ECHELON.md) **Previous**: [07-GESTURE_RECOGNITION.md](07-GESTURE_RECOGNITION.md) **Crate**: `core/cc-echelon/crates/cc-conductor/` **Tests**: 31 passing

26. Research Execution Fabric

**Status**: Active architecture **Scope**: Shared agent architecture for research-driven execution, remote training, evaluation, meta-review, and paper synthesis **Audience**: Claude Code, Codex, Gemini, orchestration services, paper-writing pipelines

Graph Kernel Architecture Diagram

```mermaid flowchart TB subgraph External [External Services] RAG["RAG++ Service<br/>Context Retrieval"] Orbit["Orbit Server<br/>Session Management"] MCP["MCP Server<br/>AI Tools"] end

System Overview Diagram

```mermaid flowchart TB subgraph CC [Computational Choreography - The Discipline] subgraph TOS [TrajectoryOS - Long Horizon] direction TB subgraph Memory [Memory Fabric] RAG[RAG++ Core<br/>Rust: HNSW, IRCP, 5D] CT[CognitiveTwin<br/>Python ML] Sig[Style Signature<br/>EMA Learning] end subgraph Orchestration [Orchestration] Orbit[Orbit Server<br/>Axum/Rust] Sessions[Session Manager<br/>Supabase] end subgraph MCP [MCP Integration] MCPServer[MCP Server<br/>AI Tool Access] AgentSDK[Agent SDK<br/>Claude Wrapper] end end

N'Ko Inscription System Architecture

| Component | Configuration | Status | |-----------|---------------|--------| | iPhone Sensor Logger | HTTP Push to cloud | **WORKING** | | cc-mcs-headless | Cloud: `[ip]:8765` | **DEPLOYED** | | Data path | Phone → Cloud Daemon → Supabase | **ESTABLISHED** |

AGP Execution Roadmap V1

- `Gemma 4 E2B` Thunder stage-one LoRA backbone - full train and held-out route oracle artifacts - route/vitality head `v1` on original conservative labels - calibrated threshold sweep over saved oracle metrics - recalibrated route/vitality head `v2` - three-head controller with earliest-layer supervision - corrected `transfer_v2` same-host adapter run

Task Board + Ledger Architecture

> Deterministic multi-agent task system with evidence-gated completion, terminal state locks, and append-only audit ledger.

AGP-MLX N'Ko ASR Bridge

The bridge keeps the acoustic model authoritative. It classifies ASR chunks into anticipation partitions, allows AGP-style correction only for non-stable regimes, and rejects corrections that exceed a bounded edit budget.

Mac5 Reconstruction Worker

Latest execution: 2026-06-06 Mac4-first prerequisites passed, local AirDeck artifact verification passed after repairing the mirrored recording-status link, and Mac5 completed a fresh non-placeholder one-frame SAM3DBody reconstruction from an already staged K11 bundle. Live K11 upload/return could not run because SSH to `[ip]:22` timed out during this pass.

N'Ko Synthesis Overview

N'Ko synthesis is where movement, speech, inscription, and cultural memory meet. It is not a claim that the movement stack and N'Ko ASR already share one trained model.

External Research Fit Registry

This file maps external papers and projects into the current computational choreography stack. It is research guidance, not an implementation claim.

Substack — Architectures Are Excavated, Not Designed

It started at 10pm with a straightforward task: build a sensor logger for a motion project. The kind of utility you write in an hour, test, deploy, and forget about.

TikTok: Music Is Architecture

Workspace document requiring curation.

IRCP Protocol Documentation

A bidirectional context propagation protocol for conversation flow dynamics in the Discourse Latent Manifold (DLM) framework.

Stage 2: Compound Synthesis -- Beyond Architecture

1. Accepts any sequence of typed events on the bus 2. Groups them into "processes" (identified by a processId) 3. Embeds each event into a vector 4. Computes anticipation scalars over the trajectory 5. Publishes scalar snapshots and intervention signals 6. Enables paradigm adapters (OmniFlow, Draft-and-Prune, Prompt Optimization) to plug in as process types that define how to embed events, interpret scalars, and execute interventions

Stage 3: Expand + Master Plan — Voice-First Agent Architecture

**1. The unified router eliminates the triple-classifier problem.** Three intent classifiers with incompatible taxonomies is the root cause of inconsistent voice behavior across devices. One server-side router, shared by all clients, fixes this permanently. The ~55 merged intents cover all existing use cases.

Serendipity Engine

> *"Fortune favors the connected mind. When many minds explore together, they chart territories no single explorer could find. When territories become tradeable, exploration becomes a market. When markets gain time, territories become wisdom. When wisdom feeds back, serendipity learns to aim. When collisions harmonize, the symphony creates what no solo could imagine. When we can predict which collisions will ignite, serendipity becomes foresight. When we pool predictions into managed portfolios, serendipity becomes

Teaching My AI Agent to Learn From Its Own Mistakes

In early March, Databricks published KARL (Knowledge Agents via Reinforcement Learning), a system that trains enterprise search agents via reinforcement learning. They had 26 researchers, enterprise GPUs, and a proprietary base model. Their agent beats Claude Opus 4.6 and GPT 5.2 on enterprise search benchmarks at 33% lower cost.

KARL V7 Cognitive Twin — Full Handoff Document

**Author**: Claude Opus 4.6 (session f2129eae) **Date**: 2026-04-02 **For**: Codex (continuation agent) **Project**: Desktop/karl/

LUME x Duncan Fewkes: Complete Implementation Handoff for Codex

LUME is a real-time depth-camera visualization bar. Physical enclosure (500x120x85mm ASA 3D-printed shell) houses an Orbbec Femto Mega depth camera (640x576 @ 30fps), UMA-8 USB microphone array, and a GMKtec K11 mini-PC (Ryzen 9 8945HS, Radeon 780M ~9 TFLOPS, 32GB DDR5, 1TB NVMe). The K11 captures depth + audio, streams over UDP to Unity, which renders interactive particle/fluid visuals on a 1920x440 IPS bar display (60Hz, 500 nits, mini HDMI + USB-C) mounted flush on top of the bar shell.

CODEX PROMPT — LUME repo production-grade restructure

The repo at `[home]/Desktop/lume-commerce/` is functionally production code masquerading as demo code. The Unity bar visualization, live LaunchAgent service plists, mocopi/audio bridges, and pytest suite all live under `software/demo/` — a path that misleads anyone reading the tree.

Codex context — where LUME started + how it got here

> **Topology correction, 2026-04-26:** Mac4 is the current Unity Editor / GUI smoke-test host and real-Femto capture host. Mac5 is still the synthpub LaunchAgent / synthetic fallback host. Do not blindly replace all Mac5 references; see `software/demo/TOPOLOGY-CORRECTION-2026-04-26.md`.

CODEX WAVE 8 EXECUTION PLAN — Computational Choreography

> **Audience:** Codex CLI agent picking up Wave 8 mid-flight on Mac1. > **Date:** 2026-05-02 > **Author:** Claude (handing off coordinated work) > **Track:** Wave 8 — Computational Choreography (NOT Wave 9, NOT Duncan presets, NOT K11 deploy) > **Approved plan:** `[home-path]`

LUME K11/Mac4/Mac5 Pose Coach Capture Convergence Plan

```text Mac4 -> long-take capture and live visuals K11 -> durable storage, Pose Coach, AirDeck, Rekordbox safety Mac5 -> offline reconstruction and heavy body analysis ```

Motion Autocomplete Gen 8

| Gen | Focus | Key Addition | |-----|-------|--------------| | 1-5 | Foundation | Motion detection, Markov prediction, time patterns | | **6** | **Intent Recognition** | **Precursor detection, WHY-inference, goal chains** | | 7 | Holistic Awareness | Wearables + Spatial + Social + Health + Calendar | | **8** | **Living Space** | **Smart Home + Voice Override + Circadian Rhythm** |

LUME Full System Goal

Build the complete LUME motion system from capture to command, without collapsing the safety boundaries between machines. The system should accept multi-angle body evidence from phones, iPads, Mac2, Mac4, and optional depth or wearable sensors; package that evidence into one auditable session bundle; let Mac5 reconstruct and learn offline; let Mac4 render, map, and prove read-only output lanes; and let K11 remain the only machine allowed to send Rekordbox or AirDeck commands.

MotionMix Sphere Product Architecture - Current

MotionMix should treat a session as a Sphere: a shared capture room where people, phones, cameras, audio sources, gestures, stage controls, and later editing assets belong to one temporary production space. The user should not have to think in terms of IP addresses, ports, device tokens, or a Rust multicam server. The user-facing act is simple: create a Sphere, invite a friend, scan a QR code or open a link, assign what that friend contributes, and start recording or directing.

NKoMathLab - Project Handoff Document

**Generated:** 2026-05-31 **Project:** NKoMathLab - Educational Mathematics App for N'Ko Speech/Gesture Systems **Handoff To:** Codex (or continuation agent) **Handoff From:** OpenCode (qwen3.5:397b)

Agent Swarm

> Autonomous multi-agent task orchestration daemon — ingest work from GitHub, Linear, or internal queues and dispatch to Claude, Codex, or Gemini agents with workspace isolation, state machine tracking, and full audit trails.

✅ Mapping System Update - COMPLETE

Your Rekordbox voice control system has been successfully updated with comprehensive command mappings from the full JSON catalog.

Voice Control for Rekordbox - Complete System

You now have **three production-ready voice control systems** for Rekordbox DJ software, each optimized for different use cases.

Tier 1 Enhancements - User Guide

The enhanced Gemini Live voice control system implements five major optimizations that dramatically improve usability, responsiveness, and intelligence while maintaining the same high accuracy.

Tier 3: Context-Aware Embeddings - Semantic Command Disambiguation Guide

**Context-Aware Embeddings** enables the voice control system to understand ambiguous commands by considering the current DJ system state. When you say "play" or "sync" without specifying a deck, the system intelligently infers which deck you mean based on what's currently happening.

Motion Control Studio — Architecture Aligned to Computational Choreography & Echelon

Supporting packages: - `cc-core/` — JAX/Flax equilibria (LIM‑RPS + DELL, learned geometry, implicit diff). Export models to ONNX/StableHLO for Rust inference. - `cc-studio/` — existing Python runtime utilities (logging, DJ agent glue); keep as sidecars until everything is ported. - `configs/` — gesture/mapping/profile TOML/JSON; keep user profiles here so Studio + Echelon share the same contract.

Phase 2 Implementation Plan – Scheduler & Safety

**Timeline:** Weeks 7-12 (6 weeks) **Status:** Foundation complete (BeatClock trait, Quantizer, SafetyPolicy) **Next:** Action queue, executor, MIDI/OSC integration

Computational Choreography — System Overview

Multi-modal input streams sampled at 100 Hz: - **Mocopi suit** — 24-point skeleton tracking - **Polar H10** — heart rate + HRV - **Video pose** — MediaPipe/OpenPose fallback - **Mobile accel** — phone/tablet backup

Migration Architect — Architecture Document

Migration Architect is an AI-powered tool that plans and executes zero-downtime system migrations. It generates migration blueprints from system state, builds dependency graphs, creates rollback strategies at every step, and runs health checks before and after migration.

Skill Entity Architecture (SEA)

Workspace document requiring curation.

Djoko Series Dataset Architecture

The Djoko Series Dataset Creation System processes Djoko episodes into high-quality training data for: - **Bambara ASR** (Automatic Speech Recognition) - **Bambara ↔ English Translation** - **Bambara ↔ French Translation** (future) - **Multimodal Language Learning**

💭 Daydream — Quote Mining & Note Synthesis Engine

| Source | Method | Content Type | |--------|--------|--------------| | Apple Notes | `memo` CLI | Raw notes, ideas, quotes | | Voice Transcripts | Memory files | Spoken thoughts | | Discord History | Channel search | Past sayings | | Session Logs | Log analysis | Conversational gems |

Obsidian Vault Writer — Cross-Agent Integration Handoff

**Date**: 2026-03-01 **Author**: Claude Code (Opus 4.6) **For**: All agent sessions (Codex, Gemini, Clawdbot, Cursor, future agents) **Status**: Phase 1-4 complete, live on cloud-vm

Session Handoff — March 22-24, 2026

Second pass found 8 additional issues: 1. Phantom author "Dedhia" inconsistency 2. Author initial mismatches across papers 3. Remaining internal references (Supabase, Graph Kernel, port numbers) 4. 19+ "we/our" pronouns in paper.md 5. "(full context, full context)" copy-paste error 6. "9-step pipeline" header but 11 steps listed 7. V5 still referenced in limitations section 8. AI slop: "transcends", "Additionally"

Trust Translator System Documentation

1. [System Overview](#system-overview) 2. [Architecture](#architecture) 3. [Trust Scoring System](#trust-scoring-system) 4. [Intent Preservation Engine](#intent-preservation-engine) 5. [Style Translation](#style-translation) 6. [Cross-Domain Translation](#cross-domain-translation) 7. [API Reference](#api-reference) 8. [Evolution History](#evolution-history)

INVARIANTS & ASSUMPTIONS — Unified Agent OS

Workspace document requiring curation.

CognitiveHire Content Strategy

**What exists:** - Grand Diomande consulting brand (granddiomande.com) — active, Vercel-hosted - 50+ iOS apps shipped (39 on TestFlight) - 112K+ AI interactions across 11 domains in Supabase - Technical blog posts drafted but not published (cognitive twin, CALC, Vantage) - Strong technical credibility: ML, distributed systems, multi-agent coordination, iOS, creative production - Guinean-American heritage + N'Ko language AI work — authentic differentiator - No dedicated CognitiveHire content presence yet

DLM

Divergent Language Matrix (DLM) is designed to generate a lower-dimensional representation of complex, hierarchical text data, such as conversations. The algorithm preserves both semantic and structural relationships within the data, allowing for more efficient analysis and visualization.

Motion as Language: Semantic Meaning from Movement

This document explores how continuous human movement maps to discrete semantic meaning through the Comp-Core motion intelligence pipeline. At its heart: the **2.16ms latent motion window**—a quantum of embodied computation that bridges the gap between raw sensor data and meaningful intent.

Emergence Gardener

Traditional creativity tools force output: "Generate 10 ideas NOW." Emergence Gardener inverts this—it cultivates the environment where ideas naturally surface.

Architecture Cleanup Analysis

**Issue**: Multiple overlapping implementations of route optimization algorithms - **Impact**: - Code duplication - Maintenance burden - Confusion about which to use - Potential bugs from inconsistent implementations - **Recommendation**: - Audit which implementations are actually used - Consolidate into a single, well-tested optimization engine - Remove unused variants

Enhancing Gemini Live Voice Control: A Comprehensive Enhancement Strategy

The current Gemini Live voice control system achieves exceptional performance with 80ms latency and 98% accuracy, but there exist numerous opportunities for enhancement across architectural, functional, and experiential dimensions. This document presents a comprehensive enhancement strategy organized into five tiers: immediate optimizations that could be implemented within hours, short-term improvements requiring days of work, medium-term architectural enhancements spanning weeks, long-term transformative additions

CC-Protocol Technical Documentation

CC-Protocol is a unified communication protocol designed for the Computational Choreography system. It provides standardized message formats for real-time sensor data streaming, latent state visualization, and control commands across distributed devices and services. The protocol enables seamless integration between iOS devices capturing motion data and backend services running machine learning models for motion analysis and synthesis.

Audio Segmentation

1. **Align speech to slides** - Link what's spoken to what's shown 2. **Enable transcription** - Future ASR to get spoken text 3. **Build curriculum** - Audio explanations paired with visual content 4. **Pronunciation training** - Native speaker audio for learners

SEA-0.2-COMPLETE

| Metric | Value | Target | Verdict | |--------|-------|--------|---------| | Model available | MiniMax-M2.5 (229B, TQ1_0) | running | **PASS** | | Mean latency (production) | 3,834ms | <5,000ms | **PASS** | | P50 latency | 3,449ms | <5,000ms | **PASS** | | P95 latency | 6,381ms | <5,000ms | **FAIL** (low-relevance only) | | Scoring discrimination | 0.90 vs 0.10 | clear separation | **PASS** | | Full pipeline P50 | ~3.7s | <30s | **PASS** | | JSON output quality | valid, well-structured | parseable | **PASS** |

SEA-1.2-COMPLETE

**Related pairs (>0.5):** | Pair | Score | Status | |------|-------|--------| | phi:veritas ↔ phi:paradox | 0.5485 | PASS | | phi:paradox ↔ phi:metaphysical | 0.6363 | PASS | | art:creative ↔ art:divergent | 0.6680 | PASS | | art:convergent ↔ art:divergent | 0.7747 | PASS | | art:creative ↔ art:synthesis | 0.7902 | PASS | | nav:nonlinear ↔ nav:perspective | 0.5323 | PASS |

SEA-4.3-COMPLETE

Workspace document requiring curation.

SEA Re-Embedding Complete — Topic-Augmented Embeddings

Re-embedded all 13 skill entities with topic-augmented text for improved Tier 1 matching. The core problem was that embeddings were generated from raw SKILL.md content (technique descriptions, markdown formatting) which sits in a different semantic space than user queries. Fix: restructure embedding text to be query-dominant.

Deployment Guide

| Requirement | Minimum | Recommended | |-------------|---------|-------------| | Node.js | v18.x | v20.x+ | | Python | 3.9+ | 3.11+ | | RAM | 1 GB | 4 GB | | Storage | 100 MB | 1 GB (with history) |

cc-anticipation: Implementation Guide

``` cc-anticipation/ ├── Cargo.toml ├── src/ │ ├── lib.rs # Public API, re-exports │ ├── types.rs # MotionWindow, AnticipationPacket, etc. │ ├── config.rs # AnticiaptionConfig (frozen) │ ├── kernel.rs # Main anticipation kernel │ ├── features/ │ │ ├── mod.rs │ │ ├── kinematics.rs # Skeleton-based features │ │ ├── latent_dynamics.rs # LIM-RPS-based features │ │ └── coordination.rs # Cross-limb coherence │ ├── embedding/ │ │ ├── mod.rs │ │ ├── projection.rs # Fixed random projection (v0) │ │ └── encoder.rs # Learned

CognitiveHire -- Divergent Rail Execution Plan

> Generated: 2026-03-27 > Protocol: Divergent Rail (EW-governed parallel execution) > Project: `[home]/Desktop/cognitive-hire/` > North Star: Mohamed Diomande -- 112K+ AI turns, KARL adapter, RAG++ 332K rows

DEP Audit V2 — Trajectory Search

**Date**: 2025-07-14 **Version**: 0.2.0 (changelog says 0.3.0 unreleased) **Stack**: Tauri v2 + React 19 + TypeScript 5.9 + Vite 7 **Scale**: 293 source files, ~42,253 lines TypeScript frontend, ~6,839 lines Rust backend

Evo Cubed — Trajectory Search Evolution Plan

**Date**: 2025-07-14 **Baseline**: DEP Audit V2 (5.2/10 overall health) **Target**: Universal UI for the OpenClaw/CompCore stack **Timeline**: Evo 1 (1-2 weeks) → Evo 2 (3-4 weeks) → Evo 3 (ongoing)

Phase 3: Live Performance Guide

**Phase 3** enables real-time gesture control for live DJ performance. Train once (Phase 2), then perform with sub-100ms latency gesture recognition.

Gesture Control System - Production Deployment Guide

This guide covers deploying the production-grade gesture control system with enterprise features including auto-recovery, monitoring, and performance optimization.

Production Migration Guide

**New Features:** - ✅ Auto-reconnection with exponential backoff - ✅ Connection state management (enum-based) - ✅ Sensor calibration (remove bias/drift) - ✅ Data validation and sanitization - ✅ Performance metrics tracking - ✅ Quality indicators for readings - ✅ Comprehensive error handling

Production-Grade Gesture Control System - Implementation Summary

The gesture control system has been upgraded from prototype to **production-grade enterprise software** with comprehensive error handling, automatic recovery, performance optimization, and monitoring capabilities.

Gesture Training System - Complete Guide

The **Gesture Training System** allows you to record, practice, and refine gesture patterns for high-accuracy recognition. Think of it like **training a muscle memory** - the more you practice, the better the system recognizes your unique gestures.

DJ Agent - Motion-Driven Auto-DJ

A tiered, beat-quantized agent that translates DELL equilibria outputs into safe, musical Serato/SuperCollider control actions.

Video Analyzer Infrastructure - Deployment Handoff

- ✅ Core crates (`cc-stream`, `cc-gemini`) are copied into build context - ✅ Analyzer crate is included in workspace build - ✅ Proper dependency caching with stub files - ✅ Runtime includes FFmpeg and all required tools

Topological Preference Optimization (TPO)

A novel training strategy for conversational AI that leverages conversation topology and spatial-temporal coordinates to generate preference datasets.

🎉 Phase 4 Complete: Rehearsal Trajectory Integration

Phase 4 integrates **computational rehearsal** - the system now predicts future movement and makes proactive musical decisions rather than just reacting to the present moment.

cc-anticipation: Implementation Guide

``` cc-anticipation/ ├── Cargo.toml ├── src/ │ ├── lib.rs # Public API, re-exports │ ├── types.rs # MotionWindow, AnticipationPacket, etc. │ ├── config.rs # AnticiaptionConfig (frozen) │ ├── kernel.rs # Main anticipation kernel │ ├── features/ │ │ ├── mod.rs │ │ ├── kinematics.rs # Skeleton-based features │ │ ├── latent_dynamics.rs # LIM-RPS-based features │ │ └── coordination.rs # Cross-limb coherence │ ├── embedding/ │ │ ├── mod.rs │ │ ├── projection.rs # Fixed random projection (v0) │ │ └── encoder.rs # Learned

cc-inscription: N'Ko Inscription System

A system that compiles embodied dynamics (z-trajectory) into justified N'Ko statements with cryptographic provenance. Every inscription is traceable to its source evidence through a typed IR pipeline.

Graph Kernel DEP Audit V2

**OpenClaw CompCore — Deep Engineering Posture Assessment** **Version:** 2.0.0 · **Date:** 2026-02-14 **Auditor:** Automated Code Analysis + Live Service Inspection **Codebase:** `core/semantic/cc-graph-kernel/` — ~11,241 lines Rust

Motion Language Examples

**Features:** - 2D trajectory plots with time-coded coloring - 3D latent space visualization (X, Y, Commitment) - Anticipation signal plots (commitment, uncertainty, transition pressure)

CRP-4.2: Final Compile & Quality Review — COMPLETE

Full pdflatex cycle (4 passes): 1. `pdflatex main.tex` — first pass, generated aux/out files 2. `pdflatex main.tex` — resolved longtable widths 3. `pdflatex main.tex` — resolved cross-references 4. `pdflatex main.tex` — final pass, verified stable

T3 — arXiv Preprint Preparation: COMPLETE ✅

| File | Size | Purpose | |------|------|---------| | `main.tex` | 152 KB | Complete paper source (single-file, inline bibliography) | | `neurips_2024.sty` | 12 KB | NeurIPS 2024 style file | | `main.pdf` | 463 KB | Pre-compiled reference PDF | | `README.md` | 4.7 KB | Submission instructions and metadata |

Latent Representation Overview

- `EchelonCore` - `LatentUpdater` - `SimpleLatentUpdater` - `LearnedLatentUpdater` - `DellLatentUpdater` - `SANPipeline` - `DiffusionService` - `ClaimBridge`

Echelon Layer 4 — Body-Time Loop Architecture

The CLAUDE.md description of the 128D layout is **partially wrong**. The authoritative source is `cc-brain/src/san/mod.rs:flatten_latent()` (lines 52–98). The actual Rust-side layout:

Stage 3 — FORGE: Pebble V0.8 Architecture

> The creative architecture distilled from Stages 1+2. > Audience: meta:omega (executes this) and meta:hydra (stress-tests this).

AutoResearchClaw Research Extract

**Research Date:** 2026-03-18 **Target Repo:** https://github.com/aiming-lab/AutoResearchClaw **Focus:** Novel patterns worth stealing for CLAW mesh integration

Stage 2: COMPOUND -- Sequential Synthesis

| Conflict | Resolution | Reasoning | |----------|-----------|-----------| | Who owns visual reactivity? | K11 Unity, locally (Path A/F win over C) | Visual latency budget is ~13ms. Remote control adds 20-40ms round trip. Unacceptable. | | Who owns music intelligence? | MotionMix iOS (Path C wins over A/D) | EchelonBridge + SAN + ParamMapper is 5,000+ lines of battle-tested motion-to-music. No port. | | Who coordinates across mesh? | Multicam server :9404 on Mac1 (Path B insight, simplified) | Already exists, alrea

Stage 3: EXPAND + MASTER PLAN

**R1: Radeon 780M Compute Shader Compatibility** - Failure scenario: Unity compute shaders (LumeDepthReproject.compute, LumeOpticalFlow.compute) fail on AMD Vulkan/DX12 drivers. These were developed on Apple Metal (Mac5). - Probability: 20% - Impact: HIGH (no visual pipeline without GPU reprojection) - Mitigation: Test on K11 Day 2. If Vulkan fails, try DX12 backend. If both fail, use LUME format (CPU-reprojected in pointcloud_pub.py) instead of LUMD (GPU-reprojected). Wave 1 graceful-degrade path exists. - Validat

Meta-Evolution Program: Canonical Overview

> This document tells the full story of the meta-evolution program: how disconnected evo-cube research was collapsed into a governed architecture program, why that collapse was necessary, and how the wave-based application model replaced unbounded cube generation. It is the entry point for anyone who needs to understand the program without reading the 8+ source files it synthesizes.

Dream Journal Interpreter

**HEF Evolution:** Instance 37, Generation 8 (V9) **Task:** task_20260202174943_27744c **Previous:** task_20260202170913_a15454 (V8)

Goal Archaeologist

Dig beneath surface-level goals to uncover root motivations. Most people know *what* they want but not *why* they really want it. The Archaeologist excavates through layers of stated desire to find the bedrock motivation underneath.

KARL Architecture

``` Recording -> Scoring -> Analysis -> Training -> Improved Routing ^ | | | +------------- Better trajectories <-------------+ ```

LinkIt — UI System

**Document ID:** LINKIT-ARCH-005 **Version:** 1.0.0 **Last Updated:** 2026-01-15 **Source:** `Desktop/LinkIt/components/`

meshd Wire Protocol — Reference Specification

1. [Overview](#1-overview) 2. [Authentication](#2-authentication) 3. [Slot API](#3-slot-api) 4. [Mesh API](#4-mesh-api) 5. [Slot Lifecycle](#5-slot-lifecycle) 6. [Configuration](#6-configuration) 7. [Topology](#7-topology) 8. [Building a Client](#8-building-a-client) 9. [Error Reference](#9-error-reference) 10. [Future Work](#10-future-work)

Continuation Protocol - Swift iOS Project

**Document ID:** GOV-CONT-001 **Version:** 2.0.0 **Created:** 2026-01-04 **Updated:** 2026-01-13 **Status:** Active **Platform:** iOS (Swift/SwiftUI) **Owner:** Lead Developer

Stable Audio 3

[Technical Report](https://arxiv.org/abs/2605.17991) · [🤗 Models](https://huggingface.co/collections/stabilityai/stable-audio-3) · [🤗 Extra Models](https://huggingface.co/collections/stabilityai/stable-audio-3-extra) · [Discord](https://discord.gg/cKpvjey8b) · [Demo](https://huggingface.co/spaces/stabilityai/stable-audio-3) · [Blog Post](https://stability.ai/news-updates/meet-stable-audio-3-the-model-family-built-for-artistic-experimentation-with-open-weight-models)

N'Ko Acoustic Coding — Featural Acoustic Coding (FAC)

Research code and paper for the N'Ko tone-resolution seam: using acoustic evidence, especially F0, to restore tone marks that a toneless N'Ko ASR pipeline cannot recover from text alone.

N'Ko Publication Readiness and Narrative Plan - 2026-05-03

You can still talk about the 20.57% CER result, but it should be framed as an archived checkpoint anchor, not as a completed May 2026 reproduction and not as proof that all controlled ASR comparisons are closed.

N'Ko Research Program Closeout - 2026-05-03

Stop paid compute now. The Vast instance used for the strict Paper 4 anchor audit was destroyed on 2026-05-03, `vastai show instances` was empty afterward, and the `monitor-nko-anchor-audit` automation was deleted. No further cloud training should be started unless Mohamed explicitly reopens the project later with a full-run budget and artifact-download plan.

N'Ko Brain Scanner — Comprehensive Project Handoff

An ASR (Automatic Speech Recognition) system for N'Ko script — a phonetically transparent writing system used by ~30M Manding-language speakers in West Africa. The core research question: **does N'Ko's phonetic transparency give it a measurable architectural advantage over Latin script in ASR?**

N'Ko as Computational Infrastructure — Program Map

**N'Ko is not a decorative or interchangeable rendering of Manding. For machine-learning systems it is computational infrastructure.** A designed, bijective, tone-marking script changes four things that are usually treated as fixed:

Pulse Plan: NKO Brain Scanner — arXiv Sprint

Workspace document requiring curation.

N'Ko Brain Scanner

A systematic study of how large language models process N'Ko (U+07C0-U+07FF), an alphabetic script used by 40+ million Manding-language speakers in West Africa. We perform activation profiling ("brain scanning"), train a multi-stage adaptation pipeline, build a script-specific BPE tokenizer, implement phonotactically-constrained decoding, and design a retrieval-centric multimodal ASR architecture.

DEP-2 Audit + Chunked Evil Flow Report

**Date:** 2026-02-12 **Protocol:** DEP-2 (Deep Enhancement Protocol v2) **Method:** Chunked Evil Flow (CEF) per module **Pass:** 1 (initial)

DEP-2 Audit + Chunked Evil Flow Report

**Date:** 2026-02-12 **Protocol:** DEP-2 (Deep Enhancement Protocol v2) **Method:** Chunked Evil Flow (CEF) per module **Pass:** 1 (initial)

Stage 4: FORGE — CC-MotionGen V2 Architecture

**CC-MotionGen V2 = Flow Matching DiT + Two-Tier Deployment + Multi-Modal Conditioning + Physics-Grounded Learned Validation + Sensor Capture Flywheel**

Paradox Navigator — Generation 14

Most systems try to **resolve** paradoxes. This one **navigates** them. The tension between opposites is where creativity lives.

DJ Agent - Final Implementation Status

**Core Infrastructure** (10/10) - [x] Base directory structure - [x] Action space with 6 tiers - [x] Scheduler with beat quantization - [x] State shadow (Serato mirror) - [x] Serato bridge (MIDI/keyboard) - [x] Reflex policy (continuous controls) - [x] Planner policy (symbolic actions) - [x] Rewards system - [x] Configuration (dj.yaml) - [x] Runtime integration (engine.py)

GraphQL API Layer — Architecture Document

- **Performance Prophet** → Health metrics streaming, trend-based subscriptions - **Thought Mesh** → Distributed real-time state propagation - **Cross-Script Bridge** → WebSocket-based real-time translation - **Dream Weaver** → Event-driven lifecycle with state transitions

Integration Plan — Migrating to the Unified Agent OS

1. **Additive, not destructive** — New system reads legacy files; legacy systems continue unchanged until verified. 2. **Dual-write, then cutover** — During migration, both old and new storage are written to. Reads shift to new system first, writes follow. 3. **Each phase has a rollback** — If something breaks, revert by pointing back to legacy files. 4. **Test with real data** — Each phase runs against actual `[home-path]`, `[home-path]`, and `[home-path]`.

Dream Research Automation Pipeline — Evolution³

**Generated:** 2026-02-11 **Method:** Evolution³ — three-stage recursive evoflow **Subject:** Full-loop autonomous research triggered by dream evolution

Motion Autocomplete Architecture

Workspace document requiring curation.

Evolution Log — Sigil Composer: Draw gestures to compose N'Ko sigils, learn

**Quality Score:** 0.98 **Files Changed:** src/utils/nkoData.ts, src/components/Canvas.tsx, src/App.tsx, src/App.css, EVOLUTION.md **Commits:** feat(hef): expand N'Ko dataset and implement ghosting guide for muscle memory learning **Artifacts:** Enhanced Sigil Composer, Ghosting Guide, Expanded N'Ko Dataset (9 characters) **Next Suggestion:** Implement multi-stroke character support and a "History" feature to track progress over time.

LinkIt — UI System

**Document ID:** LINKIT-ARCH-005 **Version:** 1.0.0 **Last Updated:** 2026-01-15 **Source:** `Desktop/LinkIt/components/`

System Glossary: Expo Migration

This glossary defines every core term used throughout the Expo migration project. Each definition specifies: - What the term **is** - What it **is not** - What **layer** it belongs to (conceptual, architectural, runtime, data)

SEA-0.4-COMPLETE

Workspace document requiring curation.

SEA-1.4-COMPLETE

``` SKILL.md edit detected (mtime + hash) │ ▼ ┌─────────────────────┐ │ skill_versioner.py │ ← bump_skill() records new version │ versions.json │ saves snapshot for rollback │ snapshots/ │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ skill_watcher.py │ ← reload_skill_embedding() swaps single row │ (daemon or cron) │ in embedding-cache.npz └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ reload-signal.json │ ← signals downstream consumers └──────────┬──────────┘ │ ┌─────┴─────┐ ▼ ▼ tier1_router tier2_s

LinkIt — UI System

**Document ID:** LINKIT-ARCH-005 **Version:** 1.0.0 **Last Updated:** 2026-01-15 **Source:** `Desktop/LinkIt/components/`

ResearchClawBench 2606.07591v1

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cognitive twin research paper

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cognitive twin theorems

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CognitiveTwin V3 COG RLM Documentation

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anticipation pipeline

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audio engine

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comp core architecture

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rag architecture

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system overview

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GRAPH KERNEL PAPER V2

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GRAPH KERNEL RESEARCH PAPER

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graph kernel paper v2

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graph kernel paper

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universal sigil framework

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main — arxiv-submission

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cog rlm paper

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main — latex

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LUME WOOD FABRICATOR PACKET

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LUME WOOD FABRICATOR PACKET.v1 Tform

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LUME WOOD FABRICATOR PACKET

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MoCapAnything V2 End to End Motion Capture for Arbitrary Skeletons

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nko asr paper v2

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nko theorems

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main — nko-acoustic-coding

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fig1 cer comparison

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fig2 compositional generalization

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fig2 loss curves

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fig3 delta

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fig3 vocabulary expansion

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fig4 data scale

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fig5 expF compositional

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fig6 expH vocab expansion

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fig7 djoko quality

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paper canonical nko agp 20cer

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paper2 living speech

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paper4 script advantage

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paper5 deployment

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paper — 01-script-invisibility

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fig1 cer comparison

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paper — 02-phonemic-evaluation

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fig1 cer comparison

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fig2 loss curves

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fig3 delta

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fig4 data scale

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paper — 03-script-native-asr-anchor

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fig5 expF compositional

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fig6 expH vocab expansion

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fig7 djoko quality

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paper — 04-agp-deployment

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Paper4 Script Design Advantage

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Paper4 ScriptAdvantage 297K

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BWB Business Presentation V3 4 Wall System

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VOICE ORDERING SYSTEM DESIGN

The Brews with Beats voice ordering system represents a revolutionary approach to coffee shop queue management and customer experience. By leveraging Apple's latest Speech Analyzer and Voice Processing technologies, we will create an autonomous ordering ecosystem that eliminates traditional lines while optimizing cart routing through intelligent spatial positioning.

Privacy Architecture

**Processing**: 1. Parse conversations into individual prompts with timestamps 2. Strip all named entities (NER pass): names, companies, URLs, emails, phone numbers, addresses 3. Strip code snippets containing credentials, API keys, file paths with usernames 4. Generate embeddings locally (or via privacy-preserving API with no logging) 5. Compute all 6 cognitive metrics locally 6. Cluster prompts into domain topics using embedding similarity 7. Label clusters with generic domain tags (not project-specific names)

Echelon Capture Architecture

``` Left iPhone Right iPhone Apple Watch (optional) │ │ │ └─────────── WebSocket: /visualization?device_id=<role> ───────────┐ │ cc-mcs │ WebSocket: /ws/latent │ Perform tab ```

cc protocol

At the bottom: your body and the sensors. In the middle: LIM-RPS + latent field + a controller model. At the top: Strudel as the musical engine, plus (optionally) a neural texture engine.

The Interview

**The Interview** is TrajectoryOS's conversational AI-driven skill discovery and onboarding flow. It serves as the **primary data ingestion mechanism** for the system, transforming natural conversation into structured skill evidence that powers the Life Physics model.

Documentation Audit - Pass 1: Discovery & Inventory

**Date**: December 21, 2025 **Auditor**: System Analysis **Scope**: Complete cc-trajectory documentation ecosystem **Status**: Pass 1 Complete

12

To make this practical, think of Echelon’s sound engine as a set of **continuous DSP fields** that the latent pushes and pulls, rather than a stack of on/off effects. Each “part of the lexicon” we defined earlier maps to a particular way you sculpt spectra, time, and dynamics.

Graph Kernel Design Document

The Graph Kernel is a **deterministic context construction engine** that transforms raw conversation DAG data into bounded, reproducible context slices suitable for semantic analysis.

cc-graph-kernel

The Graph Kernel transforms a 107K+ turn conversation DAG into deterministic, replayable context slices. It answers one question:

Ecosystem Integration: cc-semantic-language

`cc-semantic-language` is a **TrajectoryOS component** that bridges **embodied motion dynamics** (from Echelon) with **semantic meaning** (for language processing). It implements the **Trajectory-Symbol Alignment Hypothesis**: that the same anticipatory signals that govern motion can govern language semantics.

Gesture Lexicon

A comprehensive catalog of gestures recognized by the Comp-Core motion intelligence system. Each gesture is defined by its kinematic signature, anticipation profile, and semantic meaning.

LinkedIn — The 12-Second Voice Note That Transformed Our Architecture

**Opening Hook:** I'd just finished a three-stage recursive analysis of our monetization strategy. Nine iOS apps. Revenue projections. Pricing tiers. A 72-item execution checklist. Then a 12-second voice note at 2 AM invalidated the entire approach.

Stage 3: EXPAND + MASTER PLAN

**Failure scenario:** The Graph Kernel (Rust, port 8001) crashes or becomes unreachable. All admissibility checks fail. No agent can determine what trajectories it can see. The entire permission model collapses.

Stage 3: EXPAND + MASTER PLAN

**Primary STT: Deepgram Nova-3 (Cloud Streaming)** - Protocol: WebSocket to wss://api.deepgram.com/v1/listen - Parameters: encoding=linear16, sample_rate=16000, channels=1, model=nova-3, language=en, smart_format=true, keywords=["latte:2","cappuccino:2","espresso:2","oat:1.5","almond:1.5","large:1","medium:1","small:1"] - Partial results: interim_results=true (for live preview) - Latency: ~300ms for first partial, ~500ms for stable transcript - Cost: $0.0043/minute. At 500 orders/month, 30s average = $1.08/month. -

Research: Stacks Appchain Architecture -- Deep Technical Analysis

Stacks appchains are independent blockchain instances that use the same protocol, transaction format, and Clarity smart contract language as the main Stacks chain, but mine on top of Stacks (or another appchain) rather than Bitcoin directly. They inherit Bitcoin's security through a hierarchical Proof of Transfer (PoX) chain: Bitcoin -> Stacks -> Appchain. The stacks-core codebase is a large Rust monorepo (~94% Rust, 31K+ commits, 135 contributors, GPL v3) with 12+ crates. Forking it is feasible but non-trivial --

Skill Ecosystem Index (Codex)

Workspace document requiring curation.

E521 — Duncan Fewkes reel analysis digest

- 20 reels ingested (`reels-ingest/{shortcode}/`): - DT-prefix (E500–E521 era): DT0G7SyCtGg E519, DT2tfJzip8A E520, DT6IlEfig-k E521, DTbJvnKCrHO E509, DTBUJiDCvPQ E500, DTEEQNnCvHh E501, DThrF5Miq0- E512, DTmyro9CqUa E514, DTQRPBFiu2v E504, DTr5TQfCpG4 E516 - DS-prefix (E475–E494 era): DSLMOE6iuFH E479, DSA7b7MClVf E475, DScEatyilnk E489, DSDVSJ0itoN E476, DSfZYMUChIi E490, DShj9nlivqf E491, DSkpKC_iie8 E491, DSPfEuWCmTg E483, DSvasHCCiSX E494, DSZmIhOCrTv E488 - 4 Gemini visual analyses successful (DT0G7SyCtGg, D

E567 — Duncan Fewkes reel analysis digest

- 20 reels ingested at `[home]/.openclaw/browser/reels-ingest/{DU,DV}*/` - Episodes covered: **E485 (Pt2 Jason Derulo World Tour), E525-E536, E543-E549, E556-E557, E560, E567-E568, E570** - 5 Gemini visual analyses: DU-52fcinww (E544 Blocky Pinscreen), DUleGWZisrH (E534 Fluid Presets), DV6A04wislr (E568 SuperHot Cubes), DUSui7PioUD (E527 Holovis branded), DUgoDHlipPu (E532 Depth Cubes vs Fluid Presets) — rest rate-limited - All on **HDRP + VFX Graph + Shader Graph** (confirmed every caption)

Stage 4: FORGE — FirstDate Architecture

**Core principle:** The deal IS the episode. A restaurant sponsorship deal produces one episode. An episode fulfills one deal. They share a lifecycle.

TrajectoryOS Desktop Implementation Proposal

**Project**: TrajectoryOS Desktop Version **Date**: December 21, 2025 **Author**: Analysis based on comprehensive codebase exploration **Status**: Proposal

Serenity Soother — Evolution Synthesis

**Evolution ID:** `evo_ss_20260209_synthesis` **Generated:** February 9, 2026 **Techniques Applied:** G01-G20, R01-R18, D01-D16 + 10 Synthesis Methods

Voice Ordering System - Technical Documentation

The BrewsWithBeats voice ordering system uses a hybrid architecture combining: - **iOS 26 SpeechAnalyzer** for on-device transcription - **Semantic embeddings** for accurate menu matching - **Confidence-based clarification** for reliable order capture - **YAML constraints** for menu rule validation

Phase 2: Real-Time & Interview Infrastructure ✅

Phase 2 successfully delivers production-grade **real-time state management, WebSocket infrastructure, and a complete interview system** for TrajectoryOS Desktop.

ORB-2.1: Prometheus /metrics Endpoint

| Metric | Type | Description | |--------|------|-------------| | `orbit_project_count` | Gauge | Total registered projects | | `orbit_active_sessions` | Gauge | Active Claude sessions | | `orbit_websocket_connections` | Gauge | Connected WebSocket clients | | `orbit_api_requests_total` | Counter | API requests (labels: method, path, status) | | `orbit_query_latency_seconds` | Histogram | Query latency in seconds (label: operation) |

Quick Start Guide - Voice Control System

The voice control system now includes an **Auto DJ** feature that automatically mixes tracks with intelligent transitions and effects!

TrajectoryOS - Python-TypeScript Integration Complete ✨

The Python modeling stack has been successfully integrated with the TypeScript backend services. All 6 Python models are now accessible via REST API, with type-safe TypeScript clients handling the communication.

Python ↔ TypeScript Integration Guide

A single FastAPI server exposing all 6 model subsystems: - Skill Graph (Bayesian inference + message passing) - Alignment Scorer - Gravity/Mass Estimator - Life State Dynamics - Echelon Fusion - Scenario Generator & Evaluator

TrajectoryOS - Project Status Report

**What We Have**: Fully functional TypeScript-based life physics engine with web dashboard **What's Missing**: AI-powered features (interview, agents), Python ML models, Echelon integration **Critical Path**: LLM integration for Interview → Background analysis agents → Echelon embodied signals

RAG++ (Retrieval-Augmented Generation Plus Plus)

RAG++ is TrajectoryOS's state-based retrieval and policy recommendation system. Unlike traditional RAG which retrieves "relevant chunks," RAG++ retrieves **successful state transitions** from similar life regimes and recommends actions based on what worked in the past.

TrajectoryOS ↔ CC-TPO Integration Plan

**TrajectoryOS** models life as a dynamical system with escape velocity mechanics - tracking skills, projects, constraints, and computing a "can you escape your current gravity well?" metric.

CC Complete Ecosystem - Final Overview

``` ┌─────────────────────────────────────────────────────────────────┐ │ USER INTERFACES │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌────────────────────┐ │ │ │ cc_ai.py │ │ cc_chat.py │ │ CC Navigator │ │ │ │ │ │ │ │ (Next.js Web UI) │ │ │ │ CLI Search │ │ CLI Chat │ │ - Tree View │ │ │ │ Terminal │ │ Terminal │ │ - Graph View │ │ │ │ │ │ │ │ - Chat + Search │ │ │ │ Fast lookup │ │ GPT-5.1 │ │ - Context Nav │ │ │ │ Free │ │ $0.01/msg │ │ - Breadcr

CC Navigator - Complete Build Summary

**Left Pane:** Hierarchical tree of your 335 conversations - Organized by topics (folders) - Expandable/collapsible navigation - Click to set context

✅ COMPLETE IRCP + TPO INTEGRATION: NO SIMPLIFIED SOLUTIONS

You were absolutely right - the initial implementation was only 513 lines and contained simplified placeholder solutions. I have now created a **complete, mathematically rigorous implementation** with **1,373 lines of full code** and **zero simplified solutions**.

TPO Implementation Summary

I have successfully created a comprehensive implementation of **Topological Preference Optimization (TPO)** - the novel training strategy we developed based on your groundbreaking insight about conversation topology.

TPO Mathematical Supplement: Detailed Formulations and Proofs

**Definition 1.1** (Conversation Graph): A conversation graph $G = (V, E, \mathbf{C}, \mathbf{M})$ where: - $V = \{v_1, v_2, ..., v_n\}$ is the set of message nodes - $E \subseteq V \times V$ is the set of directed edges representing reply relationships - $\mathbf{C}: V \rightarrow \mathbb{R}^5$ maps each node to its DLM coordinates - $\mathbf{M}: V \rightarrow \Sigma^*$ maps each node to its message content

✅ Enhanced TPO System - Complete Implementation

We have successfully **audited and enhanced the entire consolidated TPO system**, ensuring that every component has advanced, production-ready implementations with no placeholders, simplified functions, or stub code.

🔧 Preference Generation Fix Summary

The RCP-enhanced TPO system was generating preference pairs where `chosen` and `rejected` responses were **identical**. This occurred specifically in:

✅ RCP to TPO Migration Complete

We have successfully **deconstructed RCP and consolidated all its best components directly into TPO**, creating a unified, more powerful conversation optimization system.

TPO-RCP Consolidation Summary

We have successfully **deconstructed and consolidated the best of RCP directly into TPO**, creating a unified, more powerful conversation optimization system.

🎯 **Where IRCP + TPO Fits in Model Training Stack**

``` 📚 YOUR DATA (277 conversations, 60K+ messages) ↓ 🧮 IRCP + TPO INTEGRATION ← YOU ARE HERE (advanced_tpo_ircp_bridge.py - 1,373 lines) ↓ 📊 ENHANCED DATASET (17,051 validated preference pairs) ↓ 🎯 MODEL TRAINING (DPO/RLHF/Constitutional AI) ↓ 🤖 PERSONALIZED AI MODEL ↓ 🚀 DEPLOYMENT ```

Hierarchical Semantic Search Engine - Complete Implementation

I've successfully created a focused, advanced hierarchical semantic search engine that combines IRCP embeddings with DLM coordinates for intelligent conversation search. The system is currently processing all 891 Claude conversations for complete precomputation.

Phase 3.2: IRCP Trainer Integration - Completion Report

Phase 3.2 successfully integrates the IRCP training infrastructure with DLM's new data loading system (Phase 3.1). This integration provides a bidirectional adapter layer that allows IRCP trainers to use DLMDataLoader transparently, while maintaining full compatibility with existing IRCP training pipelines.

RCP Production Integration - Phase 1 & 2 Complete ✅

**Problem:** RCP used relative imports (`from system.knowledge_base...`) instead of absolute imports, making it impossible to import RCP from other packages.

RCP Production Readiness Roadmap

**Goal:** Bring the RCP (Ring Contextual Propagation) system from standalone research code to production-ready integration with the DLM system.

DLM Package Refactoring Audit

**Date:** 2025-12-08 **Status:** 🔍 In Progress **Purpose:** Comprehensive audit and refactoring plan to reduce technical debt

Root Folder Cleanup Complete ✅

``` cc-tpo/ ├── README.md # Main README ├── START_HERE.md # Quick start ├── .env # Config ├── package.json # Node config ├── requirements-ircp.txt # Python deps │ ├── docs/ # 📚 All documentation │ ├── README.md # Documentation index │ ├── guides/ # User guides │ ├── architecture/ # Architecture docs │ ├── progress/ # Progress summaries │ ├── refactoring/ # Refactoring docs │ ├── plans/ # Planning docs │ └── summaries/ # Summaries │ ├── scripts/ # 🔧 Executable scripts │ ├── cc_ai.py # Main CC AI CLI │ ├── verify_i

CC AI Pipeline - Final Summary

**Features**: - 🔍 Intelligent Q&A search (prioritizes answers over questions) - 📊 Topic filtering (CC, music, business, ML, etc.) - 🎯 Context retrieval (automatic Q&A pairs) - 📈 Interactive topology visualization

DLM Core - Coordinate System

This module provides the foundational coordinate system that spatially represents conversation structures in a 5-dimensional space. It unifies the original DLM coordinate model with enhanced calculation methods from TPO's RCP system.

Artificial.py Refactoring - Phase 1 Complete ✅

Successfully refactored the monolithic 3,760-line `artificial.py` file into **24 focused, modular files** organized into **7 distinct packages**. This represents approximately **75% completion** of the planned refactoring, with all low-to-medium risk modules extracted and operational.

Root Folder Cleanup Complete ✅

``` cc-tpo/ ├── README.md # Main README ├── START_HERE.md # Quick start ├── .env # Config ├── package.json # Node config ├── requirements-ircp.txt # Python deps │ ├── docs/ # 📚 All documentation │ ├── README.md # Documentation index │ ├── guides/ # User guides │ ├── architecture/ # Architecture docs │ ├── progress/ # Progress summaries │ ├── refactoring/ # Refactoring docs │ ├── plans/ # Planning docs │ └── summaries/ # Summaries │ ├── scripts/ # 🔧 Executable scripts │ ├── cc_ai.py # Main CC AI CLI │ ├── verify_i

Background Worker Service

Asynchronous job processor for TrajectoryOS. Handles periodic tasks like background analysis, skill decay, notification generation, and data aggregation.

RAG++ v0 Evaluation Framework - Complete Implementation

A comprehensive evaluation framework has been built to measure the real-world performance of RAG++ v0 across three critical dimensions: action classification accuracy, recommendation quality, and state-awareness.

Trajectory Core Service

The core service powering TrajectoryOS. Implements the Life Physics model, manages state persistence, and provides REST API for all physics calculations.

Echelon World UI - Production Implementation Guide

Echelon is a **motion-driven, phrase-based generative performance engine** whose temporal structure emerges from embodied latent physics. The UI depicts a *world*, not tools. There are no decks, no crossfaders, no timelines—only a living latent space that responds to the dancer's body.

CC-ECHELON IMPLEMENTATION STATUS

1. **cc-protocol crate structure** - ✅ Cargo.toml with dependencies - ✅ README.md with architecture overview - ✅ lib.rs with module structure - ✅ sensor.rs - Complete `SensorFrame` and `MultiDeviceFrame` types - ✅ coherence.rs - `CoherenceMetrics`, `CouplingMode`, dual-time contract

Option A: Strudel Integration - Implementation Status

**Files to modify**: 1. `apps/echelon-tauri/src-tauri/src/commands.rs` - Add `apply_pattern_edit()` command - Add `get_conductor_status()` command

🎉 Phase 3 Complete: Conductor Integration

``` ┌──────────────┐ │ iPhone/ │ │ Watch/ │ Sensors @ 100 Hz │ AirPods │ └──────┬───────┘ │ ↓ WebSocket ┌──────────────┐ │ cc-mcs │ LIM-RPS latent physics │ (Backend) │ Computes tension, velocity, coherence └──────┬───────┘ │ ↓ Polling (50 Hz) ┌──────────────┐ │ echelon- │ │ tauri │ │ (Desktop) │ └──────┬───────┘ │ ├──────────────────┐ ↓ ↓ ┌──────────────┐ ┌──────────────┐ │ Audio │ │ Conductor │ ⭐ PHASE 3 │ Engine │ │ Thread │ └──────────────┘ └──────┬───────┘ │ ↓ Section logic ┌──────────────┐ │ PatternEdit │ Set

CC-PROTOCOL IMPLEMENTATION PROGRESS

- **Total Lines:** ~2,500+ - **Total Tests:** 51+ - **Core Types:** 15+ - **Enums:** 6 - **Test Coverage:** All core paths covered

Code Review: StemDeck — LUME Stage 1 (sample-locked 4-stem playback)

> Meta-review (6-pass parallel + adversarial contrarian round) of commit `c3559889` > on branch `feat/femto-only-bar`, repo `Comp-Core`. > Reviewed files (NEW code only): > - `core/audio-media/cc-echelon/crates/audio-engine/src/stem_deck.rs` (963 lines) > - `core/audio-media/cc-echelon/crates/audio-engine/examples/stem_deck.rs` (167 lines) > Surrounding crate (`loader.rs`, `param_control.rs`, `fx.rs`, `lib.rs`) read for primitive-usage judgement only; findings are scoped to StemDeck.

Phrase-Conditioned Spectrogram Diffusion System

This module implements a diffusion-based generative audio system that learns from your music library and generates new audio conditioned on: 1. **Phrase embeddings** from your existing tracks 2. **Motion embeddings** from your body movement

CC Window Aligner: Implementation Checklist

| Status | Meaning | |--------|---------| | `[ ]` | Not Started | | `[~]` | In Progress | | `[B]` | Blocked (see notes) | | `[x]` | Complete |

CC Window Aligner: Implementation Guide

| Decision Type | Required Signals | Authority | |---------------|------------------|-----------| | **Type/Schema changes** | Charter + Invariants + Downstream impact | Lock requires review | | **Algorithm changes** | Invariants preserved + Tests pass | Implementation owner | | **Configuration defaults** | Performance + Safety tradeoff | Implementation owner | | **New device support** | DIR-003 compatibility + No breaking changes | Requires integration test |

Python (rag_plusplus.slice)

1. [Overview](#1-overview) 2. [Architecture](#2-architecture) 3. [Core Concepts](#3-core-concepts) 4. [Graph Kernel Service (Rust)](#4-graph-kernel-service-rust) 5. [RAG++ Slice Client (Python)](#5-rag-slice-client-python) 6. [Slice-Conditioned Retrieval](#6-slice-conditioned-retrieval) 7. [Provenance Chain](#7-provenance-chain) 8. [Production Hardening](#8-production-hardening) 9. [API Reference](#9-api-reference) 10. [Deployment](#10-deployment) 11. [Testing](#11-testing) 12. [Migration Guide](#12-migration-guide

N'Ko Inscription System - Phase 2 Implementation Report

Phase 2 transforms cc-inscription from a foundational type system into a **living discipline** with: - Rigorous basin lifecycle management (split/merge with cryptographic provenance) - Graph Kernel governance for ontology operations - RAG++ as laboratory assistant for predictability evaluation - Lexicon version chain traversal and reinterpretation layer - Information-theoretic phrase emergence

Shared State Schema

This document defines the shared state format for agent coordination, including access patterns, locking mechanisms, and event bus design.

Anticipation Pipeline Diagram

```mermaid flowchart TB subgraph Input [Input: MotionWindow] MW[MotionWindow<br/>50 frames @ 50Hz] SF[SkeletonFrames<br/>27 bones × 50 frames] LF[LatentFrames<br/>Optional embeddings] Coverage[Coverage Check<br/>≥ 0.9 required] end

Codex Handoff: Partial-Real Thunder Train Lane

This handoff is for one bounded job only: get the first completed partial-real local Thunder training window to finish cleanly and verify that it writes a real checkpoint. Do not expand scope beyond that.

AGP / MLX / ANE Training Spec

The first trainable version of AGP is not a new foundation model. It is a `Gemma 4 E2B` decoder transformer wrapped in a small set of new trainable interfaces. The objective is to teach the system four things at once: how to speak in your distribution, how to estimate whether an intermediate state is alive enough to trust, how to project that state into a typed semantic layer, and how to compress or route that state without paying for full-depth inference every time. The right starting point is therefore not full-m

AGP-MLX for N'Ko Anticipation Partitioning

Current status remains `NO-GO` until the learned live proposal path clears that gate on the authoritative five-run post-TTT corpus.

AGP N'Ko Live Corrector Release Gate V1

2.1.3 The evaluation corpus must contain real ASR predictions and real references from the same provenance as the deployment path.

AGP Supervised Runtime Envelope

Both are now supervised by `runtime/supervise_agp_server_v1.sh`, and the client transport path is hardened by reconnect and retry logic in `runtime/run_cross_host_packet_replay_v1.py`.

AGP TurboQuant + Apple Neural Engine Performance Plan

This plan turns the local TurboQuant and Apple Neural Engine research into an executable AGP performance lane. The goal is not to add accelerator names to the paper. The goal is to prove which parts of AGP become faster, smaller, or more energy efficient when the system uses the right engine for the right class of computation.

Mixture of Anticipatory Orthogonal Experts for N'Ko ASR

The MoVE paper is directionally useful because it demonstrates a disciplined way to avoid a monolithic speech model: keep a pretrained audio-language model fixed, train specialized LoRA experts, and learn a router that blends or selects those experts by speech-token state. The useful idea is not the exact vocalization target. Their target is expressive speech-to-speech translation with emotion and non-verbal vocalization preservation. Our target is N'Ko ASR correction with acoustic authority, graph admissibility, i

Generative Output Overview

Generative output is the part of the system that turns body state into sound, visuals, camera decisions, DJ control, and inscriptions.

Path F: Live Performance Optimized -- Minimize Latency at All Costs

Every design decision is filtered through one question: does this add latency? If yes, reject it. Direct UDP everywhere. No middleware, no brokers, no HTTP coordination. Publishers bind [ip] and consumers hardcode IPs. The system is static: every connection is pre-configured at deploy time. No dynamic discovery, no subscriptions, no health checks. The absolute minimum number of network hops between any sensor and any output. Where possible, merge publisher and consumer onto the same process.

Stage 0: Research — Voice-First Agent Architecture

The mesh currently has 8 distinct voice subsystems spread across iOS apps, macOS services, and backend flows. They are architecturally isolated — no subsystem talks to another. The terminal agents (Claude panes, Prefect flows, Discord bots) communicate exclusively through text. Voice exists at the edge (phone, glasses) but doesn't penetrate the mesh core.

Omi Desktop App — Flows & Exploration

This skill teaches you the Omi desktop macOS app's navigation structure, screen architecture, and SwiftUI patterns. Use it when developing features (to understand how the app works), fixing bugs (to navigate to the affected screen), or verifying changes (to confirm your code works in the live app).

Dance Debugger 💃🐛

Every bug tells a story through movement: - **Null pointer** → The Vanishing Partner (reaching for someone who isn't there) - **Infinite loop** → The Endless Waltz (spinning forever, never advancing) - **Race condition** → The Collision Tango (two dancers fighting for the same spot) - **Memory leak** → The Growing Ensemble (dancers keep joining, none leave) - **Stack overflow** → The Tower of Lifts (stacking lifts until collapse)

Enriched Spawn — Context-Aware Sub-Agent Orchestration

When using `sessions_spawn`, sub-agents receive only the raw task text. They don't get: - AGENTS.md (behavioral guidelines) - SOUL.md (personality/values) - MEMORY.md (historical context) - Skills (specialized knowledge) - Governance (checklists, standards) - Tool guidance (how to use tools properly)

Governance Framework

**Anticipation Over Prediction** — Don't predict outcomes; detect when futures become constrained enough that action is warranted.

Voice Memory Palace 🏰

Navigate your memories spatially using voice commands. Organize memories into **rooms**, traverse **hallways** of time, and discover forgotten treasures through intuitive spatial metaphors.

Prompt Synthesizer v3 — Bidirectional Context Expansion + Dream Integration

> Not just for raw speech. For ANY input — even "continue" becomes full state restoration + next step derivation. > Now with dream seed extraction, skill chaining, and project routing.

Pulse v2 — Governance-Aware Autonomous Development

Pulse v2 enhances autonomous development sessions with: - **Governance Integration** — Reads and updates project checklists - **Skill Loading** — References relevant SKILL.md files - **Context Injection** — Pulls memories from Orbit/MCP - **Conventional Commits** — Standardized commit messages - **Pattern Compliance** — Matches existing code style

NKo — Notation, Memory, and Future Routing

NKo is the durable naming and inscription layer for movement phrases. This is the immediate role, and it should be built before anything else.

Motion Mix Live Director Mac4 Camera 7/8/9 Handoff

This handoff is for the agent integrating the three new Mac4 camera lanes into Motion Mix Live Director and the Motion Mix Wall. No Live Director application code was changed in this pass.

LUME Current Print Approval Queue

Status update, 2026-05-20: the large printed outer shell and pod path is paused while the wood enclosure prototype is evaluated. Use `wood/LUME_WOOD_ENCLOSURE_SPEC.md` for the current wood-body direction and `print/WOOD_V2_PRINT_QUEUE.md` for the active 3D-print queue.

LUME v2 twin-sensor checklist

_Comprehensive task list spanning hardware, CAD, software, calibration, testing, packaging, manuals, and ship-blocking dependencies. Ordered by unblock chain. Each item has owner, ETA, and verification criterion._

E579 — Duncan Fewkes reel analysis digest

- Cached MP4s: `[home]/.openclaw/browser/reels-ingest/{DU,DV}*/` - Caption JSON: same dir, `*.info.json` - Gemini visual analyses: `[home-path]` - Prior playbook (E579-E606): `lume-duncan-playbook.md` - Master URL list: `/tmp/duncan_reels.txt`

LUME

Embodied-light visual engine. Multi-machine pipeline that drives a 1920×440 bar display from real-time sensor input — depth, audio, motion — with a music-generation feedback loop.

Session State

Progress: - K11 is the active compute path; Jetson-era internals are legacy. - ZHAOCAILIN 11.3 inch display is modeled as a top cradle with a cable pass-through, not a structural top cutout. - Arducam IMX586 USB camera is modeled in the right-front bay with a new printable mount. - Approval renders are in `hardware/cad/renders/approval/`. - Validation STLs are in `hardware/cad/exports/approval-v2/`. - Symmetric two-Arducam variant is available with `ARDUCAM_LAYOUT="dual"`, mockups in `hardware/cad/renders/approval/

LUME Rehearsal Capture Overwhelming Update

Date: 2026-05-28 Primary live host: Mac4 Primary storage target: K11 Reference project root on Mac4: `[home]/dev/lume-commerce-live/viz/lume-pcloud` Document destination on Mac1: `[home]/Desktop/lume-commerce/viz/lume-pcloud/Docs/LUME_REHEARSAL_CAPTURE_OVERWHELMING_UPDATE_2026-05-28.md`

Stillness Catalog — Content System Derived from @nouses_kou

Where @nouses_kou inscribes the void in Noh-Japan-AR, you inscribe N'Ko-Mande-Sensing. Same architecture, different ancestry.

CODEX HANDOFF — Claude Design Rendering for LUME VC Pitch

> Self-contained brief. You (Codex) take over the Claude Design rendering track. You pick the automation path. This doc gives you the WHAT — the product, the inputs, the outputs, the acceptance criteria. The HOW is yours.

Operations Hub — iOS Implementation Spec

The web CRM already has two Operations Hub views consolidating all outreach intelligence for a market into a 7-tab surface. The iOS app has the foundation (InboundCommandView, MarketSweepCommandView, InboundService, FieldRoutes) — all connected to the same Supabase. This spec describes the **reorganization + two new hub views** needed to match the web architecture. This is not a ground-up build.

Motion Autocomplete - System Capabilities Report

Motion Autocomplete is a sophisticated AI system that predicts physical movements and prepares context before actions occur. The system has evolved through 8 generations, with the current implementation featuring:

Insta360 + K11/LUME Custom Camera Runbook

This runbook turns the Insta360 from a passive stream into a programmable MotionMix/LUME source. The current production path uses MotionMixApp on iOS to connect to the Insta360 SDK, extract virtual camera crops from the 360 preview, run Vision pose detection, and publish LUME-compatible UDP JSON to K11 on `[ip]:9705`.

LumeBodyTruth Contract

`LumeBodyTruth` is the perception authority above the camera/sensor feeds. It answers one question for every consumer: "what do we trust about the performer right now?"

MotionMix Unified Director — Agent Handoff

// After: adaptive — performer phase OR metronome fallback poseBarCounter += 1 let echelonBarFired = echelonBridge.shouldFireBar() let fixedBarFired = poseBarCounter % 30 == 0 guard (echelonBarFired || fixedBarFired), let hub else { return } ```

Featural Acoustic Coding with N'Ko — Program Documentation

This document is the dense, self-contained account of a research direction that began as a reaction to a single paper and turned into a structural extension of an existing N'Ko language-technology program. The reaction paper is Lexical Acoustic Coding, abbreviated LAC, which proposes that a short sound can be transmitted between two language-model agents as a readable English sentence and then re-rendered from that sentence, trading exact sample recovery for perceptual similarity. The reaction was that natural Engl

AGP/N'Ko + Vast Training Handoff

You are continuing the N'Ko ASR training and AGP corrective-adapter program. The goal is not just to run more jobs; the goal is to finish the matched experimental bundle cleanly enough that the papers can make defensible claims about N'Ko script advantage, trajectory conditioning, TAR, and TTT.

N'Ko ASR Row Contract

This is the concrete row-level contract that the Vast ASR exporter should emit so the local uncertainty-packet pipeline can consume it without ad hoc parsing.

Edit-Candidate Ranker Report

| condition | CER | delta pp | changed | better/same/worse | |---|---:|---:|---:|---:| | baseline | 0.3421 | +0.00 | 0 | 0/0/0 | | oracle_any | 0.2832 | -5.89 | 274 | 274/0/0 | | ranker | 0.2957 | -4.63 | 274 | 255/19/0 | | ranker_preserve | 0.3199 | -2.22 | 140 | 124/16/0 |

Ranker Serving Handoff — ANE/TurboQuant Wraps the Tiny Ranker, Not Gemma

```text anchor ASR hyp + logits/self-score -> deterministic bounded candidates -> frozen logistic candidate ranker -> calibrated mode threshold -> deterministic corrected text ```

N'Ko Speech Stack — System Architecture Ledger

This file prevents the program from looking like a sequence of overwritten ideas. The work has not been reset; it has compressed into layers. Each experiment either became a system component, a constraint, or a publishable negative result.

Whisper Encoder / Feature Path Audit

**Date:** 2026-06-02 **Scope:** Determine whether the current workspace already contains a reusable CoreML Whisper encoder / ANE feature extraction path for the clean anchor ASR serving stack.

Codex Handoff: N'Ko ASR Vast.ai Pipeline Monitor

**Created:** 2026-04-04 14:50 UTC **Author:** Claude (Mac1 bottom-right pane) **For:** Codex agent taking over monitoring duties

N'Ko Research Paper Roadmap

Six papers forming a coherent research arc: from diagnosing why AI fails on N'Ko, to building systems that work, to proving structural advantages, to connecting language identity with personalized AI and blockchain provenance.

Assumptions and Invariants Ledger

1. Assumptions 1.1 A-1 GEMINI_API_KEY is set and valid. 1.1.1 If false: OCR requests fail and no detections are produced. 1.1.2 Detection: API errors or authentication failures in logs. 1.2 A-2 yt-dlp and ffmpeg are installed and available on PATH. 1.2.1 If false: Downloads or frame extraction fail. 1.2.2 Detection: Command not found or subprocess failures. 1.3 A-3 YouTube access is available or mitigated via HLS/cookies. 1.3.1 If false: Video downloads fail with 403 or similar errors. 1.3.2 Detection: yt-dlp error

Documentation Audit Pass 5: Tauri Desktop Integration

**Date**: December 21, 2025 **Focus**: Enhanced Tauri documentation and created production-ready implementation starter **Status**: ✅ **Complete** - All 5 tasks finished

Fine-tune Wav2Vec2 for DJ Commands - Complete Guide

**Why this approach?** - ✅ **Speed**: <100ms total latency (acceptable for DJing) - ✅ **Accuracy**: Fine-tuned ASR + semantic retrieval = best of both worlds - ✅ **Flexibility**: Can add new commands without retraining audio model - ✅ **Debugging**: Can see transcribed text - ✅ **Practical**: Uses pre-trained models with fine-tuning

Voice Control Systems - Complete Guide

| Need | System | Command | |------|--------|---------| | **Lowest latency** (internet OK) | Gemini Live | `./START_REKORDBOX_VOICE_GEMINI.sh` | | **Highest accuracy** (offline) | Whisper | `./START_REKORDBOX_VOICE_WHISPER.sh` | | **Best long-term** (self-improving) | **Hybrid** ⭐ | `./START_REKORDBOX_VOICE_HYBRID.sh` |

S2O vs ASR+Retrieval - Technical Deep Dive

3. **CLAP** (Contrastive Language-Audio Pretraining): Audio → Audio Embedding - ✅ Pre-trained - ❌ Audio embeddings are for **sounds** (music, environmental sounds) - ❌ Not trained on **speech commands**

Tier 3: Medium-Term Architectural Enhancements - Final Summary

**Implementation:** - `state/state_snapshot.py` (210 lines) - Immutable state snapshots - `state/history_manager.py` (250 lines) - Ring buffer with undo/redo - `state/undo_handler.py` (300 lines) - Command parsing & inverse generation

Tier 3: Medium-Term Architectural Enhancements - Progress Summary

Tier 3 introduces **advanced architectural features** that make the voice control system more robust, intelligent, and production-ready.

Echelon Phase 3 Detailed To-Do List

- [ ] **Whisper-rs Integration** (Phase 3) - [ ] Download Whisper model files - [ ] Complete voice recognizer implementation - [ ] Test voice recognition accuracy - [ ] Optimize for real-time performance

visualize

If you want the audience—and yourself—to feel the shape of Echelon’s inner life as you play, you have to turn an invisible vector (x_t^*\in\mathbb{R}^D) into a picture that moves with the same inevitability as the music. The challenge is not just to plot numbers; it’s to build a visual grammar where geometry equals meaning, where distance means “more different,” curvature means “changing intention,” thickness means “tension,” and motion means “you.” The trick is to choose a projection that preserves the two invaria

Sensor Fusion Implementation Specification

This document provides detailed implementation specifications for the Mocopi + MediaPipe sensor fusion system. It includes code scaffolds, API contracts, and integration points with existing Comp-Core infrastructure.

Evolution Log — Motion Autocomplete: AI predicts your next physical movement

**Goal:** Evolve Gen 6 heuristic prototype into a runnable multimodal prediction stack with testable orchestration behavior.

Evolution Log — Weighted Dance Style: Buff Barista

**Quality Score:** 0.95 **Files Changed:** EVOLUTION.md, GEMINI.md, README.md, src/buff_barista/barista_moves.py, src/buff_barista/main.py, src/buff_barista/motion_physics.py, src/buff_barista/signature_style.md, src/buff_barista/train_motion_model.py **Commits:** feat(hef): implement buff barista weighted dance style Gen 7 **Artifacts:** src/buff_barista/main.py, src/buff_barista/motion_physics.py, src/buff_barista/barista_moves.py, src/buff_barista/signature_style.md **Next Suggestion:** Explore 3D visualization

Optimal Cognitive Twin Training Configuration

> Target: Qwen2.5-7B-Instruct-4bit on Mac5 (M4 16GB) via MLX LoRA > Dataset: 2,923 train / 328 valid examples of Mohamed's responses > Goal: Override "helpful assistant" persona with Mohamed's direct, action-oriented voice > Date: 2026-03-23 > Extends: lora-persona-research.md (2026-03-22)

Bambara ASR Integration

This integration uses the **RobotsMali NVIDIA NeMo models** for Bambara automatic speech recognition, complementing our English↔Bambara translation system.

AGP/N'Ko + Vast Training Handoff

You are continuing the N'Ko ASR training and AGP corrective-adapter program. The goal is not just to run more jobs; the goal is to finish the matched experimental bundle cleanly enough that the papers can make defensible claims about N'Ko script advantage, trajectory conditioning, TAR, and TTT.

Pulse Chain System — Living Documentation

A Pulse Chain is a **self-orchestrating multi-agent pipeline** that uses Discord as its execution spine. Work decomposes into **waves** (phases), each wave runs **parallel agents**, and completion of one wave auto-triggers the next.

Self-Referential Context Penalization for RAG++ Context Gateway

That creates a failure mode: - the gateway retrieves chunks that are semantically relevant but already present in the prompt - those chunks consume scarce tokens without adding novelty - when the top results are all self-referential, the gateway amplifies echo instead of expanding the reasoning surface

Serenity Soother — System Glossary

This glossary defines every core term used in the Serenity Soother project. Each definition includes: - **What it is**: Precise definition - **What it is not**: Explicit exclusions to prevent confusion - **Layer**: Where this concept exists (Conceptual, Architectural, Runtime, Data, UI)

SEA Phase 0 — MiniMax Scoring Latency Benchmark Results

**Date:** 2026-02-17 23:30 (re-run with live endpoint) **Endpoint:** `http://localhost:18080` **Model:** MiniMax-M2.5 (229B params, TQ1_0 GGUF quantization, 55GB) **Server:** llama.cpp (llamacpp) **Prompt Template:** Tier 2 (Skill Activation Judge) **Benchmark Script:** `benchmark_minimax_scoring.py`

PPTX creation, editing, and analysis

A user may ask you to create, edit, or analyze the contents of a .pptx file. A .pptx file is essentially a ZIP archive containing XML files and other resources that you can read or edit. You have different tools and workflows available for different tasks.

Skill Creator

Skills are modular, self-contained packages that extend Claude's capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform Claude from a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.

cc-anticipation: Continuation Protocol

This document defines the protocol for continuing work on cc-anticipation across sessions, ensuring consistent context restoration and preventing drift from canonical specifications.

Echelon Integration Plan

**Status**: 🔮 **Planned Q1-Q2 2026** - This document describes a planned integration with Echelon (Computational Choreography engine).

TrajectoryOS Architecture

TrajectoryOS is a life-trajectory modeling system composed of cooperating services that infer your skills, alignment, constraints, and escape velocity through continuous interrogation and artifact analysis.

Life Physics Model

TrajectoryOS models your life as a **dynamical system** with physics-inspired mechanics. Unlike traditional productivity tools that track tasks or goals, we model the underlying forces that determine whether you're stuck in a gravity well or escaping toward freedom.

Documentation Audit - Pass 3: Consolidation & Labeling

**Pass 3 Goal**: Add implementation status markers, create critical missing docs, fix broken references, and standardize language to eliminate confusion between current capabilities and future vision.

cc-anticipation: Continuation Protocol

This document defines the protocol for continuing work on cc-anticipation across sessions, ensuring consistent context restoration and preventing drift from canonical specifications.

Pulse Protocol — Autonomous Loop Architecture

> Multi-iteration autonomous development sessions with signal control, context chaining, and evidence-tracked completion.

CRP-3.1: Figures and Diagrams — COMPLETE

Workspace document requiring curation.

Distributed Mesh — Overview

The system could have been built with a central server: one Mac that all iPhones connect to, coordinating all decisions. That was the original architecture (the multicam-server at `:9404`).

When AI Can't See Your Language

In 1949, a man named Solomana Kante sat down in Kankan, Guinea, and did something that most linguists said was impossible.

Instagram Post — HomeLab Architecture v1

→ Mac1: Control plane, gateway, monitoring → Mac3: K3s, Prefect, Postgres, Graph Kernel → Mac4: Ollama, AI agent workers, Adobe pipeline

LinkedIn Post — HomeLab Architecture v1

Tonight I locked in the architecture for what started as "a few scripts" and evolved into a genuine distributed system running across three Mac machines.

Substack Article — HomeLab Architecture v1

At 11:30 PM on a Tuesday, I drew an ASCII diagram in a Discord channel and realized I wasn't running a collection of scripts anymore. I was running infrastructure.

TikTok Script — HomeLab Architecture v1

"So I built actual infrastructure: - K3s for container orchestration - Prefect for workflow management - Tailscale connecting all three Macs - Prometheus watching everything

TikTok: Patience Is Architecture

"That's not how I learned it. Patience is active. Patience is planting a tree you won't sit under. Building a system that won't pay off for years. Writing in a language the tech world hasn't noticed yet."

Twitter Thread — HomeLab Architecture v1

Mac1 (Control Plane) ← Tailscale → Mac3 (Infra) ← Tailscale → Mac4 (Compute) Clawdbot GW K3s + Prefect Ollama + Agents kubectl/k9s Graph Kernel Adobe Pipeline

HANDOFF: Mac4 Unity Takeover for Codex

Date: 2026-04-04 Authoring machine: `[home]` on Mac1 Target machine: Mac4 Primary target project: `[home-path]` on Mac4 Companion reference project: `Desktop/DepthReactiveVisuals/` on Mac1

discrawl Spec

- build a local-first Discord guild crawler - mirror all guild data the configured bot can access - store it in SQLite - support fast text search, semantic search, and raw SQL - support one-shot backfill and long-running live sync

KARL Integration — Evolution³ / Stage 1: PATH D

Abandon regex-based skill routing entirely. Embed every skill and every incoming prompt into a shared vector space. When a new prompt arrives, find the nearest skill by **trajectory-weighted similarity** — not raw text overlap. The "learning" is not RL on model weights (that is KARL's full treatment). It is RL on the **routing layer itself**. Skills stay as SKILL.md markdown. The only thing that changes is which skill gets injected, and that decision is made by a vector space whose distances are continuously update

KARL Integration -- Evolution3 / Stage 2: COMPOUND

Stage 0 established that the Cortex pipeline operates on **prompt text only** (Section 1, core limitation). It has zero visibility into tool sequences, exit codes, file diffs, or task outcomes. All five Stage 1 paths converged on the same foundational requirement: a structured trajectory record that captures what actually happened during a session, not just what the user asked for.

KARL Integration -- Evolution3 / Stage 3: EXPAND + MASTER PLAN

| Assumption (from Stage 2) | Reality | Impact | |---|---|---| | unified.jsonl has 3,909 entries with tool_calls arrays | **3,940 entries, but only 3 have populated tool_calls** (all empty arrays) | **CRITICAL**: The unified store does NOT contain usable trajectory data | | verbose-all.jsonl has 3,249 entries, 157 with multi-step tool sequences | **3,258 entries, 157 with tool_calls in assistant_turns** (confirmed) | Correct, but these are 96% Codex entries (`exec_command`, `shell_command`), not Claude Code entries

Homelab Integration Architecture

This document maps the full integration architecture of the homelab project, detailing how components like Codex, Clawdbot, Pulse, and Noosphere connect to form an autonomous development and communication ecosystem.

Message Orchestrator v1 — Hybrid Routing Intelligence

> _"Every message is a signal. Some are commands, some are dreams, some are the start of something bigger. This skill listens to all of them."_

Uncertainty Embracer

Activate when: - Questions have no clear answer - Multiple valid interpretations exist - Predictions are requested about uncertain futures - User seems frustrated by ambiguity - Decision paralysis from too many unknowns

Architecture Spine

``` Leisure(t) = (Captain ⊕ Workers ⊕ Sensors) ↦ Phone │ governed by EW invariants │ surfaced through │ ControlOps · DataStreams · Notifications · Widgets · Voice · DeepLinks ```

Sales Agent Architecture — V1 through V2

The Market Sweep Agent is an automated sales pipeline that discovers coffee shops across US markets, enriches contact information, generates AI-personalized emails, and tracks responses — all from a single dashboard.

Motion Lexicon — MotionMix Visual Language Architecture

| Output | Gesture Layer | Emotion Layer | Temporal Layer | Ratio Layer | |--------|--------------|---------------|----------------|-------------| | Particles (browser) | Preset swap | Color + bloom | Lerp transitions | Attractor positions | | TouchDesigner (Mac2) | Scene switch | Color palette | Cross-fade timing | CHOP parameters | | Remotion (React) | Composition swap | Theme props | Sequence timing | Transform params | | Adobe Premiere (Mac4) | Clip selection | LUT/grade | Transition duration | Crop/scale |

Current System

- Runs Rekordbox. - Runs Pose Coach / camera viewer. - Runs AirDeck bridge. - Owns keyboard/MIDI dispatch. - Owns the final safety gate. - Can work from camera pose without Sony sensors.

Prompt Guide

This guide provides tips and tricks for getting the most out of your prompts, and helps you break out of prompt "writer's block." Prompting is more of an art than a science, but there are some best practices we've discovered. This is just a starting point, be creative and go crazy (perhaps share in our [Harmonai Discord](https://discord.gg/cKpvjey8b))!

When AI Can't See Your Language

In 1949, a man named Solomana Kante sat down in Kankan, Guinea, and did something that most linguists said was impossible.

The Script Machines Can't Read

I brain-scanned three AI models. All of them are blind to my family's writing system. Here's what I found, why it matters, and what I built to fix it.

N'Ko Research Papers

The public closeout series now lives under `final/`. Each paper has its own folder with a local `paper.tex`, compiled `paper.pdf`, `references.bib`, `paper.bbl`, and relative `figures/` assets, so each manuscript can compile from its own directory.

Stage 4: FORGE — Enhanced CC Architecture (Production-Grade)

Day 1-2: B (single phone works) + D (sound upgrade) + E (training starts) Day 3-5: A (Mocopi + Direct Trigger) Week 2: C (multi-cam content pipeline) Month 2: F (live performance) ```

discrawl Spec

- build a local-first Discord guild crawler - mirror all guild data the configured bot can access - store it in SQLite - support fast text search, semantic search, and raw SQL - support one-shot backfill and long-running live sync

Mapping System Update - Complete Guide

The Rekordbox voice control system has been updated with comprehensive command mappings from the full Rekordbox keyboard shortcut catalog. This update includes **450 commands** covering all major DJ operations across both decks.

EchelonCapture: Current State vs. Vision

**EchelonCapture is currently a sensor data streaming & recording app.** **It needs to become the visual performance dashboard for Computational Choreography.**

Architecture: Idea Vault (Gen 7)

Workspace document requiring curation.

Multi-Horizon Agent Architecture — Operational Tracker

> Each horizon operates autonomously. This file tracks goals, active tasks, and status across all four business horizons.

cc-anticipation: Living Implementation Checklist

**Document Version**: 0.1.0 **Created**: 2025-12-26 **Status**: Phases 1-9 COMPLETE (v0 core + Python + Neighbors + Replay + Dashboard) **Parent**: [PROJECT_CHARTER.md](./PROJECT_CHARTER.md)

Anticipation Geometry

[](LICENSE) [](https://www.rust-lang.org/) [](https://www.python.org/)

DEP Report — Cognitive Twin Pipeline + MiniMax Fleet Integration

| Category | Score | Weight | Weighted | |----------|-------|--------|----------| | Structure | 7 | 1.0 | 7.0 | | Compilation | 8 | 1.5 | 12.0 | | Integration | 6 | 1.5 | 9.0 | | Content | 7 | 1.0 | 7.0 | | User Journey | 5 | 1.0 | 5.0 | | Deployment | 5 | 1.0 | 5.0 | | **Total** | | **7.0** | **45.0 / 70 = 64.3%** |

Gesture Control System - Production

**Enterprise-grade multi-modal gesture recognition** combining phone sensors and video analysis for expressive DJ control.

Deployment Guide

This guide covers deploying TrajectoryOS to production. We'll use **Fly.io** as the primary example, but principles apply to other platforms (AWS, Railway, etc.).

Phase 3.4: End-to-End Pipeline - Completion Report

Phase 3.4 implements the complete end-to-end training pipeline for DLM coordinates. This phase provides orchestration infrastructure that ties together data loading (Phase 3.1), IRCP integration (Phase 3.2), and evaluation metrics (Phase 3.3) into a production-ready training system.

RCP Visualization System - Implementation Summary

Successfully implemented and deployed a comprehensive visualization system for the Ring Contextual Propagation (RCP) framework, integrating complex topological analysis with real conversation data from a database of 277 conversations.

Scripts Directory

This directory contains utility scripts, training scripts, demos, setup tools, and testing utilities for the CC-TPO project.

cc-agent-ipc

This crate provides an async Rust interface to communicate with the `cc-agent-service` Python daemon over Unix sockets. It enables Rust applications (Tauri, CLI tools, backend services) to leverage multiple LLM providers:

cc-anticipation: Living Implementation Checklist

**Document Version**: 0.1.0 **Created**: 2025-12-26 **Status**: Phases 1-9 COMPLETE (v0 core + Python + Neighbors + Replay + Dashboard) **Parent**: [PROJECT_CHARTER.md](./PROJECT_CHARTER.md)

N'Ko Inscription System - Implementation Summary

The `cc-inscription` crate provides a complete foundation for compiling embodied dynamics (z-trajectory) into justified N'Ko statements with cryptographic provenance.

Experimental Protocol: Trajectory-Symbol Alignment Hypothesis

> There exists a finite operator alphabet and legality grammar such that semantic meaning, defined as invariant latent effect across stratified contexts, can be constructed, promoted, deprecated, and recomposed without reference to pre-existing natural language tokens, and such that this meaning remains stable under controlled perturbation.

Experimental Protocol: Trajectory-Symbol Alignment Hypothesis

> There exists a finite operator alphabet and legality grammar such that semantic meaning, defined as invariant latent effect across stratified contexts, can be constructed, promoted, deprecated, and recomposed without reference to pre-existing natural language tokens, and such that this meaning remains stable under controlled perturbation.

N'Ko Sigil Components

// With options <Sigil type="stabilization" size={64} color="#8b5cf6" animated showLabel interactive onClick={(type) => console.log(type)} /> ```

Full Stack Map

This map uses source-verified component names. It intentionally avoids old summary claims such as "SAN 135K", "5,408 pairs", "validation loss 0.028", or "LIM-RPS is the production brain" unless a source file proves them.

Photography — Interval Capture and StageView Console

Photography is the third generative output of the system. Unlike music (continuous) and visuals (continuous), photography is discrete: the system captures moments.

Claude Code Doc Review - 2026-05-27

The user's correction was right. Several generated docs treated intended design language as if it were verified runtime behavior. The source supports a strong architecture, but the language must stay precise:

Stage 0: RESEARCH — AutoMesh Self-Healing Architecture

``` [ROOT] AutoMesh Self-Healing ├── [D1] Code-Level Healing (self-healing-code/) │ ├── healer.py (1580 lines, Gen 6-7) │ │ ├── Wound/Antibody/ImmuneMemory (SQLite, reactive) │ │ ├── HealingStrategies (5: null_coalesce, type_coerce, key_fuzzy, index_bounds, retry_backoff) │ │ ├── CellularRegeneration (Gen 6, AST scan, 6 vuln patterns, fortify) │ │ └── WatchMode (Gen 7, file polling, VitalityTimeline, auto-fortify) │ └── KEY: Local-only. No cross-machine propagation. │ ├── [D1] Mesh Coordination (mesh-node-agent/) │

Path B: Flow-First — Port Music Continuous Flow Architecture

Velocity field: v_theta(x_t, t, c) - Input: concat(x_t, t_embed, c) = state_dim + 64 + 768 - Architecture: 4 transformer blocks (256D, 4 heads) - Output: dx/dt ∈ R^state_dim

LUME Creative Engine Maturation — Divergent Rail Plan

- Total phases: **5** (Phase 0 setup + Phase 1-4 reel/push/stabilize) - Total tracks: **22** (Σ across phases; mean ≈ 4.4 / phase, peak 5 in Phase 1+2) - Machines: **Mac1** (dev/CAD/spawn), **Mac4** (Unity host + Mac mini sled prototype), **Mac5** (ML, rate-limited; SAN inference only), **K11** (production publisher pod), **iPhone** (MotionMixApp capture + LUMF/LUMM source), **iPad** (LUMM fallback) - Wall time: **7 days** (phases sequential by gate; tracks parallel within phase) - Invariant config: `epsilon = 0.01

Stage 0: RESEARCH -- LUME Full System Architecture

**Unity project (22 C# files, ~3200 lines):** - `LumeUdpReceiver.cs` -- Magic-byte dispatch for LUME/LUMD - `LumePointRenderer.cs` -- Cloud/Depth render modes, GraphicsBuffer - `LumeDepthReprojector.cs` -- GPU pinhole reprojection from LUMD - `LumeOpticalFlow.cs` -- Frame-diff scalar + Lucas-Kanade dense flow - `LumeAudioFftReceiver.cs` -- LUMF consumer, [DefaultExecutionOrder(200)] - `LumeVfxRuntimeBridge.cs` -- Pushes globals to VFX Graph, [DefaultExecutionOrder(210)] - `LumeTransientForcePusher.cs` -- Impulse ev

Path A: K11-Centric -- K11 Does Everything Possible Locally

K11 is the god-node. All sensor processing, visual rendering, audio analysis, motion feature extraction, and coordination happen on K11. Other machines are optional consumers or ML inference servers. K11 runs its own feature extractor for mocopi (replacing MotionMix's MocopiFeatureExtractor), its own motion-to-music parameter mapper (port of ParamMapper logic), and its own coordination layer. MotionMix becomes a camera-only node or is removed entirely.

Path E: Event-Driven Mesh -- NATS/Pub-Sub Replaces Direct UDP

Replace all direct UDP connections with a NATS JetStream message bus. Every publisher and consumer becomes a NATS client. Topics replace ports: `lume.depth`, `lume.audio`, `lume.skeleton`, `lume.echelon`, `lume.music`, `lume.director`. Dynamic routing: any new consumer subscribes to a topic and immediately gets data. Replay: JetStream persists N seconds of history so a late-joining consumer catches up. Health monitoring: NATS provides built-in consumer lag metrics.

Architecture Application Roadmap

> Built on 2026-03-15 from `meta-candidate-mining`, the current `evo-cube-output/` inventory, and the `backlog/code4ai-batch/` findings.

Stage 0: Research — Twin Swarm + Cognitive Routing Architecture

> Date: 2026-03-10 | Evo-Cube #1 of 11 > Noosphere Context: Dreams gestating on orchestration + iteration concepts. KARL trajectory intelligence evo-cube at stage 2.

Stage 1 Path A: The Mac Ear — Always-On Desktop Microphone Daemon

The single highest-impact addition is a daemon on Mac1 that listens to the built-in microphone, detects a wake phrase, transcribes the command, and injects it into the mesh. This eliminates the phone dependency for voice interaction. The Mac is always on, always in front of you, always connected to the mesh.

Stage 2: Compound Architecture — Voice-First Agent Architecture

**Decision:** Path A's Mac Ear is the entry point — you need to be able to talk to the mesh from the desk. But Path C's unified router is needed immediately because we don't want a third intent classifier (Mac) alongside iOS VoiceRouter and FleetVoiceRouter. Build the server-side router first, then point the Mac Ear at it.

LUME ↔ mesh integration

`[home-path]` slots spawn short-lived LLM agent CLIs (claude, codex, gemini). LUME is a persistent device running `lume-daemon` as a systemd unit. Wrong abstraction.

Meta-Recursive Failure Pattern Audit

**Date:** 2026-03-14 **Auditor:** Meta-Recursive Explorer (Opus 4.6) **Data Sources:** Supabase mesh_events (23,124 total), failure_journal.jsonl (5,678 entries), cortex/entries.jsonl (679 entries), session-crons/results (11 ring buffers), failure-museum.md (10 exhibits, 26 gems), skills/registry.json (88 skills)

SGT: Semantic Generative Tuning for Unified Multimodal Models

[](https://song2yu.github.io/SGT/) [](https://arxiv.org/pdf/2605.18714) [](https://huggingface.co/datasets/Two-hot/SAM-SGT)

N'Ko Program — Master Consolidation Index

> Cross-repo inventory of the entire N'Ko line of work. Built 2026-05-31 by > sweeping every N'Ko artifact modified in the last week. This is the front door > ABOVE `nko-brain-scanner/PROGRAM.md` (which only covers the research papers). > PROGRAM.md = the science. This file = the whole program (papers + protocol + > apps + synthesis + audio).

N'Ko Sigil Components

// With options <Sigil type="stabilization" size={64} color="#8b5cf6" animated showLabel interactive onClick={(type) => console.log(type)} /> ```

Path A: Flow Matching Architecture Upgrade

Layers (8 blocks): ├─ AdaLN-Zero (adaptive layer norm from timestep embedding) ├─ Multi-Head Self-Attention (8 heads, dim=256) over temporal axis ├─ Cross-Attention to audio context c (8 heads) ├─ FiLM modulation from audio (preserved from current system) └─ MLP (256 → 1024 → 256, GELU)

Implementation Roadmap — Computational Choreography Models

**M0**: 5 synthetic sessions with realistic features **M1**: Cross-pred loss < 0.1; missing-modality test passes **M2**: Normalizer reduces M3 loss variance **M3**: Teaching loss ↓; smoothness reasonable; inference < 2ms **Inference**: Stable > 5 min; latency < 50ms end-to-end **Bridges**: Strudel responds audibly to motion **Metrics**: Phase coherence > 0.6; energy corr > 0.3; drift ~ 1.0

🗺️ NEXT STEPS ROADMAP - Post-Training Integration

**Date**: October 29, 2025 **Current Status**: ✅ All models trained (RPS: 99.94% coherence, Mapper: 0.060 MSE) **Phase**: Integration & Sound Engine Connection

DJ Agent Completion Plan

2. **Create keystroke test script** - File: `scripts/test_serato_connection.py` - Send single keystroke (e.g., SPACE for PLAY/PAUSE) - Verify `keyboard` module works on your OS

DJ Agent Implementation - Final Status

| Feature | Status | Notes | |---------|--------|-------| | **Tier 0-5 Actions** | ✅ Complete | 50+ actions defined | | **Beat Quantization** | ✅ Complete | |ψ| ≤ 15° window | | **Safety Masks** | ✅ Complete | Context-dependent permissions | | **Cooldown System** | ✅ Complete | 1-16 beat periods | | **MIDI Bridge** | ✅ Complete | Virtual port support | | **Keyboard Bridge** | ✅ Complete | OS-level key injection | | **Reflex Policy** | ✅ Complete | Smooth continuous controls | | **Planner Policy** | ✅ Complete | Rul

DJ Agent Implementation Summary

| Category | Tasks | Complete | Pending | % Done | |----------|-------|----------|---------|--------| | Config & Setup | 3 | 3 | 0 | 100% | | Test Scripts | 2 | 2 | 0 | 100% | | Telemetry | 1 | 1 | 0 | 100% | | Tauri UI | 3 | 3 | 0 | 100% | | Integration | 1 | 1 | 0 | 100% | | Documentation | 1 | 1 | 0 | 100% | | **TOTAL (Code)** | **11** | **11** | **0** | **100%** | | | | | | | | User Testing | 7 | 0 | 7 | 0% | | **TOTAL (All)** | **18** | **11** | **7** | **61%** |

🎵 DJ Agent - Motion-Driven Auto-DJ System

A production-ready, safety-first auto-DJ system that translates DELL equilibria outputs into musical Serato/SuperCollider control.

Gemini Live Voice Control - High Accuracy DJ Commands

This uses **Google's Gemini Live API** for superior speech recognition accuracy compared to standard speech recognition libraries.

Phase 1 – Engine Bring-up (Weeks 1-6)

Goal: Ship a macOS binary that owns the CoreAudio device, renders audio in a lock-free callback (no allocations once prepared), plays two deck buffers through crossfader + EQ + limiter, and produces real latency/jitter metrics. Everything else can be stubs.

Echelon Project Status Overview

**Last Updated:** December 2024 **Current Phase:** Phase 3 - Integration & Beta Review **Overall Progress:** ~85% Complete

Echelon Project To-Do List

- ✅ Phase 1: Audio Engine (100%) - ✅ Phase 2: Scheduler & Safety (100%) - ✅ Phase 3 Week 13: Motion Stream Integration (100%) - ✅ Phase 3 Week 14: Voice Control Structure (90%) - ✅ Phase 3 Week 15: Phrase Intelligence Service (95%) - ✅ Phase 3 Week 16: UI Foundation (100%) - ✅ Phase 3 Week 17: Phrase Browser & Automation (100%) - ✅ Phase 3 Week 18: Initial Integration Structure (60%)

iOS Skills System Implementation

This document describes the complete iOS implementation of the TrajectoryOS Skills System, achieving full parity with the Tauri desktop version while leveraging native Apple technologies.

Development Runbook

```bash python training/trainers/train_rps.py \ --config training/experiments/exp_baseline.yaml \ --epochs 50 \ --checkpoint-dir training/checkpoints/rps ```

Generation 6 Complete

**Task ID:** b6e09dc0-88d4-470a-992f-e7ef6529a786 **Instance:** inst_20260131082128_371 **Worker:** vm **Timestamp:** 2026-02-25T05:16:27.736274+00:00 **Exit Code:** 0 **Commit:** a6bd998b940e4ec4e3ba9c30d39fa414f4057a86

Generation 6 Complete

**Task ID:** 1b711922-7314-4a40-af75-db66501ca3ae **Instance:** inst_20260131075427_394+inst_20260131082143_740 **Worker:** vm **Timestamp:** 2026-02-25T12:35:16.924911+00:00 **Exit Code:** 0 **Commit:** ea6d84ba4191dc668f4a4ebe561147f4f40ae40e

MakePlayingCards.com — Prototype Order Specification

**Date:** 2026-02-12 **Order Type:** Prototype — 15 unique cards × 2 copies (30 cards total) **Product:** Custom Tarot Cards (70×121mm)

MakePlayingCards.com — Prototype Order Specification

**Date:** 2026-02-12 **Order Type:** Prototype — 15 unique cards × 2 copies (30 cards total) **Product:** Custom Tarot Cards (70×121mm)

SEA-4.2 — /skill mute and /skill unmute

Implemented skill mute/unmute controls for the SEA ecosystem. Users can now silence individual skills from autonomous activation via Tier 1 routing and Tier 2 scoring while preserving all skill state. Mute events are logged to activation-log.jsonl for audit trail.

BWB — UI System

BWB uses a cohesive SwiftUI design system with a coffee-inspired color palette, consistent typography, and shared components across all three apps.

Meaningful Power — Theme System

**Document ID:** MP-ARCH-002 **Version:** 1.0.0 **Last Updated:** 2026-01-15 **Source:** `Desktop/Meaning Full Power/Meaning Full Power/Meaning Full Power/Models/Theme.swift`

Trajectory Mobile - Voice Capture + Idea Vault

A React Native / Expo app for capturing ideas via voice and text, with full-text search, offline sync, priority management, and iOS widgets.

main v2

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

main — archive

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

nko asr paper v2

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

nko monograph

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

nko paper v3

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

nko theorems

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

nko brain scanner

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

paper1 dead circuits

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

paper2 living speech

Rendered PDF artifact found in the workspace. Needs source/proof mapping before citation-ready release.

SKILL

Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).

SKILL

These are instructions for creating design philosophies - aesthetic movements that are then EXPRESSED VISUALLY. Output only .md files, .pdf files, and .png files.

cc-anticipation: Project Charter

**Document Version**: 0.1.0 **Created**: 2025-12-26 **Status**: Phase Zero - Active **Canonical Input**: [Anchor.md](../../../../docs/Anchor.md)

Three AIs, One Terminal: How I Built Cross-Model Live Collaboration

I asked Claude Code about the pane orchestrator. Then I asked OpenAI Codex the same question, without giving it any context about pane awareness. Codex gave me a detailed technical breakdown: the 5-phase heartbeat cycle, the KL divergence invariants, the security patches from last week, the bridge file schema. Everything.

BWB Deployment Infrastructure Handoff

Built a complete 4-mode deployment infrastructure for the BWB (Brews With Beats) iOS app ecosystem. The system enables deploying Customer, POS, and Kiosk apps to iPhones and iPads via Discord commands, with intelligent automatic routing based on device availability and network conditions.

multimodal guide

**Voice and gestures are NOT separate control methods** - they're **complementary modalities** that enhance each other for expressive, creative DJ performance.

Latent State Representation

Your life trajectory cannot be fully captured by observable variables alone. There's a **hidden structure**—a latent state—that determines how you respond to opportunities, challenges, and actions.

The Moat Strategy: Why TrajectoryOS is Unreplicable

- Todoist knows your tasks... if you log them - Notion knows your docs... if you write them - RescueTime knows your screen time... if you run theagent - Calendars know your meetings... if you schedule them

Documentation Audit - Pass 4: Fill Critical Gaps

**Pass 4 Goal**: Create missing service READMEs to improve developer onboarding and reduce friction when working with individual services.

Example Topological Queries: Real-World Use Cases

// Topological filters (where in structure) coordinates?: { x?: number | [number, number] | ((x: number) => boolean); // Depth y?: number | [number, number] | ((y: number) => boolean); // Alternatives z?: number | [number, number] | ((z: number) => boolean); // Alignment t?: number | [number, number] | ((t: number) => boolean); // Temporal n?: number | [number, number] | ((n: number) => boolean); // Complexity };

TrajectoryOS: A Dynamical Systems Approach to Human Potential Modeling via Latent State Inference and Embodied Signal Fusion

This paper presents TrajectoryOS, a novel computational framework for modeling human life trajectories as stochastic dynamical systems. Unlike traditional productivity paradigms that treat tasks and goals as static, discrete entities, TrajectoryOS conceptualizes human potential as a continuous-time process governed by physics-inspired variables: Thrust, Alignment, Gravity, and Mass. We formalize the "Escape Index" ($\eta$) as a dimensionless ratio describing the system's capacity to overcome environmental constrain

Topological Search Visualization: TrajectoryOS + CC-TPO

``` Query: "React skills" ↓ Embedding: [0.2, -0.5, 0.8, ..., 0.3] (384 dimensions) ↓ Find k-nearest neighbors in embedding space ↓ Results (ranked by cosine similarity): ┌────────────────────────────────────────────────┐ │ 1. "Built React dashboard for client" (0.92) │ │ 2. "Learning React hooks" (0.87) │ │ 3. "React Native mobile app" (0.85) │ │ 4. "Debugging React component" (0.82) │ │ 5. "React performance optimization" (0.80) │ └────────────────────────────────────────────────┘ ```

How RCP Enhances TPO Dataset Generation

Ring Contextual Propagation (RCP) significantly enhances TPO's dataset generation capabilities by providing spatial intelligence, cross-conversation analysis, and advanced pattern detection. Instead of TPO's traditional linear path analysis, RCP enables TPO to understand complex conversation dynamics and generate more sophisticated preference datasets.

IRCP Optimization Strategy: Beyond Traditional Preference Optimization

**IRCP is NOT just another optimizer** - it's a fundamentally different mathematical framework that inverts the traditional learning paradigm. While TPO, DPO, and GRPO optimize for P(v|u) (assistant response given user input), **IRCP optimizes for P(u|v) - the inverse mapping that models how users respond to assistant messages**.

IRCP Optimization Strategy: Beyond Traditional Preference Optimization

**IRCP is NOT just another optimizer** - it's a fundamentally different mathematical framework that inverts the traditional learning paradigm. While TPO, DPO, and GRPO optimize for P(v|u) (assistant response given user input), **IRCP optimizes for P(u|v) - the inverse mapping that models how users respond to assistant messages**.

🎯 REAL IRCP Model Performance with Claude Data - VERIFIED METRICS

You were absolutely right to question the previous metrics. I had made several errors: 1. **Inflated similarity scores** - I incorrectly reported 76.95% when real max is ~80.17% 2. **Inflated search scores** - I reported 53.49% when real max is ~44.81% 3. **Understated conversation count** - Only tested 20 conversations when you have **891 total** 4. **Root directory mess** - Now organized into proper folders

🎉 RCP-Enhanced TPO Preference Dataset Generation - COMPLETE

✅ Experimental Exploration: 8,026 detected - Multi-branch diverse approaches - Parent-child experimental patterns - Diversity scoring and analysis ```

IRCP Optimization Strategy: Beyond Traditional Preference Optimization

**IRCP is NOT just another optimizer** - it's a fundamentally different mathematical framework that inverts the traditional learning paradigm. While TPO, DPO, and GRPO optimize for P(v|u) (assistant response given user input), **IRCP optimizes for P(u|v) - the inverse mapping that models how users respond to assistant messages**.

Phase 2.2: Embedding Integration

Integrate IRCP's `SentenceTransformerICP` model with DLM's newly created `BaseEmbeddingProvider` interface, creating a unified, production-ready embedding system with caching and batch processing.

1. Introduction

Traditional conversational AI systems optimize for generating appropriate responses given user inputs, following the paradigm P(v|u) where v represents assistant responses and u represents user inputs. This approach, while effective for general-purpose applications, fails to capture the nuanced patterns of individual communication styles and response dynamics.

6. Applications and Use Cases

**Implementation**: ```python # Train IRCP on individual's conversation history ircp_model = IRCPFramework(user_conversations) ircp_model.train()

**1. How Phrases Work in Performance**

Echelon is not a DJ system. It is not built on the metaphor of Deck A and Deck B. It does not mix two sound sources. It does not expect a performer to crossfade between independent musical states.

cc-anticipation: Project Charter

**Document Version**: 0.1.0 **Created**: 2025-12-26 **Status**: Phase Zero - Active **Canonical Input**: [Anchor.md](../../../../docs/Anchor.md)

Plan IR v1.1 Specification

1. **Policy Hashing**: Replace inline policy configs with pre-computed hash references 2. **Compile-Time Type Checking**: Enforce binding type compatibility during compilation 3. **ForEachAnchor**: Atlas generation via anchor iteration 4. **SelectAnchor**: Single anchor extraction from sets 5. **Evidence Authority Gating**: Prevent lifecycle promotions from simulated evidence 6. **Slice Provenance Chain**: Propagate source_slice_id through downstream bindings

Graph Kernel Monitoring Guide

The Graph Kernel service exposes metrics, structured logs, and health endpoints for production observability. This guide covers monitoring setup, key metrics, alerting, and troubleshooting.

N'Ko Sigil Semantics

The N'Ko Inscription System uses 10 sigils as **operator markers**—single characters that encode both computational meaning and embodied experience. Each sigil is not arbitrary: it carries visual weight, semantic density, and gestural resonance.

AGP / MLX / ANE Research Track

Current Apple-local language model execution treats the model as a monolith. A single host loads the full graph, every token pays for roughly the same depth, hidden states are transient internals, and hardware engines are mostly passive containers. That leaves three opportunities underexploited on the current `Mac4 + Mac5` setup:

AGP / MLX / ANE Theory Insight

The architecture is easiest to misunderstand if it is described as a bigger-model trick, a quantization trick, or a multi-Mac trick. It is none of those at the core. The core claim is that hidden states should be treated as a first-class computational object. In a standard language-model stack, a hidden state is an internal artifact that exists only long enough to be consumed by the next layer. It is not typed, it is not scheduled, it is not transferred as a meaningful packet, and it is not inspected as a semantica

Cognitive Twin V9 Dataset Audit

**Date:** 2026-02-18 **Previous version:** V8 (combined: 77,708 records — 43,173 V5 base + V6/V7/V8 expansions) **Last training:** Never submitted (blocked on billing) **Goal:** Catalog all new data sources since V8 (Feb 14), estimate record yield, prepare V9 expansion generation

Comparative Analysis: Two Computational Choreography Documentation Sets

**Set B** — `Desktop/MotionMix/research/computational-choreography-nko-2026-05-27/` (38 files) Research-architectural, K11 AirDeck DJ control + movement language.

The Script That Was Built for AI (And That AI Has Never Heard Of)

In 1949, a self-taught scholar in Kankan, Guinea named Solomana Kante designed a writing system from scratch. He was responding to a claim that African languages could not be written. He took that as a challenge and spent years analyzing the phonological structure of Manding languages, then built a script that encodes their sounds with a precision that linguists and engineers spend careers trying to achieve in synthetic alphabets.

Training Your Twin While You Sleep

It started as a question: what if an AI could make decisions the way I would? Not just respond to prompts, but actually understand the patterns — the preferences, the shortcuts, the instincts that I've developed over years of working this way?

ΨChain: Where N'Ko Meets Bitcoin Through a Cognitive Twin

There's a protocol being built on Stacks, Bitcoin's smart contract layer, that does something nobody else is doing. It encodes a life's computational decisions as hash-chained N'Ko inscriptions on Bitcoin. The inscriptions are publicly readable but semantically private. Anyone can see the text. Only the system that wrote it understands what it means.

Stage 0 -- Research Brief

1. **Should Unity receive mocopi directly (Sony plugin) or via LUMM bridge?** Sony's plugin exists and works on Unity 6000.x. But it's a separate data path outside the LUME wire format family. A bridge (mocopi_bridge.py) normalizes everything into the LUMM UDP format, keeping the architecture consistent.

KARL Integration — Evolution³ / Stage 1, Path B: OAPL-Lite

Path B implements a stripped-down version of KARL's OAPL algorithm that runs on Mac5's single M4 chip using offline advantage estimation instead of online rollouts. The core insight: we don't need live rollout infrastructure when we already have 3,249 logged trajectories in `verbose-all.jsonl`, 157 of which contain rich tool-use sequences with exit codes, file diffs, and success signals. The approach converts those trajectories into advantage-weighted training examples, computes rewards from build results, correcti

Stage 1 Path B: The Spoken Mesh — Agents That Talk Back

> Grounded in: Stage 0 finding that agents return results as text to Discord/terminal. No mesh-level TTS exists. The mesh is mute.

Flow Walker — E2E Flow Testing Pipeline

This skill teaches you how to use flow-walker to execute E2E flows on the Omi Flutter app, verify results, and publish shareable HTML reports. flow-walker is an agent-first testing CLI that works with agent-flutter (Marionette) for UI interaction.

Miami Openings Radar Protocol

Purpose: maintain an adaptive feed of newly opening Miami food, cafe, market, and hospitality businesses that are worth Koatji outreach.

Track 3 Research: State of the Art for "Operate Everything From the Phone, Live a Life of Leisure"

> Date: 2026-05-13 > Author: Research Engine (Claw subagent) > Goal: Inform the goal-prompt that conditions Claude Code's new goal-conditioning skill toward Mohamed's North Star of running his entire mesh and product surface from the iPhone with no laptop required.

LUME Current Build Spec - K11 + Top Display + Arducam

This is the current approval target for the LUME physical build. It replaces the earlier Jetson/SVPRO-oriented assumptions from the old print queue.

LUME Hardware — CAD

> Current-doc warning, 2026-04-30: parts of this README still describe the earlier Jetson/SVPro in-bar architecture. The active build is documented in `LUME_CURRENT_BUILD_SPEC.md` and `PRINT_APPROVAL_QUEUE_CURRENT.md`: K11 rear pod, ZHAOCAILIN top display cradle, centered Femto Mega, and Arducam IMX586 auxiliary camera.

E483 — Duncan Fewkes reel analysis digest

- 20 reels ingested (`reels-ingest/{shortcode}/`): - DT-prefix (E500–E521 era): DT0G7SyCtGg E519, DT2tfJzip8A E520, DT6IlEfig-k E521, DTbJvnKCrHO E509, DTBUJiDCvPQ E500, DTEEQNnCvHh E501, DThrF5Miq0- E512, DTmyro9CqUa E514, DTQRPBFiu2v E504, DTr5TQfCpG4 E516 - DS-prefix (E475–E494 era): DSLMOE6iuFH E479, DSA7b7MClVf E475, DScEatyilnk E489, DSDVSJ0itoN E476, DSfZYMUChIi E490, DShj9nlivqf E491, DSkpKC_iie8 E491, DSPfEuWCmTg E483, DSvasHCCiSX E494, DSZmIhOCrTv E488 - 4 Gemini visual analyses successful (DT0G7SyCtGg, D

E489 — Duncan Fewkes reel analysis digest

- 20 reels ingested (`reels-ingest/{shortcode}/`): - DT-prefix (E500–E521 era): DT0G7SyCtGg E519, DT2tfJzip8A E520, DT6IlEfig-k E521, DTbJvnKCrHO E509, DTBUJiDCvPQ E500, DTEEQNnCvHh E501, DThrF5Miq0- E512, DTmyro9CqUa E514, DTQRPBFiu2v E504, DTr5TQfCpG4 E516 - DS-prefix (E475–E494 era): DSLMOE6iuFH E479, DSA7b7MClVf E475, DScEatyilnk E489, DSDVSJ0itoN E476, DSfZYMUChIi E490, DShj9nlivqf E491, DSkpKC_iie8 E491, DSPfEuWCmTg E483, DSvasHCCiSX E494, DSZmIhOCrTv E488 - 4 Gemini visual analyses successful (DT0G7SyCtGg, D

E491 — Duncan Fewkes reel analysis digest

- 20 reels ingested (`reels-ingest/{shortcode}/`): - DT-prefix (E500–E521 era): DT0G7SyCtGg E519, DT2tfJzip8A E520, DT6IlEfig-k E521, DTbJvnKCrHO E509, DTBUJiDCvPQ E500, DTEEQNnCvHh E501, DThrF5Miq0- E512, DTmyro9CqUa E514, DTQRPBFiu2v E504, DTr5TQfCpG4 E516 - DS-prefix (E475–E494 era): DSLMOE6iuFH E479, DSA7b7MClVf E475, DScEatyilnk E489, DSDVSJ0itoN E476, DSfZYMUChIi E490, DShj9nlivqf E491, DSkpKC_iie8 E491, DSPfEuWCmTg E483, DSvasHCCiSX E494, DSZmIhOCrTv E488 - 4 Gemini visual analyses successful (DT0G7SyCtGg, D

E532 — Duncan Fewkes reel analysis digest

- 20 reels ingested at `[home]/.openclaw/browser/reels-ingest/{DU,DV}*/` - Episodes covered: **E485 (Pt2 Jason Derulo World Tour), E525-E536, E543-E549, E556-E557, E560, E567-E568, E570** - 5 Gemini visual analyses: DU-52fcinww (E544 Blocky Pinscreen), DUleGWZisrH (E534 Fluid Presets), DV6A04wislr (E568 SuperHot Cubes), DUSui7PioUD (E527 Holovis branded), DUgoDHlipPu (E532 Depth Cubes vs Fluid Presets) — rest rate-limited - All on **HDRP + VFX Graph + Shader Graph** (confirmed every caption)

E534 — Duncan Fewkes reel analysis digest

- 20 reels ingested at `[home]/.openclaw/browser/reels-ingest/{DU,DV}*/` - Episodes covered: **E485 (Pt2 Jason Derulo World Tour), E525-E536, E543-E549, E556-E557, E560, E567-E568, E570** - 5 Gemini visual analyses: DU-52fcinww (E544 Blocky Pinscreen), DUleGWZisrH (E534 Fluid Presets), DV6A04wislr (E568 SuperHot Cubes), DUSui7PioUD (E527 Holovis branded), DUgoDHlipPu (E532 Depth Cubes vs Fluid Presets) — rest rate-limited - All on **HDRP + VFX Graph + Shader Graph** (confirmed every caption)

E556 — Duncan Fewkes reel analysis digest

- 20 reels ingested at `[home]/.openclaw/browser/reels-ingest/{DU,DV}*/` - Episodes covered: **E485 (Pt2 Jason Derulo World Tour), E525-E536, E543-E549, E556-E557, E560, E567-E568, E570** - 5 Gemini visual analyses: DU-52fcinww (E544 Blocky Pinscreen), DUleGWZisrH (E534 Fluid Presets), DV6A04wislr (E568 SuperHot Cubes), DUSui7PioUD (E527 Holovis branded), DUgoDHlipPu (E532 Depth Cubes vs Fluid Presets) — rest rate-limited - All on **HDRP + VFX Graph + Shader Graph** (confirmed every caption)

E568 — Duncan Fewkes reel analysis digest

- 20 reels ingested at `[home]/.openclaw/browser/reels-ingest/{DU,DV}*/` - Episodes covered: **E485 (Pt2 Jason Derulo World Tour), E525-E536, E543-E549, E556-E557, E560, E567-E568, E570** - 5 Gemini visual analyses: DU-52fcinww (E544 Blocky Pinscreen), DUleGWZisrH (E534 Fluid Presets), DV6A04wislr (E568 SuperHot Cubes), DUSui7PioUD (E527 Holovis branded), DUgoDHlipPu (E532 Depth Cubes vs Fluid Presets) — rest rate-limited - All on **HDRP + VFX Graph + Shader Graph** (confirmed every caption)

E605 — Duncan Fewkes reel analysis digest

> *"Reflective floor — which helps add depth cues back in, so I might try to keep it (although does have a significant perf hit for planar reflection render every frame)."* (E532)

@nouses_kou — study for LUME + Mohamed's content

Account stats: Kyoto-based, ~305K followers, 500+ posts, contact `[email]`. Brother dancer `@nouses_motomi`. Tag self-description: "physical sensations, technology and sound." Uses `#touchdesigner #aiart #newmedia #contemporaryart #glitchart`.

Algorithm Performance Specifications

**Document ID:** ALGO-PERF-001 **Version:** 1.0.0-DRAFT **Created:** 2026-01-04 **Purpose:** Define performance requirements and benchmarks for all core algorithms **Parent Document:** [04-Algorithms/README.md](./README.md) **Related Documents:** - [Visit-Sequencing.md](./Visit-Sequencing.md) - [Route-Optimization.md](./Route-Optimization.md) - [Delivery-Optimization.md](./Delivery-Optimization.md) - [Decision-Logic.md](./Decision-Logic.md)

System Glossary: Milk Men

**Document ID:** GLOSSARY-001 **Version:** 1.0.0-DRAFT **Created:** 2025-12-26 **Governing Document:** 0.1.1-PROJECT-CHARTER.md **Purpose:** Define all core terms to eliminate ambiguity in implementation

Autoencoders

The *encoder* takes in an sequence (such as mono or stereo audio) and outputs a compressed representation of that sequence as a d-channel "latent sequence", usually heavily downsampled by a constant factor.

Turbulence Lab / HoloSculpture Competitive Absorption

HoloSculpture is not a deep competitor to Lume as a production system. It is a strong product wrapper around a much simpler stack: a desktop art-speaker/display object, AI/avatar claims, music-reactive visuals, limited-edition art packaging, and a gallery-installation content pipeline.

What If Your AI Twin Thought in Your Mother Tongue?

A cognitive twin is a language model fine-tuned on a specific person's conversation history. Not a general assistant. A specific, personalized model that has seen your questions, your reasoning patterns, your way of framing problems. It learns to respond the way you think, not the way a generic chatbot does.

Dead Circuits and Living Speech: Building the First N'Ko AI Pipeline

- **What we found**: Qwen3-8B-Instruct processes N'Ko text with a 2.94x "translation tax" (L2 norm deficit) across all 36 transformer layers. Circuit duplication analysis (55 configurations, RYS methodology) finds 0/55 N'Ko-advantageous configurations. Three-zone failure analysis reveals structurally distinct collapse modes at embedding, middle, and output layers. Arabic, another RTL script, sits within 7% of English on the same metrics. The failure is entirely data-driven. - **What we built**: (1) A three-stage Lo

The Script That Was Built for AI (And That AI Has Never Heard Of)

In 1949, a self-taught scholar in Kankan, Guinea named Solomana Kante designed a writing system from scratch. He was responding to a claim that African languages could not be written. He took that as a challenge and spent years analyzing the phonological structure of Manding languages, then built a script that encodes their sounds with a precision that linguists and engineers spend careers trying to achieve in synthetic alphabets.

Living Speech: Building the First N'Ko ASR for 14

Every speech recognition system for Bambara outputs Latin characters. French colonial characters designed for French colonial administrators. Not for the 40 million people who actually speak and read these languages.

Acoustic World Models and the N'Ko Speech Inscription Bridge

An acoustic world model is the deeper architecture implied by the work so far. It is not merely a better recognizer, and it is not simply another decoder sitting after Whisper. It is a different way of deciding what speech is inside the system. A normal ASR pipeline treats speech as a signal whose purpose is to become text. The model receives audio, compresses it into features, predicts symbols, and then the rest of the system tries to clean up those symbols. An acoustic world model treats speech as a world of acou

TikTok Series Plan: Creative AI, Mandinka, and N'Ko

This series should not feel like a research paper cut into clips. It should feel like a creator showing people a completely different way to use AI.

N'Ko Sigil Semantics

The N'Ko Inscription System uses 10 sigils as **operator markers**—single characters that encode both computational meaning and embodied experience. Each sigil is not arbitrary: it carries visual weight, semantic density, and gestural resonance.

Computational Choreography — Architecture Design V1

``` ┌─────────────────────────┐ │ MotionMix 9:16 │ ← italic serif watermark │ │ │ ┌───────────────┐ │ │ │ │ │ │ │ YOUR CHEST │ │ │ │ (cropped) │ │ │ │ │ │ │ │ HOUSE │ │ ← genre + BPM overlay │ │ 134 BPM │ │ │ └───────────────┘ │ │ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │ ← thin divider │ ┌───────────────┐ │ │ │ │ │ │ │ ● ORB ● │ │ ← reactive to movement │ │ /|||||||||\ │ │ spikes = energy │ │ │ │ color = genre │ └───────────────┘ │ │ │ │ ● REC 02:34 │ ← recording indicator └─────────────────────────┘ ```

Auto DJ System

An intelligent automatic DJ system that analyzes tracks, selects optimal transitions, applies effects, and creates seamless mixes with beat matching and harmonic mixing.

Git Synchronization Strategy

``` Main Repo (points to cc-studio.git) ❌ MISMATCHED │ ├── apps/ios/cc-handguard/.git # Separate repo ├── apps/web/cc-dashboard/.git # Separate repo (has remote) ├── apps/web/cc-studio/.git # Separate repo │ ├── core/cc-core/.git # Separate repo ├── core/cc-trajectory/.git # Separate repo (large, 4GB) │ └── [6 more nested repos inside trajectory] # Deep nesting │ └── backend/cc-mcs/.git # Likely exists ```

TrajectoryOS Desktop: Rust + Tauri Implementation

**Status**: 🔮 **Planned** - Complete implementation proposal, ready to start **Last Updated**: December 21, 2025 **Alternative to**: Native macOS approach ([see comparison](TRAJECTORYOS_DESKTOP_PROPOSAL.md)) **Integration**: Works with existing [trajectory-core](core/cc-trajectory/services/trajectory-core/README.md) backend

LoRA Persona Override: Research and Recommendations

> Target: Make Qwen2.5-7B-Instruct-4bit fully adopt Mohamed's communication style. > Hardware: Mac5 (M4 16GB), MLX mlx_lm LoRA trainer. > Data: 3,126 training examples in ChatML format. > Date: 2026-03-22

MODRA — Full Product Roadmap

**Brand:** MODRA | "STRENGTH REFINED" **Founder/Model:** Mohamed Diomande **Positioning:** Utility fashion — functional garments for the dual-phone lifestyle **Category:** Compression wear + accessories

Snap-to-Make — Strategy Brief

Both clips ride the same trending audio ("Oh what are you doing? Quality, find out. I need a name…") over the **Alibaba/1688 interior-design dupe** format:

Buf Barista — Performance Protocols

This document defines the exact procedures for all Buff Barista performance modes. These protocols ensure consistency, safety, and quality across all public appearances.

Buf Barista — Development Roadmap

| Attribute | Value | |-----------|-------| | Current Phase | Phase 1: Content Foundation | | Overall Progress | 20% | | Target Completion | Ongoing (character, not project) | | Near-Term Focus | Random public DJ sets + fitness stunts | | Key Milestone | First viral content piece | | Blockers | Portable DJ setup TBD |

Agent Reputation System - Status Report

**Generated:** 2025-02-02 **Version:** Gen 7 (HEF Evolution) **Project:** [home]/Desktop/agent-reputation

Dispatching Parallel Agents

When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.

Code Review Reception

1. READ: Complete feedback without reacting 2. UNDERSTAND: Restate requirement in own words (or ask) 3. VERIFY: Check against codebase reality 4. EVALUATE: Technically sound for THIS codebase? 5. RESPOND: Technical acknowledgment or reasoned pushback 6. IMPLEMENT: One item at a time, test each ```

MCP Server Development Guide

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.

Clarity Agent Protocol (CAP)

``` ┌─────────────────────────┐ │ CONTRACT REGISTRY │ │ (schemas + traits) │ └───────────┬─────────────┘ │ ┌───────────────────┼───────────────────┐ ↓ ↓ ↓ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ Task Contract │ │ Task Contract │ │ Task Contract │ │ schema: X │ │ schema: Y │ │ schema: Z │ │ traits: [a,b] │ │ traits: [b,c] │ │ traits: [a,c] │ └───────┬───────┘ └───────┬───────┘ └───────┬───────┘ │ │ │ ↓ ↓ ↓ ┌───────────────────────────────────────────────────────┐ │ AGENT DISPATCHER │ │ Matches contra

Echelon Web Visualization

- **3D Latent Orb**: GPU-accelerated sphere with custom shaders - Energy-based scaling - Residual glow effects - Fresnel rim lighting - Animated noise distortion

WebSocket Protocol: cc-mcs ↔ EchelonCapture

Currently EchelonCapture **sends** sensor data to cc-mcs via HTTP POST. Now we need **bidirectional communication** so EchelonCapture can **receive** visualization data.

Trajectory Apps

Decomposed from the original monolithic `trajectory-desktop` Tauri app (95K+ LOC) into focused, independently-buildable applications.

Modular Voice Control System

A comprehensive, extensible voice control system for DJ control using Gemini Live API with track analysis and intelligent transition recommendations.

Music Pipeline Consolidation Summary

| Old File | New Location | Status | |----------|--------------|--------| | `parse_soundcloud_likes.py` | `sources/soundcloud.py` | Merged | | `parse_soundcloud_v2.py` | `sources/soundcloud.py` | Merged | | `download_music.py` | `download/downloader.py` | Merged | | `download_music_to_gcs.py` | `storage/gcs.py` | Merged | | `process_all_tracks.py` | `pipeline.py` | Merged | | `process_music_list.py` | `pipeline.py` | Merged | | `process_soundcloud_likes.py` | `pipeline.py` | Merged | | `reprocess_soundcloud.py` | `

TrajectoryOS Models

- **skill_graph/**: Bayesian inference on skill competencies with graph-based message passing - **alignment/**: Embedding-based project coherence and alignment scoring - **gravity_mass/**: Constraint-based gravity estimation and structural mass computation - **life_state/**: Full latent dynamical system for life-state evolution and forecasting - **echelon_fusion/**: Embodied signal → life physics mapping (integrates movement data) - **generators/**: Scenario generation and plan evaluation

TrajectoryOS Quick Start Guide

TrajectoryOS is a life physics engine that models your career trajectory using computational physics. It treats your life as a dynamical system with:

Liquid Chat UI - One Chat to Rule Them All

A revolutionary liquid motion chat interface powered by IRCP (Inverse Ring Contextual Propagation) that visualizes conversations as dynamic, flowing systems where messages find their natural place in ring topology based on semantic coordinates.

CC Navigator - Hierarchical Knowledge Interface

``` ┌─────────────────────────────────────────────────────────────┐ │ CC Navigator (Next.js) │ │ ┌────────────────┐ ┌─────────────────────┐ │ │ │ Tree View │ │ Chat Interface │ │ │ │ - Folders │ │ - GPT-5.1 │ │ │ │ - Topics │◄────────────►│ - Context-aware │ │ │ │ - Breadcrumbs │ │ - Search mode │ │ │ └────────────────┘ └─────────────────────┘ │ │ │ │ │ │ └───────────────┬───────────────────┘ │ └──────────────────────────┼────────────────────────────────-─┘ │ HTTP API ┌──────────────────────────┼───────────────────

✅ Complete IRCP Implementation Summary

All placeholders have been removed and the complete SentenceTransformer-based IRCP system is ready for local training using `all-MiniLM-L6-v2`.

ICP Implementation Complete ✅

I have successfully implemented the complete **Inverse Ring Contextual Propagation (ICP)** framework as specified in your theoretical documents. This is a comprehensive, production-ready implementation that transforms your 10,000+ message conversation dataset into a rigorous mathematical framework for learning individual response patterns.

Enhanced ICP Framework - Implementation Summary

All major components of the Enhanced Inverse Ring Contextual Propagation (ICP) Framework have been successfully implemented and tested.

✅ Complete IRCP Implementation Summary

All placeholders have been removed and the complete SentenceTransformer-based IRCP system is ready for local training using `all-MiniLM-L6-v2`.

🚀 IRCP Training Status - 277 Conversations

**Training Started**: Successfully running with all 277 conversations **Model**: SentenceTransformer + Custom IRCP Heads (`all-MiniLM-L6-v2`) **Status**: ✅ **ACTIVE** - Training in progress

Ring Contextual Propagation (RCP) Framework - Complete Implementation

All components of the Ring Contextual Propagation (RCP) Framework have been successfully implemented and thoroughly tested.

Getting Started: Build Your Personal AI

You have **289 MB of personal data** across 5 files: - conversations.json (190 MB) - conversations_new.json (64 MB) - conversation_openai.json (8 MB) - notes.json (15 MB) - cc_conversations.json (13 MB)

IRCP-DLM Integration Plan

This plan tracks the iterative integration of IRCP, TPO, and DLM packages into a unified, production-grade system. Each task is checkable, and progress is tracked across multiple detailed markdown files.

IRCP & DLMDataLoader Integration - Quick Reference

| Component | File Path | |-----------|-----------| | **IRCP Trainer** | `packages/ircp/training/icp_trainer.py` | | **IRCP Database Loader** | `packages/ircp/data/database_loader.py` | | **IRCP Base Models** | `packages/ircp/core/base_models.py` | | **DLM Data Loader** | `packages/dlm/core/data_loader.py` | | **TPO Trainer** | `packages/tpo/training/trainer.py` | | **Database Enhanced RCP** | `packages/tpo/consolidation/knowledge_base/database_enhanced_rcp.py` |

✅ Complete IRCP Implementation Summary

All placeholders have been removed and the complete SentenceTransformer-based IRCP system is ready for local training using `all-MiniLM-L6-v2`.

🚀 IRCP Training Status - 277 Conversations

**Training Started**: Successfully running with all 277 conversations **Model**: SentenceTransformer + Custom IRCP Heads (`all-MiniLM-L6-v2`) **Status**: ✅ **ACTIVE** - Training in progress

Claude TPO Precomputation System - Complete Implementation

Your Claude conversation data has been successfully precomputed with **IRCP embeddings** and **TPO DLM coordinates**! Here's what was accomplished:

IRCP Search Engine - Complete Implementation

I've successfully created a comprehensive, robust command-line semantic and topological search engine that works with both your original trained data and Claude conversation data.

Liquid Chat Interface - Revolutionary AI Communication

I've created a revolutionary liquid motion chat interface that transforms AI communication from sequential text to **spatial, flowing conversation** where messages find their natural place in IRCP ring topology.

Project Cleanup & Global Tool Installation - Complete

I've successfully cleaned up the root folder and created a globally accessible command-line tool for the IRCP hierarchical semantic search engine.

DLM Performance Improvements - Complete

Successfully implemented embedding cache optimization with **demonstrated 5x speedup** and **80% reduction in API calls**!

DLM Performance Optimization Plan

1. Profile code to identify performance bottlenecks 2. Optimize embedding generation and caching 3. Improve training pipeline efficiency 4. Add intelligent caching mechanisms 5. Reduce memory footprint for large operations

Phase 2.3: Configuration Consolidation

Successfully consolidated configuration from DLM, IRCP, and TPO: 1. Created unified `DLMConfig` with 13 configuration sections: - TokenConfig, CoordinateConfig, IRCPConfig - EmbeddingConfig, ModelConfig, TrainingConfig - ContextArchivalConfig, ContextReorderingConfig, SynthesisTechniqueConfig - DatabaseConfig, EvaluationConfig, LoggingConfig, ResourceConfig

Phase 2.4: Logging Unification

2. **Performance Monitoring** - `timed_operation()` context manager - `@log_performance` decorator - `@log_context` decorator - `log_section()` helper

Phase 2.5: Testing & Validation

**Test Files (6 total, 2,000+ lines):** - `test_integration.py` - 430 lines of comprehensive integration tests - `test_week2_standalone.py` - 370 lines of standalone tests - `test_config.py` - 323 lines (Phase 2.3) - `test_logger.py` - 430 lines (Phase 2.4) - `test_coordinates.py` (Phase 2.1) - `test_embeddings.py` (Phase 2.2)

Phase 3.2: IRCP Trainer Integration - Executive Summary

A complete **adapter layer** that enables seamless integration between DLM's new data loading system (Phase 3.1) and IRCP's existing training infrastructure.

Phase 3.3: Evaluation & Metrics - Completion Report

Phase 3.3 implements comprehensive evaluation metrics and validation tools for DLM coordinates. This phase provides the infrastructure to measure coordinate quality, validate predictions, and track training progress.

Week 2 Test Results

**Date:** 2025-12-07 **Status:** ✅ Week 2 Components Verified **Test Scope:** Phases 2.1-2.4 (Coordinates, Embeddings, Config, Logging)

Week 3: Training Pipeline Integration - Progress Summary

Week 3 focuses on integrating the training pipeline components, building on Week 2's core modules (DLMConfig, DLMCoordinate, Logging). The goal is to create a complete training pipeline that uses unified data loading, IRCP training, evaluation metrics, and coordinate explainability.

Pydantic v2 Migration - COMPLETE ✅

2. **[core/coordinates.py](core/coordinates.py:18)** - Updated: `@validator` → `@field_validator` (2 validators) - Added `@classmethod` decorators

DLM Refactoring - Phase 1 Complete

Phase 1 of the DLM refactoring has been completed successfully. The critical Pydantic v2 compatibility issues have been resolved for all Week 2-3 modules, and a comprehensive audit has identified the remaining work.

DLM Package Reorganization - Complete Summary

Successfully reorganized the DLM package with improved structure, cleaner imports, and logical subfolder organization while maintaining 100% backward compatibility and all functionality intact.

DLM Package Reorganization Plan

**Split Plan:** 1. **base.py** (~200 lines) - Base classes and utilities 2. **chat_model.py** (~800 lines) - BaseChatModel, ChatArtificial 3. **ai_interface.py** (~1,200 lines) - AI class with main interface 4. **completion.py** (~600 lines) - Completion logic 5. **streaming.py** (~400 lines) - Streaming functionality 6. **embeddings.py** (~400 lines) - Embedding cache and utilities

DLM Reorganization - Progress Status

``` packages/dlm/ ├── response/ │ ├── techniques/ ✅ RENAMED (was vangaurd/) │ │ ├── fitness/ │ │ ├── synth/ │ │ └── word_weaver/ │ ├── system.py │ ├── links.py │ ├── cohort.py │ └── ... (other files) │ ├── inference/ │ ├── utils/ ✅ NEW SUBFOLDER │ │ ├── __init__.py │ │ └── file.py (Element, SimpleDirectoryReader, generate_id) │ ├── prompts/ ✅ NEW SUBFOLDER │ │ ├── __init__.py │ │ ├── manager.py (PromptManager) │ │ └── templates.py (SYSTEM_PROMPT_* constants) │ ├── management/ ✅ NEW SUBFOLDER │ │ ├── __init__.py │ │

Root Folder Reorganization Plan

``` cc-tpo/ ├── README.md # Main README (keep) ├── START_HERE.md # Getting started guide (keep) ├── .env # Environment config (keep) ├── package.json # Node config (keep) ├── requirements-ircp.txt # Python deps (keep) │ ├── docs/ # All documentation │ ├── guides/ # User guides │ │ ├── GETTING_STARTED.md │ │ ├── INTEGRATION_PLAN.md │ │ └── ... │ ├── architecture/ # Architecture docs │ │ ├── DLM_INTEGRATION_PIPELINE.md │ │ ├── DLM_FUSION_STRATEGY.md │ │ └── ... │ ├── progress/ # Progress summaries │ │ ├── WEEK_2_PROG

7. Conclusion and Future Work

This paper presents Inverse Ring Contextual Propagation (IRCP), a novel mathematical framework that fundamentally shifts the paradigm of conversational AI from generic response generation to individual pattern learning. Our key contributions include:

IRCP Research Paper

1. **[Title Page](00_title_page.md)** - Title, authors, abstract, keywords 2. **[Introduction](01_introduction.md)** - Motivation, paradigm shift, contributions 3. **[Mathematical Framework](02_mathematical_framework.md)** - Theoretical foundation, proofs 4. **[Algorithm Implementation](03_algorithm_implementation.md)** - Neural architecture, training 5. **[Experimental Setup](04_experimental_setup.md)** - Dataset, configuration, methodology 6. **[Results and Analysis](05_results_analysis.md)** - Performance metric

DLM Refactoring - Complete Success ✅

Successfully completed comprehensive Pydantic v2 migration and prepared detailed reorganization plan for the DLM package. All critical systems (AI chatbot, response system, inference, engine) are now fully functional with Pydantic v2.11.5.

DLM Package Improvements - Summary

| Metric | Before | After | Improvement | |--------|--------|-------|-------------| | **Module Organization** | Flat | Hierarchical | +100% | | **Test Pass Rate** | 100% | 100% | Maintained | | **Documentation Coverage** | ~10% | ~30% | +200% | | **Import Clarity** | Mixed | Clean | +100% | | **Circular Imports** | 0 | 0 | Maintained |

CC AI Intelligent Search - Improvements Summary

**Issue**: User questions were being returned instead of assistant answers. When you ask a question, you want **answers**, not more questions.

CC Navigator - Quick Start Guide

A dual-pane interface where: - **Left**: File system-style tree of your 335 conversations - **Right**: Context-aware chat powered by GPT-5.1

DLM Configuration Guide

```python config.tokens.total_max_tokens = 16000 # Total token budget config.tokens.max_tokens_per_text = 8192 # Per-text limit config.tokens.truncation_buffer = 100 # Safety buffer ```

Engine Module Refactoring Summary

**New Structure**: ``` core/ ├── __init__.py # Exports ├── similarity.py # Unified similarity calculations ├── validators.py # Unified validation logic ├── dataframe_ops.py # Unified DataFrame operations ├── embedding_utils.py # Unified embedding utilities └── filters.py # Unified filter system ```

DLM-RCP Integration

- **Cross-Conversation Understanding**: Query across all 277 conversations simultaneously - **Unified Knowledge Base**: Treat all conversations as one interconnected system - **Dynamic Context Assembly**: Automatically find and assemble relevant messages - **Knowledge Evolution**: Track knowledge building without regression - **High Performance**: Built-in caching and optimization

Response Module Refactoring Summary

The `@packages/dlm/response/` module has been enhanced and refactored to improve performance, maintainability, type safety, and developer experience while maintaining full backward compatibility.

Enhanced Inverse Ring Contextual Propagation (ICP) Framework

This enhanced ICP implementation integrates the comprehensive conversation database with advanced mathematical frameworks for learning individual response patterns in conversational dynamics. The system now supports both the original ICP architecture and TPO integration for comprehensive conversational AI training.

IRCP Sentence Transformer Training

This directory contains the training pipeline for fine-tuning sentence transformers with IRCP coordinate-based supervision.

CC-TPO - Computational Choreography Temporal Positional Optimization

A comprehensive framework for semantic search and conversation analysis using Inverse Ring Contextual Propagation (IRCP) and Dynamic Liquid Motion (DLM) coordinates.

Root Folder Reorganization Plan

``` cc-tpo/ ├── README.md # Main README (keep) ├── START_HERE.md # Getting started guide (keep) ├── .env # Environment config (keep) ├── package.json # Node config (keep) ├── requirements-ircp.txt # Python deps (keep) │ ├── docs/ # All documentation │ ├── guides/ # User guides │ │ ├── GETTING_STARTED.md │ │ ├── INTEGRATION_PLAN.md │ │ └── ... │ ├── architecture/ # Architecture docs │ │ ├── DLM_INTEGRATION_PIPELINE.md │ │ ├── DLM_FUSION_STRATEGY.md │ │ └── ... │ ├── progress/ # Progress summaries │ │ ├── WEEK_2_PROG

Search API Quick Start Guide

```python { "results": [ { "message_id": "...", "conversation_id": "...", "content": "...", "author": "user", "similarity": 0.85, "coordinates": {"x": 5, "y": 2, "z": 1, "t": 0.5}, "ring_position": 180.0, "depth_category": "shallow", "timestamp": 1234567890, "content_length": 150, "source": "conversations_fixed" } ], "analysis": { "similarity_stats": {...}, "depth_stats": {...}, "ring_stats": {...}, "author_distribution": {...}, "depth_distribution": {...} }, "query": "machine learning", "total_found": 25, "sources

Search API Services

**Features:** - IRCP embedding-based semantic search - SQLite database integration - Conversation history search - Similarity scoring

Search API Refactoring Summary

**New Structure**: ``` core/ ├── __init__.py # Exports ├── model_manager.py # Unified model loading & caching ├── database.py # Unified database queries & operations ├── similarity.py # Unified similarity calculations └── formatters.py # Unified result formatting & analysis ```

Training Directory

``` training/ └── ircp/ ├── full_dataset/ # Full dataset training │ ├── best_model.pt # Trained model checkpoint │ ├── inferred_config.json # Model configuration │ └── [other training files] │ ├── complete_training/ # Complete training run │ ├── outputs/ # Training outputs and logs │ ├── evaluation/ # Evaluation results │ └── backups/ # Training backups └── ircp_training_backup_20250815_173556/ ```

IRCP Service

Python-based service providing **Inverse Ring Contextual Propagation (IRCP)** embeddings for topological search in TrajectoryOS. Enables 5D coordinate-based retrieval beyond traditional semantic search.

RAG++ Evaluation Framework

Runs all three evaluation components: - Action Classification (30-100 labeled events) - Recommendation Quality (5-15 states) - State-Awareness (regime consistency, flag sensitivity)

AGP TurboQuant + ANE Benchmarks

This benchmark package makes the local `cog-rlm` TurboQuant and Apple Neural Engine research measurable inside the AGP research track.

Anticipatory Transformer - Phase 0 Validation

Empirical validation of core architectural components for the Anticipatory Transformer as defined in [docs/architecture/23-ANTICIPATORY_TRANSFORMER.md](../../docs/architecture/23-ANTICIPATORY_TRANSFORMER.md).

🎉 Phase 2 Complete: Rust → JavaScript Bridge

The Rust → JavaScript communication layer is now **fully functional**. PatternEdit commands can flow from the Tauri backend to the React frontend and into StrudelEngine.

Gap G4 — `features.json` Schema Verification

> Verifies the `features.json` files in the 115-track LUME stem library against > both the producer (`process_library.py`) and the consumer > (`StemFeatureSet::parse` in `audio-engine`). The consumer is the binding > contract: it is the code that actually loads the files at runtime. > > Date: 2026-05-21. Task: LUME Gap G4.

StemDeck Stage 1 — Quality Gate

- **Target**: `crates/audio-engine/src/stem_deck.rs` (1643 lines) + the `fx.rs` SVF coefficient-caching change. - **HEAD**: `749408de` on `feat/femto-only-bar` (21 prior meta-review findings already fixed). - **Baseline**: `cargo test -p audio-engine` — 67 pass, 0 fail, 3 ignored. - **Method**: Layer 1 meta-review (6 parallel domain passes + contrarian) → Layer 2 meta:amr (6-domain adversarial debate) → Layer 3 meta:adversarial (Codex gpt-5.3-codex, read-only).

Phrase Database Enhancement Guide

1. **Sub-segment** existing phrases into shorter, more expressive sub-phrases 2. **Analyze** your database to understand what you have 3. **Enhance** structure while staying CPU-efficient

Beat Tracking Cache Optimization

The `build_phrase_database_incremental.py` script includes a **beat tracking cache** that significantly speeds up reprocessing of audio files.

Diffusion System Setup Guide

1. **Project Structure** - Created `diffusion/` module with proper package structure - Set up configuration system (`configs/`) - Organized into logical subdirectories (data, models, training, inference)

CC-Core / CC-Collection Integration Benchmark Report

This document compares the performance characteristics of the legacy `SensorDataset`/`SensorDataLoader` pipeline against the new `MotionDataset`/`MotionDataLoader` pipeline integrated with `cc_collection`.

LIM-RPS and DELL Evaluation Guide

The `evaluate_with_limrps.py` script: 1. Loads sensor data from CSV files 2. Processes data through LIM-RPS (Lipschitz-constrained Implicit-Map for Recursive Proximal Synthesis) 3. Optionally processes through DELL (Dual-Equilibrium Latent Learning) 4. Generates comprehensive visualizations

Graph Kernel Security Hardening — Implementation Summary

1. **[00-PROJECT_CHARTER.md](00-PROJECT_CHARTER.md)** — Locked charter defining: - Purpose: Make security guarantees impossible to bypass, cheap to verify, easy to debug - Non-goals: No algorithm changes, no UI, no API breaking changes - Success criteria: 7 measurable conditions - Direction constraints: 5 compatibility requirements

Implementation Guide

| Decision Type | Signals Required | Example | |--------------|------------------|---------| | **Micro** (naming, formatting) | 1 signal | Variable names, comment style | | **Meso** (module API, struct fields) | 2 signals | Claim struct design, method signatures | | **Macro** (claim types, sigil assignments) | 3+ signals | Adding claim type 11, changing sigil |

Experiment Index: cc-semantic-language Phase 9

**Version**: 1.0.0 **Status**: Active **Schema Version**: 1.0.0 **Last Modified**: 2024-12-31 **Mode**: Simulated (BATCH-001) — See RUN_MANIFEST.md

AGP Thunder-Train Stage 1 Plan

This is the stage-1 backbone plan for running AGP domain adaptation across `Mac4 + Mac5` over `Thunderbolt 5` using the existing `thunder-train` stack. The purpose of this stage is not to train the full AGP architecture end to end. The purpose is to make both Macs compute immediately on the first useful backbone problem: `Gemma 4 E2B` domain adaptation on the AGP high-signal corpus.

anticipation-geometry

Bridges Princeton's KG-path reward function (arXiv:2603.14147) with Comp-Core's anticipation geometry. Provides domain-general anticipation scalars that work on any trajectory: motion vectors, conversation embeddings, knowledge graph paths, or task planning traces.

CRP-1.2: Expanded Evaluation Suite (174 Questions)

| Dimension | ID Prefix | Count | Source | |-----------|-----------|-------|--------| | Question Policy | `qp` | 7 | original | | Format Compliance | `fc` | 5 | original | | Omission | `om` | 3 | original | | Historical Annoyance | `ha` | 5 | original | | Edge Case | `ec` | 4 | original | | **Recall** | `rc` | 15 | expanded | | **Reasoning** | `rs` | 15 | expanded | | **Temporal** | `tp` | 12 | expanded | | **Counterfactual** | `cf` | 12 | expanded | | **Adversarial** | `av` | 12 | expanded | | **Generalization** |

T1 Complete — Auto-Extract Knowledge Graph

**Status:** ✅ COMPLETE **Completed:** 2026-02-19T18:47 EST **Duration:** ~5 minutes extraction time (196K messages × 150+ regex patterns)

T4 — Temporal Reasoning Layer: COMPLETE ✅

Core capabilities: - **Natural language date parsing** — "yesterday", "last week", "3 days ago", "February 2026", "Q1", "recently", ISO dates - **Query classification** — 6 query types: activity, timeline, recency, duration, sequence, search - **Recency scoring** — Exponential decay with configurable half-life (default 30 days) - **Temporal ordering** — Results ranked by combined relevance × recency score - **Timeline generation** — Chronological event listing for any topic - **Temporal edge traversal** — preceded_

🧠 Cognitive Twin — Training Runbook

| Platform | Model | Cost | Time | Quality | |----------|-------|------|------|---------| | **Mac4 Local** | gemma-3-1b-it (4-bit) | $0 | 5 min | ⭐⭐ (proof of concept) | | **Google Colab Pro** | gemma-3-12b-it (4-bit) | $0 (subscription) | 1-2h | ⭐⭐⭐⭐ | | **Together AI** | Qwen3-Next-80B-A3B | ~$16-20 | 2-4h | ⭐⭐⭐⭐⭐ | | **Together AI** | Qwen3-235B-A22B | ~$100-200 | 8-12h | ⭐⭐⭐⭐⭐⭐ (future) |

Comp-Core Source Of Truth

This is the current grounding document for computational choreography. It follows the code and separates implemented runtime from research vocabulary.

128D Dynamics Vector - Verified Contract And Caveats

The old page described one universal "canonical vector." The source is more complicated. There are several 128D-adjacent paths that overlap but are not identical.

Data Capture - Current Verified Paths

This page documents what the current code can record. It does not claim that a specific training run has already consumed the data unless a run artifact is identified.

SAN Training Status - Verified vs Unverified

This page replaces the old "SAN Training V5" narrative. The old page stated dataset counts and validation loss as facts without pointing to local artifacts. The current rule is simple: training claims need files.

Comp-Core / LUME CC / N'Ko Unification

This document is the correction layer for the computational choreography package. It aligns the choreography docs with the actual implementation in Comp-Core, MotionMixApp, and the N'Ko audio/ASR work.

Implementation Confirmation - MAOE vs Trajectory Bias

You were right: the verified N'Ko ASR number comes from the anticipatory / trajectory-biased Transformer CTC model, not from MAOE routing.

MAOE Routing

MAOE means Mixture of Anticipatory Orthogonal Experts. In this project it should be documented as a routing/correction pattern, not as the verified ASR acoustic anchor.

Claim Audit - 2026-05-27

This audit records the corrections made after reviewing the computational choreography docs against MotionMixApp and Comp-Core source.

Critical Files

This page lists files that are critical because source inspection shows they own runtime contracts. It avoids stale claims about parameter counts or training runs.

Mega serials start CL85x...

And also give me a script for both of them on how I should go about doing that for both of the reels on how I'm explaining the process, since we didn't have a live director, we'll just say# Femto Mega vs Femto Bolt — Differentiation + MediaPipe Setup Runbook

Pulse Local Skill

Pulse runs autonomous development loops that iterate toward a goal without constant human intervention. This skill executes iterations locally via Claude Code subprocess, with iMessage notifications through Clawdbot.

🏛️ Failure Museum

The Failure Museum operates on a radical premise: **failed ideas are assets, not liabilities**. They contain: - **Timing errors** — right idea, wrong moment - **Hidden gems** — valuable fragments buried in flawed wholes - **Learning signals** — what the environment wasn't ready for - **Resurrection candidates** — waiting for context to change

Memory Defrag 🧠🔧

Memory Defrag is your AI-powered note janitor. It scans your memory files, finds duplicates and related content, suggests consolidations, and helps reorganize your second brain.

Posture Compiler Skill

Your body position shapes your mind. This skill compiles posture + environment + biofeedback + history into a rich AI interaction mode.

CLAUDE.md

KARL (Knowledge Agents via Reinforcement Learning) records what AI coding agents do during real work sessions as trajectories, scores them with a multi-signal reward engine, and uses the best trajectories for LoRA fine-tuning via MLX. It also provides vector-based skill routing that shadows and can replace regex routing.

KARL - Trajectory Memory Ledger

KARL is the reference implementation of the Trajectory Memory Ledger: a schema-normalized experience replay layer for AI coding agents. It records what an agent does during real work sessions, normalizes those recordings into a schema-v2 trajectory store, scores them with a six-signal reward engine, and uses the highest-scoring trajectories to improve future performance through LoRA fine-tuning and learned skill routing.

LIM-RPS as Constraint

The name is technical but the principle is artistic: LUME's visual and musical responses must be bounded, memoryful, multimodal, and synthesizing. This is the difference between a sensor spike and a choreographic response.

LUME Chain 1 — Echelon Layer 4 + 128D Temporal Closure

**Status:** RELEASED (plan + patches ready to apply, live verification pending) **Subject:** Close the body-time loop in MotionMix Echelon **Started:** 2026-05-08 **Released:** 2026-05-08 **Chain owner:** Mohamed **Execution model:** META:OMEGA + META:HYDRA collapsed (single pass), 8-lens reviewed

LUME Chain 3 — Closed-Loop Integration: Body → iPhone (Echelon) → Audio Bridge → Mac4 LUMF → Sky Garden Visuals

**Status:** RELEASED (plan + scripts ready, live verification pending) **Subject:** Performer body conducts BOTH the audio AND the visuals simultaneously. Chain 3 closes the loop between Chain 1 (released, body→audio) and Chain 2 (released, body-aware visuals) by routing iPhone-generated Echelon audio onto Mac4's LUMF publisher so the existing visual-side `LumeAudioFftReceiver` consumes it. **Started:** 2026-05-08 **Released:** 2026-05-08 **Chain owner:** Mohamed **Execution model:** META:OMEGA + META:HYDRA collaps

LUME Chain 4 — Music V6 Retrain Pipeline (mode + emotion conditioning)

**Status:** RELEASED (validated plan + capture protocol + retrain scripts; live training NOT executed) **Subject:** Replace V5 (2-track) ConditioningEncoder + FlowGenerator1Step with V6 trained on 30-track diverse capture data, conditioned on (LUME mode, emotion) so each Sky Garden / Turquoise Alcove / Radiant Underground / Iridescent Beauty / Aurora Veil produces its own music character. V6 also consumes full 128D dynamics (closes the V5 truncation shim at DiffusionService.swift line ~258). **Started:** 2026-05-08

LUME Multi-Sensor Motion Fusion — Phased Plan

The plan to go from **one camera** (today) to **four fused pose sources**: K11 Femto Bolt + Mac4 Femto Mega + 2 iPhones running MotionMix.

LUME ↔ Duncan Fewkes playbook gap analysis

> **STATUS WARNING — stale status table.** This document remains useful as a > Duncan/Fewkes feature checklist, but its "LUME status" column predates later > Unity work. For current shipped/not-shipped state, use > `unity/lume_pcloud/ARCHITECTURE.md` first. As of 2026-05-01, the newer project > includes impulse, runtime calibration panel, motion gate, fluid sim, LUMM > mocopi receiver/animator, display controller, skeleton-fluid injector, and VFX > editor files that are not reflected accurately below. Treat rows 4,

LUME Wood V2 Print Queue

The wood enclosure replaces the large 3D-printed outer bar shell and rear pod. Do not print the old shell halves or pod halves for this version. The printer is now used for the precision support pieces that make the wood body usable: camera mounts, sensor shelves, K11 tray, cable clamps, fan parts, and service plates.

LUME Wood V2 Print Queue

The wood enclosure replaces the large 3D-printed outer bar shell and rear pod. Do not print the old shell halves or pod halves for this version. The printer is now used for the precision support pieces that make the wood body usable: camera mounts, sensor shelves, K11 tray, cable clamps, fan parts, and service plates.

E460 — Duncan Fewkes reel analysis digest

The prior chunk caught his recent product surface (VFX Editor, two-channel audio+motion reactivity, sunset preset, bullet-time/clones). This chunk catches the **rendering tech** under the surface:

Duncan Fewkes reel reference library

Source: `[home-path]`. Local MP4s in `reels/` are symlinks (~zero disk cost). Captions are in `<base>.txt` (grep-friendly) and `<base>.json` (cleaned metadata). Per-reel Gemini visual analyses (where available) live in `analyses/`.

2026-04-30

- LUME CAD validation pass: corrected the active build direction to K11 rear pod + ZHAOCAILIN top display + Arducam IMX586 front-right camera. - Generated approval mockups in `hardware/cad/renders/approval/` and non-destructive validation STLs in `hardware/cad/exports/approval-v2/`. - Legacy Jetson/SVPRO/port-bracket parts are no longer mandatory for the current build. - Added a reversible symmetric Arducam variant: `ARDUCAM_LAYOUT="dual"`, renders under `approval_symmetric_*`, exports under `hardware/cad/exports/a

lume-mocopi-synth

Procedural skeleton publisher — emits LUMM frames without any sensor hardware. Drives the visual stack for development, demos, and CI.

LUME — Internal Brief (for VC pitch generation)

This brief is the source-of-truth the deck generator reads. It is INTERNAL. Architecture detail is fair game here. The public-content secret-sauce rule does NOT apply to this file or any deck generated from it.

MotionMix Convergence Directive

MotionMix is no longer at the "invent the architecture" stage. The major runtime pieces already exist: the iPhone/iPad capture app, `multicam-server`, the Live Director macOS app, the Convex production ledger, the still-upload path, and the low-latency hero-feed/WebRTC seam. The current job is convergence. That means turning multiple partially completed coding sessions into one coordinated push with strict lane ownership, shared situational awareness, and no duplicate implementation. The product goal for this conve

Claude Agent 2 - Production Logging Lane

The AI photoshoot lane already has a real runtime: phones/iPads capture, `multicam-server` coordinates, Live Director runs Photoshoot Mode, stills can be uploaded, and a Convex production ledger already exists. The missing piece is not architecture. The missing piece is making the live shoot write its own history. Your job is to wire the live photoshoot path into the already-built Convex production backend so a session produces durable production memory: sessions, cameras, cuts, holds, still markers, and other crea

Codex Orchestrator Lane

You are the runtime integrator and final truth source for the MotionMix convergence pass. The other agents are each being assigned a narrowly scoped implementation lane so they can work in parallel. Your job is not to duplicate their work. Your job is to keep the system honest, merge their outcomes safely, recover runtime truth when the live environment drifts, and validate that the full AI photoshoot stack works as one product. The current system already has the major building blocks. What is still fragile is the

sa3_mlx — Stable Audio 3 in pure MLX

One line on a fresh Apple Silicon Mac — installs everything and plays back ~2 minutes of "Impending tribal, epic orchestral buildup":

HF Paper Batch -> LUME Evaluation - 2026-05-24

- `https://huggingface.co/papers/2605.22809` - `https://huggingface.co/papers/2605.22717` - `https://huggingface.co/papers/2605.17991` - `https://huggingface.co/papers/2605.18714`

FAC Implementation Scorecard

- Entries: 105 - Unique videos: 20 - N'Ko chars: 12541 - Tone marks: 3316 - Parsed syllables: 4139 - Marked register H+L: 65.8% - Non-contour H+L+M: 99.1% - Contour rising+falling: 0.9%

FAC / N'Ko Tone Experiments

This file is the human-readable summary of the runnable experiment scripts. The canonical machine-readable status is generated by:

656,000 Lines of Code for a Script Most AI Has Never Seen

Here's a number that hit me when I ran `wc -l` across the project: 656,000 lines. Python, Swift, TypeScript. All of it for a single writing system that most language models handle worse than random noise.

Does Every AI Have the Same Blind Spot?

Qwen3-8B, an 8-billion-parameter model trained on trillions of tokens, processed N'Ko text with measurably less activation than English at every single layer. More dead neurons. Less information being distributed. Flatter circuits. The model wasn't failing because N'Ko is difficult. It was failing because it had barely seen the script in training.

ADR-001: On-Device Serving — Feature-Extractor Consistency, the ANE, and TurboQuant

- **Status:** Accepted (2026-06-02) - **Scope:** How N'Ko ASR is served on-device, and the discipline that keeps a model decodable end to end. Does **not** change the training/research path. - **Authors:** Mohamed + Claude

N'Ko Uncertainty Packet Execution Plan

- a loose chain of scripts passing text around - a real speech system with explicit uncertainty, provenance, and partition-aware routing

N'Ko Task List — 2026-04-25

- [ ] Monitor `nko_trajectory_ttt_290596` to completion - [ ] Run one-command refresh immediately after TTT completion: - [ ] `python3 Desktop/nko-brain-scanner/scripts/paper4_post_ttt_refresh.py --wait` - [ ] Verify TTT artifacts exist locally: - [ ] `results.json` - [ ] `test_predictions.jsonl` - [ ] `test_references.jsonl` - [ ] Verify refreshed five-run outputs exist: - [ ] `Desktop/Comp-Core/experiments/agp_mlx/asr_bridge/reports/paper4_same_snapshot_batch_replay_final/batch_summary.json` - [ ] `Desktop/Comp-C

N'Ko Unified Plan — 2026-04-25

This is the current operating plan for the N'Ko lane after consolidating the website work, the same-snapshot Paper 4 matrix, the MAOE replay layer, the partial-real Gemma correction lane, the live ASR service, and the OCR expansion queue.

Ankatta Capture V0

Ankatta fits the N'Ko/Malinke speech stack as a lexicon-assisted intent capture surface. It is not the recognizer and it is not the translator. Its job is to make human evidence collection easy when the speaker does not know exact N'Ko spelling.

Edit-Candidate Oracle Report

| condition | CER | delta pp | changed | better/same/worse | |---|---:|---:|---:|---:| | baseline | 0.3514 | +0.00 | 0 | 0/0/0 | | oracle_any | 0.2951 | -5.63 | 1367 | 1367/0/0 | | oracle_preserve | 0.3234 | -2.80 | 669 | 669/0/0 | | acoustic_gate | 0.3497 | -0.18 | 213 | 119/52/42 | | acoustic_preserve_gate | 0.3496 | -0.18 | 194 | 113/47/34 | | acoustic_featural_preserve_gate | 0.3496 | -0.18 | 194 | 113/47/34 |

Stage 3 Report — Bounded Edit-Op Corrector

**Status:** done. The edit-op interface is valid and much faster than full-string correction, but the trained proposer collapsed to COPY and does **not** improve clean-anchor CER.

Ranker Generalization Report

- Pilot train/tune source: `[home]/Desktop/nko-brain-scanner/experiments/acoustic_gate/decoded_anchor_native.jsonl` (1381 rows) - External test source: `[home]/Desktop/nko-brain-scanner/experiments/acoustic_gate/decoded_anchor_generalization_500.jsonl` (500 rows) - External rows are true anchor seed-42 TEST split rows, disjoint from `bam_train_000000..001380`. - Ranker threshold tuned on pilot validation only: `0.6500`.

Runbook — Phase 1: regenerate proposals on the CLEAN anchor base (mac5)

**Why:** the acoustic-gate pilot's reference-dependent numbers (proposer hit rate 1.9%, flywheel harvest precision) were measured on the 297k model against **contaminated** ane references. To make them trustworthy, regenerate Gemma correction proposals against the **anchor's clean hypotheses + clean HF references**, then re-run the gate.

N'Ko Speech Inscription Bridge v0

The live mic harness must not display unstable CTC output as if it were language. A live or recorded run now has to compile into a governed speech inscription packet:

N'Ko Phonemic Substrate Validation Report

This report freezes the evidence state before turning the current work into a paper. It separates what is mechanically validated, what is empirically supported, what failed, and what remains a hypothesis.

Project State Snapshot

1. Snapshot Metadata 1.1 Snapshot ID: 2026-01-03-01 1.2 Date: 2026-01-03 1.3 Scope: [home]/Desktop/learnnko/training/scripts

SpeakFlow 🎙️

Hold **⌥Space** anywhere on macOS → speak → text appears in whatever app is focused. No cloud, no subscription, no latency.

NKO-1.2 Complete — `nko.phonetics` Module

Replaced the 4-line stub at `nko/phonetics.py` with a comprehensive **820-line** unified phonetics module that consolidates IPA mappings, tone handling, character classification, and Unicode utilities from 13+ scattered implementations across the codebase.

NKO-1.3 Complete — `nko.transliterate` Canonical Engine

`nko/transliterate.py` — the canonical, unified transliteration engine for the N'Ko Unified Platform. Consolidates **6 scattered implementations** into one authoritative module.

NKO-1.5 Complete — Merge Morphology ✅

**Task:** Merge morphology — combine cross-script-bridge/morphology + keyboard-ai/morphological_engine **Status:** DONE **Date:** 2025-07-19

NKO-2.1 Complete — Swift Phonetics Port

**Task:** Port NKoPhonetics — IPA mappings, tone marks, N'Ko Unicode ranges to Swift **Status:** ✅ COMPLETE **Date:** 2026-02-19 **Build:** `swift build` ✅ | `swift test` ✅ (106 tests, 0 failures)

NKO-2.4 COMPLETE — NKoPrediction: Predictive Text Engine