Graph-Augmented Recursive Language Models for Personal Knowledge Systems
The cognitive twin work treats a long interaction history as a cognitive fingerprint: a record of how someone asks, builds, revises, and reasons. The research question is how to distill that record into a model and retrieval system without flattening away personal structure.
Paper workspace
Live draft structure
Artifacts
Draft PDF
Cognitive twin draft PDF. Public page should avoid exposing private training data.
Open artifactEditable source
Draft PDF exists, but release requires a privacy pass over corpus details and training claims.
Source anchors
cognitive-twin-research-paper.tex
cognitive-twin-theorems.tex
Comp-Core/packages/cognitive-twin/paper/cog-rlm-paper.md
Method tags
Ingest intersections
Status
Paper plus theorems document drafted; SFT pipeline operational.
Key claims
01
A person's interaction history contains trainable cognitive structure.
02
Theorems and pipeline documents separate the conceptual claim from the SFT machinery.
03
Privacy and release boundaries matter more here than in ordinary papers.
Public reading note
Public abstract is safe; full corpus and training details remain private.
Standard skeleton
What this paper must keep proving
problem
Personal AI loses structure when conversation history is treated as raw text rather than a graph of recurring cognition.
method
Distill interaction history into a graph-augmented model and companion theorems.
implementation
Conversation extraction, graph construction, SFT pair generation, theorem document.
data
Private interaction corpus. Public work should only expose aggregated methods and privacy-safe findings.
evaluation
Behavioral fidelity, retrieval grounding, privacy boundaries, and personal reasoning reconstruction.
references
Personalization, cognitive modeling, graph-augmented LMs, SFT/DPO training.
openQuestions
Which results can be made public without leaking private corpus structure.
Checkpoints and references
Proof chain
Claim checkpoint
central-claim slot
Every central claim must point to a proof anchor or remain labeled as speculative.
Implementation checkpoint
implementation-map slot
Every method should identify the code path, harness, schema, or protocol that embodies it.
Evidence checkpoint
evidence-manifest slot
Every reported result should point to run IDs, packet IDs, data snapshots, commits, or review artifacts.
Reference checkpoint
references slot
Every external claim should resolve to a cited paper, benchmark, standard, or documented prior system.
Release checkpoint
release-gate slot
Every PDF needs a named condition before it can move from draft to citation-ready.