Extracted abstract or opening context
\section{Cognitive Twin Synthesis: A Recursive Polymodal Framework for Autonomous Agent Identity from Conversational Corpora}\label{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 (\(\mathcal{V}_V\)), and temporal rhythms (\(\mathcal{V}_T\)). The corpus comprises 379,426 conversation turns spanning December 24, 2022 to March 18, 2026, collected across ChatGPT and Claude Code sessions, representing one of the largest known single-person conversational datasets used for cognitive modeling. We propose a 6-layer architecture --- the Living Executor --- progressing from knowledge ingestion (Journal), through voice replication (Mirror), meta-prompted identity (Conductor), multi-model consensus (Parliament), graduated autonomy (Apprentice), to decision-boundary modeling (Oracle). The central theoretical contribution is a formal extension of the RPS coherence energy functional \(\Phi(z; \mathcal{A}, \mathcal{T})\) to cognitive space, where cross-cognitive translators \(T_{n \leftarrow m}\) map between modalities and a proximal fixed-point iteration \(z^{(t+1)} = \mathcal{P}_\alpha(z^{(t)}; x)\) converges to a latent identity fixed point \(z^*\) --- a computational representation of the originator. The cognitive twin does not merely replicate voice; it maintains a persistent latent identity across sessions, makes decisions consistent with the originator's documented patterns, and earns autonomy through a formal graduation protocol. We define this protocol as the Autonomy Ratchet: a 4-level progression (Supervised, Spot-Checked, Directed, Autonomous) governed by a quality function \(Q(a) \in [0,1]\) with auto-pass threshold 0.85, human-review band \([0.60, 0.84]\), and auto-reject below 0.60.
Consider a system comprising 50+ deployed applications across 6 interconnected machines (Mac1--Mac5 plus a cloud-vm), 80+ operational skills, 54 Prefect automation flows, 5 storefronts, and a continuous integration pipeline that archives, signs, uploads, and submits iOS apps to TestFlight without human intervention. Every architectural decision, every priority call, every ``ship it or hold it'' judgment currently flows through a single person. This person is the fixed point around which th
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