Reasoning-NEST v2 — Gen-0 Pilot Corpus

100 densified metacognitive reflection records across seven error classes, produced by a tri-model frontier teacher pipeline (Gemini 3.1 Pro, GPT-4o, Claude Sonnet 4.6), released under CC-BY-4.0.
This dataset is the Gen-0 pilot corpus for Negentropic Self-Correction — a dual-system architecture for recursive language-model self-improvement that converts sparse runtime failures into dense causally-disentangled training records before any parametric update. It extends Metavolve Labs' prior work on data density (The Density Imperative) and recursive training collapse (The Entropy of Recursion).
Each record conforms to the Reasoning-NEST v2 schema (included in deposit) and contains: sparse event log, annotated chain-of-thought reconstruction with divergence index, Pearl-style Structural Causal Model graph (nodes + edges + LaTeX back-door adjustment), generalized avoidance rule, and provenance-anchored trust metadata.
Every record was produced by cross-family oracle consensus with Claude Sonnet judge arbitration. Cross-family divergence-index disagreement was observed at ~80% rate — a scientifically-meaningful finding indicating that multi-step reasoning failure attribution is fundamentally underdetermined.
See README.md for full methodology, citation, and production pipeline details.