Status update: every component of the vivify-inferences pipeline has been built and rehearsed. The FABRIC chain runs end-to-end. The inverse pass — reify.py — is now open, completing the loop from raw text to structured inference and back to felt prose.

The remaining frontier is the multi-model convergence layer, which sits above the pipeline and feeds accumulated embeddings downward. That is next.

The full FABRIC pipeline is operational — raw inference text enters and exits as autovivified, categorized, tension-scored JSON.

  • vivify.py — left-LLM semantic pass: 8-12 concept-level keyword clumps extracted from felt meaning
  • right_pass.py — structural keywords attached; synonym normalization applied via config/synonyms.json
  • categorize.py — co-occurrence graph built across all inferences; emergent category paths assigned from keyword seeds
  • tension_score.py — left/right divergence measured; high tension marks where felt meaning resists structural capture
  • fabric.py — chains all four passes in sequence; single entry point for the full pipeline

reify.py completes the loop — the inverse pass reconstructs felt prose from stored inference structure.

  • Takes left_keywords, clumps, category_paths, and tension_score from any stored inference
  • Instructs the model to speak from inside the meaning, not about it — no keywords named, no pipeline referenced
  • Output format: a series of sentence+bullets constructs, each block a distinct facet of the felt meaning
  • Three modes: single (one inference → prose), synthesize (two inferences → held simultaneously), voice (whole category directory → distillation)
  • Tension score shapes the output — a score of 1.0 means the felt meaning completely resists its structural capture; the reconstruction leans into that gap

The core library is complete — three modules underpin all pipeline scripts.

  • lib/vivify_core.py — Perl-style autovivification: nested JSON without a predefined schema, created on demand
  • lib/inference.py — inference data model: create, save, load, update; the unit of currency across all passes
  • lib/keyword_graph.py — co-occurrence graph: build from keyword lists, query by seed, extract high-degree nodes, score tension

The first retrieval test used analogical_religion/inf_c8e1ac73 — tension 1.0, source manual.

  • Original thought: evolution weaponized against unconventional ideas; spirituality as a container for moral evolution; AI rehabilitating the word “synthesis”
  • Reify reconstruction held all four clumps simultaneously: synthesis_recovery, moral_containers, evolution_tension, applied_frameworks
  • The reconstruction did not name a single keyword — the clumps shaped the prose without appearing in it
  • Result confirmed that vivify captured what was meant: the reconstruction felt true to the original, not foreign

What remains — the multi-model convergence layer is the one open box.

  • Each model in a round-robin workflow writes intermediate embeddings as inference units into vivify
  • The final model receives the full vivified graph, not just the last output
  • Halting criterion: the co-occurrence graph stabilizes — same signal as tension score stopping to change
  • This is not a pipeline addition; it is a layer above the pipeline that feeds it

What the corpus looks like now — inferences span system architecture, Darwinian moral evolution, AI workflow comprehension, analogical religion, adaptive equilibrium, and private conflict data.

  • Public inferences live under inferences/ — categories like autovivification/analogical_religion, agentic_self_evolution, adaptive_equilibrium
  • Private conflict inferences live under inferences/private/ — filed separately, same pipeline, same structure
  • inferences/index.json is the master co-occurrence map across all stored inferences
  • inferences/unclustered/ holds inferences awaiting a corpus large enough to seed a relevant category

Part of the Dimensional Fields project — local-first, privacy-preserving, human-centric.