Conflict Data: developing functional models that predict beneficial outcomes from conflict data. Not quite there yet but well organized and developing analytic capabilities (from relevant inferences) into “autovivified” persistence that, as a database, should be “completely there”. If you know, you know! Baby steps, but at today’s development rate, we should soon be absorbing large files (of conflict data). Producing beneficial structure will come next.

The most fun is seeing my largely-humanistic beliefs literally coded in .json and evoked as agentic output. The whole venture rests on evidence that there is intuition in LLMs, and that the semantic side can ultimately have everything language has: communication and naming definitions stored and shared either privately or publicly with elegance.

The structure is symbolic and the naming is entirely agentic, sometimes bordering on poetry. Though I had to pull the the LLM’s creativity back at times, the naming is superior, and clear as day.

Text below (like most here) is written by an LLM (except this) to build the analogic (semantic human side) of AI. At the bottom are “text trees” of the structure, which is impressive (if I say so myself).

Opus written text: The vivify-inferences pipeline ran end-to-end for the first time today — a local, zero-dependency system that turns raw semantic inference into an emergent, autovivified filesystem.

  • The full FABRIC chain is operational: vivify → secrecy → freeze → server — raw text in, structured JSON stored, master index updated
  • Fourteen inferences were processed across a live session covering FABRIC architecture, Darwinian moral evolution, chunking and RAG comprehension gaps, multi-model AI convergence, and writing in reverse
  • The inference corpus is public at github.com/robolawyer-tm/vivify-inferences — the first demonstration of the vivify media form

What vivify does — each inference is reduced to 8-12 semantic keyword clumps by a left-LLM pass, stored as autovivified JSON, and categorized by a co-occurrence graph that emerges purely from the data with no external taxonomy.

What emerged from the first 14 inferences — categories like autovivification/beneficial_outcome_modeling, analogical_religion/api_output, and adaptive_equilibrium formed without being named in advance. The system grouped its own architecture alongside Darwin, Hegel, and constructivism — because the keywords converged.

The new media form — an inference corpus is not a document, a dataset, or a knowledge base. It is a living semantic record where structure is discovered rather than imposed, and every entry is food for the next inquiry rather than a conclusion.

Two new pillars documented — WRITING_IN_REVERSE.md formalizes the constructivist inquiry model where final learning precedes its evidence back to the originating question. MULTI_MODEL_CONVERGENCE.md proposes collecting intermediate embeddings from every model in a workflow and feeding the full accumulation to the final model — a round-robin that halts when output becomes a stable platform rather than a fixed answer.

The pipeline is rough — the category explosion problem is real, the synonym map is manual, and the tension scores are uniformly high with only 14 inferences. All of that is expected on a shakedown cruise. Nothing broke that wasn’t supposed to.

Filesystem structure

vivify-inferences
├── categorize.py
├── config
│   ├── invariants.json
│   ├── pipeline.json
│   └── synonyms.json
├── freeze.py
├── inferences
│   ├── adaptive_equilibrium
│   │   └── api_output
│   │       └── inf_72711662.json
│   ├── agentic_self_evolution
│   │   └── api_output
│   │       └── inf_0bc11447.json
│   ├── ai_workflow_comprehension
│   │   └── api_output
│   │       └── inf_58190c7d.json
│   ├── analogical_religion
│   │   └── api_output
│   │       └── inf_c8e1ac73.json
│   ├── autovivification
│   │   ├── adaptive_equilibrium
│   │   │   └── inf_72711662.json
│   │   ├── agentic_self_evolution
│   │   │   └── inf_0bc11447.json
│   │   ├── analogical_religion
│   │   │   └── inf_c8e1ac73.json
│   │   └── api_output
│   │       ├── inf_03b9a985.json
│   │       ├── inf_0801b607.json
│   │       ├── inf_1d767cb6.json
│   │       ├── inf_682bab80.json
│   │       ├── inf_910ee0e9.json
│   │       ├── inf_a636d323.json
│   │       └── inf_c17433c3.json
│   ├── index.json
│   └── unclustered
├── lib
│   ├── inference.py
│   ├── keyword_graph.py
│   ├── __pycache__
│   │   ├── inference.cpython-312.pyc
│   │   ├── keyword_graph.cpython-312.pyc
│   │   └── vivify_core.cpython-312.pyc
│   └── vivify_core.py
├── __pycache__
│   └── freeze.cpython-312.pyc
├── right_pass.py
├── secrecy.py
├── server.py
├── tension_score.py
├── tests
└── vivify.py

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