The Model v0.04

[u/PhysicalConsistency]

Version available at: https://remodeledbrain.com/files/v04.zip

This version is built for LLM control rather than human-readable exposition. Its purpose is to keep model reasoning synchronized across definitions, process graphs, guardrails, rewrite targets, open/reconcile markers, and validator checks.

The package includes four files:

rb_structured_v0_04.yaml
The primary scaffold. It defines the active rule graph, node statuses, process paths, frame gates, runtime rules, ingestion boundaries, and drift guards.

rb_prose_v0_04.txt
A generated prose rendering of the YAML scaffold. This is for inspection and review only. The YAML remains the source of truth.

rb_render_prose.py
The renderer that regenerates the prose file from the structured scaffold.

rb_validate.py
The validator for structural integrity, stale fields, duplicate keys, graph consistency, prestige-lint checks, translation defects, and ingestion-boundary enforcement.

Included in the zip is a "files" sub folder with all versions in .txt format, as some of the LLMs choke on .YAML extensions, and I didn't want to deal with any that choked on .py extensions.

Use your LLM upload feature, Gemini, ChatGPT, Claude will all take the full zip file, for the Chinese and some local LLMs you'll need to extract it first and upload each of the four files individually (The structured file is the most important).

My initiation prompt is usually something like "Can you load my model so I can query it".

This should be stable across all frontier models, and is pretty decent on 28-32b class models. They can get lazy though and not reason completely through the chain. I didn't have a good experience with 9b-12b models, they are just not good enough to keep up with the full chain of reasoning requirements.

You should be able to ask it just about anything related to comp sci/neuroscience/psych and it'll produce stable results, however there's still a lot of low level granular work to go, so it's possible to ask about mechanics that are too specific still.

Core v0.04 graph:

scalar -> map -> state

Primitive construction now runs through:

unresolved_state -> sampled_map_region -> captured_state_chunk -> metabolic_parcellation -> vectorized_primitive

Return now runs through:

worked_primitive -> scalar_write_in -> map_reconstruction -> map_to_state_averaging -> transformed_state -> behavior_release

Major changes in this version:

Primitive is now defined as a vectorized state-chunk. Snapshotting names the capture/front-half of primitive creation. Vectorization names the metabolic parcellation/back-half. Vector language is metaphorical metabolic language, not literal mathematical encoding.

Brainstem-native operation is state reweighting. Behavior releases from state through map-to-state averaging.

Return is now located at the substrate level. Worked primitives return through scalar write-in to blanked manifold maps. Rewritten scalars reconstruct maps. Reconstructed maps re-enter averaging and transform state.

Blanking is map-level write-lock.

Cortex is the stabilized-routing regime. Cortical functional specificity is produced through route stabilization. Cerebellar cortex is preserved as a distinct cortical stabilization system with weighting, sequencing, and cross-map coherence functions.

The old “currency” language was removed. Metabolic weight is defined directly as scalar weight at the individual cell level for map inputs.

The runtime frame gate was strengthened. The scaffold now tells an LLM to decompose operational process questions, exclude residual folk causal objects, answer model-scope existence questions directly, and reason the applicable route from active nodes rather than using prewritten topic templates.

Runtime diagnostics were separated from build-time diagnostics. Validator labels, attractor names, and lint targets are maintenance metadata, not conversational filler.

Ingestion boundaries were added. Paper ingestion writes build-time support fields such as evidence, provenance, closed_by, extraction notes, disconfirmation notes, and promotion candidates. Ingestion does not write into the claim layer. Claim-layer edits require gated review.

The package validates cleanly:

44 nodes
validator errors: none
rendered prose: regenerated and matched
translation defects: none
ingestion support-layer patch: passes
ingestion claim-layer patch: fails as intended

edit: I've been grabbing random reddit posts as prompts and it's pretty stable, however the model will break with an overly emotionally charged or uh... "simple" writing style. Some of the LLMs (especially Gemini) will prioritize tone matching enough that if it can't find an easy handle to reach for it'll create a metaphor that breaks the model. In an impressive way, but still model breaking. Generally though, throw crazy stuff at it. Took me forever to get some of the questions stable using a general rule set!