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ARC-Neuron-LLMBuilder — Model Governance Grinder Plan

Document status: Planning / roadmap draft
Target repo: ARC-Neuron-LLMBuilder
Purpose: Define a realistic path for turning ARC-Neuron-LLMBuilder into a governed model-evaluation, model-output filtering, and verified dataset-generation system.

1. Plain-English Summary

ARC-Neuron-LLMBuilder should not claim that it can magically “extract intelligence” from external models.

The realistic goal is stronger and more defensible:

ARC-Neuron-LLMBuilder treats external models as raw reasoning engines, then verifies, logs, scores, archives, rejects, or promotes their outputs through deterministic governance.

In other words:

External model goes in
        ↓
ARC gives it controlled tasks
        ↓
Model outputs code, plans, data, tests, or explanations
        ↓
ARC validates the output
        ↓
ARC writes receipts and archives evidence
        ↓
Only passing outputs become training data, patches, or promoted build material

This is the real “meatgrinder”: not magic intelligence extraction, but strict controlled conversion of model output into verified artifacts.

2. What This System Is

ARC-Neuron-LLMBuilder can become a governed local cognition builder with these roles:

  1. Model Adapter Host
    Connects to local or external model runners such as llama.cpp, Ollama, vLLM, or OpenAI-compatible APIs.

  2. Task Orchestrator
    Gives models controlled tasks such as repo audit, patch generation, test creation, documentation repair, log analysis, or dataset generation.

  3. Verification Grinder
    Checks outputs through syntax checks, unit tests, validators, lint rules, policy rules, benchmark tasks, and repo-specific constraints.

  4. Receipt Ledger
    Stores exactly what happened: prompt, model, context, output, patch, validator result, score, hash, timestamp, and final decision.

  5. Promotion Engine
    Promotes only validated outputs into accepted artifacts, training datasets, candidate builds, or future model-improvement material.

  6. Failure Archive
    Keeps failed model outputs as useful negative examples, regression evidence, or future training material.

3. What This System Is Not

This project should avoid public claims like:

  • “Extracts intelligence from any model”
  • “Turns Llama into Jarvis automatically”
  • “Controls the internal experts of MoE models”
  • “Makes huge models run without hardware requirements”
  • “Automatically produces AGI”
  • “Guarantees hallucination-free output”
  • “Fully self-improves without human review”

A safe public claim is:

ARC-Neuron-LLMBuilder is an open-source governed local model-building framework for evaluating model outputs, recording receipts, generating verified datasets, and promoting only validated improvements.

4. Core Pipeline

4.1 Input Sources

The system can accept:

  • Local source files
  • Repo documentation
  • Error logs
  • Test failures
  • User instructions
  • Prior model outputs
  • Benchmark prompts
  • Operator review notes
  • External model responses
  • Generated patches
  • Generated training samples

4.2 Model Execution

Each model should be treated as an interchangeable candidate brain.

Supported adapter targets:

llama.cpp / llama-cli
Ollama
vLLM
OpenAI-compatible local endpoint
Manual import from cloud model output
Future GGUF runners

Every model call should produce a structured record:

{
  "run_id": "...",
  "model_name": "...",
  "model_adapter": "llama.cpp",
  "prompt_hash": "...",
  "context_hash": "...",
  "output_hash": "...",
  "timestamp": "...",
  "task_type": "repo_audit|patch|test_generation|dataset_generation|explanation",
  "operator": "local_user",
  "status": "pending_validation"
}

4.3 Verification Layer

Outputs should not be trusted by default.

They should pass through checks such as:

Python syntax check
JSON/YAML validation
Markdown local-link validation
Repo validator script
Unit tests
Targeted smoke tests
Patch dry-run
Security scan
Dependency sanity check
Benchmark scoring
Operator review

A generated patch is not accepted because the model sounds confident. It is accepted only if evidence supports it.

4.4 Decision Layer

Every output gets one of these decisions:

PROMOTE       — passes checks and becomes accepted material
REJECT        — fails checks and is archived
HOLD          — promising but needs human review
RETRY         — model should attempt again with failure evidence
QUARANTINE    — unsafe, malformed, destructive, or suspicious

5. “Meatgrinder” Modes

Mode A — Repo Audit Grinder

Purpose: convert model reasoning into verified repo-improvement reports.

Flow:

Select repo or folder
        ↓
Chunk files safely
        ↓
Ask model to audit each chunk
        ↓
Normalize findings into structured issues
        ↓
Deduplicate findings
        ↓
Verify findings against actual files
        ↓
Emit audit report + receipts

Output examples:

  • AUDIT_REPORT.md
  • audit_receipts/*.jsonl
  • verified_findings.json
  • false_positive_archive.jsonl

Mode B — Patch Grinder

Purpose: use external models to propose code/doc patches, but only accept verified patches.

Flow:

Issue selected
        ↓
Model proposes patch
        ↓
Patch applied to temp workspace
        ↓
Tests and validators run
        ↓
Diff is scored
        ↓
Passing patch becomes candidate
        ↓
Operator approves or rejects

Required safeguards:

  • Never patch directly into live repo without staging.
  • Always preserve original files.
  • Always emit diff receipts.
  • Always run validators after patching.
  • Always archive failed attempts.

Mode C — Dataset Grinder

Purpose: convert verified model outputs into future training data.

Flow:

Task + context + model answer
        ↓
Validation checks
        ↓
Operator review if needed
        ↓
Accepted answer becomes training sample
        ↓
Rejected answer becomes negative sample

Possible dataset formats:

{
  "instruction": "Audit this Python function for runtime risks.",
  "input": "...source code...",
  "accepted_output": "...verified response...",
  "rejected_output": "...failed response...",
  "evidence": {
    "tests_passed": true,
    "validator_passed": true,
    "operator_approved": true
  }
}

This is the real path toward “extracting value” from a stronger model: not by copying its weights, but by collecting verified examples that a smaller local model can learn from.

Mode D — Multi-Model Battle Grinder

Purpose: compare multiple models on the same task.

Flow:

Same task sent to Model A, Model B, Model C
        ↓
Outputs normalized
        ↓
Validators run
        ↓
Scores assigned
        ↓
Best output promoted
        ↓
Failures archived

Scoring categories:

Correctness
Test pass rate
Patch minimality
Security risk
Runtime safety
Documentation quality
Repo-style compliance
Operator preference

This creates a practical “brain comparison” system without claiming magical internal model control.

6. Llama 4 / Large Model Integration Reality Check

Large models such as Llama 4 Scout, Llama 4 Maverick, Qwen, DeepSeek, Gemma, or future GGUF models can be useful as reasoning engines.

However:

  • The system cannot directly control internal MoE experts unless the runtime exposes that capability.
  • Chunking prompts does not remove the need to load model weights somewhere.
  • Long context helps, but does not guarantee accurate reasoning.
  • Large models still hallucinate.
  • ARC must verify outputs before promotion.

A realistic claim:

Large models can provide high-quality candidate reasoning. ARC-Neuron-LLMBuilder can govern that reasoning through receipts, tests, validation, and promotion rules.

7. Proposed Repo Structure

Suggested additions:

arc_neuron/
  adapters/
    base.py
    llama_cpp_adapter.py
    ollama_adapter.py
    openai_compatible_adapter.py
  grinder/
    task_schema.py
    run_model_task.py
    normalize_output.py
    verify_output.py
    score_output.py
    promote_candidate.py
    archive_failure.py
  receipts/
    receipt_schema.py
    receipt_writer.py
    hash_utils.py
  datasets/
    sample_schema.py
    build_verified_dataset.py
  battles/
    compare_models.py
    scorecard.py
  safety/
    patch_sandbox.py
    destructive_change_guard.py
    secret_scan.py
  cli/
    grinder_cli.py

Suggested docs:

docs/MODEL_GOVERNANCE_GRINDER.md
docs/MODEL_ADAPTERS.md
docs/RECEIPT_SCHEMA.md
docs/VERIFIED_DATASET_PIPELINE.md
docs/MULTI_MODEL_BATTLE_MODE.md
docs/SAFETY_AND_PROMOTION_RULES.md

8. Minimum Viable Implementation

The first real version should be intentionally small.

v0 — Manual Output Import

Goal: allow outputs from any model to be pasted/imported and scored.

Features:

  • Import a model answer from .txt or .md
  • Attach task metadata
  • Run validators
  • Write receipt
  • Mark as accepted/rejected/hold

Why this matters:

This works before local Llama integration exists.

v1 — Local Model Adapter

Goal: connect to one local runner.

Recommended first adapter:

llama.cpp / llama-cli

Features:

  • Send prompt to local model
  • Capture response
  • Hash prompt/context/output
  • Save receipt
  • Run simple validator

v2 — Patch Sandbox

Goal: allow model-generated patches without risking the repo.

Features:

  • Copy repo to temp workspace
  • Apply generated patch
  • Run validators/tests
  • Generate diff report
  • Promote only if checks pass

v3 — Verified Dataset Builder

Goal: generate supervised training data from accepted outputs.

Features:

  • Export accepted examples
  • Export rejected examples
  • Include evidence metadata
  • Produce JSONL training dataset
  • Include provenance hash chain

v4 — Multi-Model Battle Mode

Goal: compare multiple models on the same tasks.

Features:

  • Same prompt sent to multiple adapters
  • Outputs scored through same validators
  • Best candidate promoted
  • Scorecard saved
  • Failure archive preserved

9. Safety Rules

The system should enforce these rules:

  1. Never trust model output directly.
  2. Never modify source without a sandbox or backup.
  3. Never promote without evidence.
  4. Never hide failed attempts.
  5. Never overwrite receipt history.
  6. Never treat confidence as correctness.
  7. Never claim autonomous improvement unless tests prove the improvement.
  8. Never let an external model become the authority layer.

The model is a worker. ARC is the governor.

10. Receipt Fields

Every task should emit a receipt with at least:

{
  "receipt_version": "1.0",
  "run_id": "uuid",
  "task_id": "uuid",
  "task_type": "patch_generation",
  "model": {
    "name": "example-model",
    "adapter": "llama.cpp",
    "quantization": "Q4_K_M",
    "context_window": 8192
  },
  "inputs": {
    "prompt_hash": "sha256:...",
    "context_hash": "sha256:...",
    "files_hash": "sha256:..."
  },
  "outputs": {
    "raw_output_hash": "sha256:...",
    "normalized_output_hash": "sha256:...",
    "patch_hash": "sha256:..."
  },
  "validation": {
    "syntax_passed": true,
    "repo_validator_passed": true,
    "tests_passed": false,
    "security_scan_passed": true
  },
  "decision": "REJECT",
  "reason": "Unit tests failed after patch application.",
  "timestamp_utc": "2026-05-05T00:00:00Z"
}

11. Public Positioning

Recommended README language:

ARC-Neuron-LLMBuilder is a governed local model-building framework that treats model outputs as candidates, not truth. It records receipts, validates generated artifacts, compares candidate brains, archives failures, and promotes only verified improvements into future builds or datasets.

Avoid:

ARC extracts intelligence from frontier models.

Use instead:

ARC converts model output into verified artifacts through validation, receipts, scoring, and promotion rules.

12. Long-Term Vision

The mature version of this system becomes a local-first AI build loop:

External model / local model / human operator
        ↓
Task orchestration
        ↓
Candidate generation
        ↓
Validation grinder
        ↓
Receipts and audit trail
        ↓
Verified dataset growth
        ↓
Fine-tuned local GGUF candidate
        ↓
Benchmark comparison
        ↓
Promotion or rejection
        ↓
Next governed brain

This is the honest path toward a self-improving local AI workshop.

The project should not claim that the machine already has a finished brain.

The stronger claim is:

The machine now has the governance path required to repeatedly grow a better brain without losing truth, continuity, or control.

13. Next Engineering Steps

Immediate next steps:

  1. Add this document to docs/MODEL_GOVERNANCE_GRINDER_PLAN.md.
  2. Add a minimal receipt schema.
  3. Add manual model-output import.
  4. Add validator runner against imported output.
  5. Add accepted/rejected archive folders.
  6. Add local llama.cpp adapter.
  7. Add patch sandbox mode.
  8. Add verified dataset exporter.
  9. Add multi-model comparison CLI.
  10. Add README section describing model governance without overclaiming.

14. Recommended First CLI Commands

Possible future CLI shape:

arc-neuron grinder import-output \
  --task docs/tasks/audit_readme.json \
  --model-name manual-gpt-output \
  --output outputs/readme_audit.md
arc-neuron grinder run-local \
  --adapter llama.cpp \
  --model models/qwen.gguf \
  --task docs/tasks/audit_repo.json
arc-neuron grinder verify \
  --run-id RUN_ID \
  --validators validate_repo,python_syntax,markdown_links
arc-neuron grinder promote \
  --run-id RUN_ID \
  --decision PROMOTE
arc-neuron dataset export-verified \
  --out datasets/verified_arc_training.jsonl

15. Final Doctrine

The external model is not the authority.

The model proposes.
ARC verifies.
The receipt remembers.
The validator decides.
The operator can override.
Only evidence promotes.