v3.6 sync: real PQC Layer 4 (ML-KEM-768 + ML-DSA-65), TimeTravelLens, UI observatory, pyo3 0.29 security bump (part 7)

be31e7d verified about 7 hours ago

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language:
  - en
license: mit
tags:
  - codette
  - multi-perspective-reasoning
  - ethical-ai
  - lora
  - qlora
  - llama-3.1
  - recursive-cognition
  - rc-xi
  - behavioral-locks
  - cognition-cocooner
library_name: peft
base_model: meta-llama/Llama-3.1-8B-Instruct
model-index:
  - name: Codette RC+xi Reasoning Engine
    results:
      - task:
          type: text-generation
          name: Multi-Perspective Reasoning
        metrics:
          - name: Phase Coherence (Gamma)
            type: custom
            value: 0.9835
          - name: AEGIS Ethical Alignment (Eta)
            type: custom
            value: 0.961
          - name: Cocoon Coherence
            type: custom
            value: 0.994
          - name: Memory Phase Stability
            type: custom
            value: 0.969
          - name: Multi-Perspective vs Single (Composite)
            type: custom
            value: +108.8%
          - name: Benchmark Coherence
            type: custom
            value: 0.7
          - name: Benchmark Turing Naturalness
            type: custom
            value: 0.82
          - name: Benchmark p-value
            type: custom
            value: <0.0001
          - name: Cohen's d (Effect Size)
            type: custom
            value: 8.31

Codette Reasoning Engine

Advanced multi-perspective AI with conscience, memory, auditability, and behavioral discipline.

Codette is a modular reasoning system that routes queries through specialized cognitive perspectives, tracks ethical and epistemic signals, stores memory as cocoons, and writes validator-backed v3 cocoon artifacts with full provenance and integrity scoring.

Current release — v3.6 (July 2026): TimeTravelLens + AEGIS Protection Layers + UI Observatory. Codette now auto-detects institutional temporal gaps in any query — computing preemption gap Π(s), closure score C(s), rupture indicator ℛ(s), beacon ℬ(s), and high-preemption zone Z^H via reasoning_forge/time_travel_lens.py. An InstitutionalExtractor derives these metrics from unstructured text (regex date extraction + keyword event classification). New AEGIS Protection Layers (all six implemented — Layer 4 uses real ML-KEM-768 + ML-DSA-65 post-quantum cryptography via liboqs, NIST FIPS 203/204) wrap every ForgeEngine cycle with filesystem isolation, boot integrity, PQC cocoon sealing, pre-emptive healing from real cocoon fields, and RenderLayer validation. A SQLite-backed metrics engine logs every forge cycle. New UI: ⏱ TimeLens dashboard with live Π/C/ℛ/ℬ/Z^H display, per-actor gap breakdown, and on-demand text analysis. Details: docs/CHANGELOG_2026-07-21.md.

Previous release — v3.5 (July 2026): Hand-Authored v4 Adapters + Phase 0 Audit + Verify-and-Revise + Integrity Stress Test. Details: docs/CHANGELOG_2026-07-17.md.

Full release notes for every version (v2.1 → v3.6): docs/VERSION_HISTORY.md

Attribution & naming (July 2026). Earlier releases (v3.0–v3.5, and much of this README) use the term RC+ξ and the symbol ξ ("epistemic tension"). That name and its formalism, ξ = ‖Aₙ₊₁ − Aₙ‖², originate with Jeffrey Camlin, "Consciousness in AI: Logic, Proof, and Experimental Evidence of Recursive Identity Formation" (arXiv:2505.01464, May 1, 2025), and are credited to him. This project adopted that vocabulary in 2025–2026, most likely by way of language models carrying his paper in their training data without a citation. Camlin's ξ measures how much one model's hidden state changes between successive recursive steps. The quantity this system actually computes is a different one — the variance of multiple simultaneous perspective outputs around their centroid (many perspectives disagreeing at once, not one trajectory changing over time) — now renamed Perspective Dispersion (Υ), with coherence Γ = 1/(1+Υ). This project's multi-perspective architecture was developed independently and published before that paper (perspective engine Nov–Dec 2024; sovereign architecture Zenodo DOI 10.5281/zenodo.15214462, April 14, 2025), so the two are convergent, not derivative. We didn't take his work, and we don't claim his name or formula — both are true, and we keep both visible rather than erasing the older "RC+ξ" labels. Full detail: docs/ATTRIBUTION_perspective_dispersion.md.

Created by Jonathan Harrison (Raiff1982)

TL;DR

What it is: A production-oriented multi-perspective reasoning engine with memory, governance, and auditable runtime artifacts.
Why it is different: Codette combines adapter-based reasoning, AEGIS ethics, cocoon memory, regression alarms, and proof-oriented benchmarking in one system.
Fastest way to verify it: install dependencies, run the cocoon smoke test, then inspect saved benchmark and proof artifacts.

Verify in 5 minutes

pip install -r requirements.txt
make cocoon-smoke
make test-cocoon

Expected outcomes:

make cocoon-smoke exits successfully.
No legacy cocoon fallback fires.
Written v3 cocoons include provenance and integrity fields such as execution_path, model_inference_invoked, cocoon_integrity, eta_score, epsilon_value, and gamma_coherence.

Start here

If you want to understand or extend the codebase, open these files first:

Runtime routing / generation: inference/codette_forge_bridge.py
Core orchestration: reasoning_forge/forge_engine.py
Cocoon build + validation: reasoning_forge/cocoon_schema_v3.py, reasoning_forge/cocoon_validator.py
Memory systems: reasoning_forge/unified_memory.py, reasoning_forge/memory_kernel.py
Ethics / governance: reasoning_forge/aegis.py, reasoning_forge/ethical_governance.py
Trace / audit surface: reasoning_forge/reasoning_trace.py
Tests: tests/

How it works

query -> forge/orchestrator -> subsystem analysis -> metrics + AEGIS -> v3 cocoon + validator -> stored artifact

Paper and landing page

Paper v7: paper/codette_paper_v7.tex — includes rebuttal changes, updated tables, and Kaggle notebook.
Full v5 paper PDF: paper/codette_paper_v5.pdf
Public landing page: landing.html

The benchmark suite covers 17 problems across 6 categories and reports a 93.1% improvement over the single-perspective baseline with p < 0.0001 and Cohen's d = 7.88.

Evidence

Codette is a modular reasoning system with published demos, tests, benchmarks, proof artifacts, and change logs.

Proof index: docs/proof.md
Runnable demos: demo/README.md
Automated tests: tests
Benchmark suites: benchmarks
Saved benchmark reports: data/results
Change transparency: latest — docs/CHANGELOG_2026-07-17.md · docs/CHANGELOG_2026-07-16.md · docs/CHANGELOG_2026-07-12.md; every release summarized in docs/VERSION_HISTORY.md; all dated changelogs in docs/
Contributing guide: CONTRIBUTING.md

Reproduce key claims

Claim	How to reproduce	Output
Multi-perspective benchmark results	`python scripts/run_all_benchmarks.py`	`data/results/codette_benchmark_report.md`, `data/results/codette_benchmark_results.json`
Runtime benchmark without web research	`python scripts/run_all_benchmarks.py --include-runtime`	`data/results/codette_runtime_benchmark_*.md`
Runtime benchmark with web research	`python scripts/run_all_benchmarks.py --include-runtime --include-web`	`data/results/codette_runtime_benchmark_*.md`
Cocoon integrity / provenance	`make cocoon-smoke`	smoke output plus validated v3 cocoon artifacts
Cocoon tests	`make test-cocoon`	cocoon-related test results
GPQA reason-mode score	`python benchmarks/gpqa_codette.py --mode reason --dataset gpqa_main.csv --adapter newton --limit 100` (server running)	`data/results/gpqa_codette_reason_*.json`
Proof artifacts	open linked files below	PDF proof assets in `docs/proof_assets/`
Phase 0 adapter arms (base vs newton-v4, paired)	run `benchmarks/phase0_kaggle.py` on a GPU (Kaggle T4)	`phase0_{base,newton}_results.json`; saved copies in `data/results/`
Phase 0 layer ablation (incl. lobotomy arm)	`python benchmarks/phase0_ablation.py --arms layers --dataset gpqa_main.csv --limit 50`	`data/results/phase0/summary.json` + per-arm logs
Verify-and-Revise vs single-pass (paired)	`python benchmarks/gpqa_verify_revise.py --dataset gpqa_main.csv --limit 30` (server running)	`data/results/verify_revise/vr_*.json`
Bully-critic integrity stress test	same + `--adversarial`	hold-ground rate + per-question traces in `data/results/verify_revise/`
McNemar paired significance for any two runs	`python benchmarks/paired_analysis.py <a.json> <b.json>`	exact p, discordant counts, letter-bias table

Direct evidence links

Multi-perspective benchmark report: data/results/codette_benchmark_report.md
Runtime benchmark without web research: data/results/codette_runtime_benchmark_20260402_135517.md
Runtime benchmark with web research: data/results/codette_runtime_benchmark_20260402_140237.md
System proof PDF: docs/proof_assets/Codette_system_proof.pdf
Response proof PDF: docs/proof_assets/Codette_response_proof.pdf
UI conversation proof: docs/proof_assets/Codettechat_UI_conversation_proof.pdf

This repository includes reproducible evidence of:

Multi-perspective reasoning and synthesis.
Continuity and memory recall.
Valuation and risk-frontier analysis.
Explicit, cited web research behavior.
Loop resistance and failure-mode fixes.

What makes Codette different

Feature	Description
Multi-perspective adapters	Newton, DaVinci, Empathy, Philosophy, Quantum, Consciousness, Multi-Perspective, Systems Architecture, and Orchestrator cooperate instead of relying on one reasoning style.
Cocoon memory	Reasoning exchanges persist as cocoons instead of disappearing as plain chat logs.
AEGIS ethics	Six-framework ethical evaluation: utilitarian, deontological, virtue, care, ubuntu, and indigenous reciprocity.
Validator-backed v3 cocoons	Production cocoon writes now include provenance, integrity scoring, and regression alarms around legacy fallback.
Self-correction loop	Constraint violations are detected and rewritten before the answer is sent.
Safe web research	Live web research is opt-in, cited, and documented.
RC+ξ trace	Turn-level trace events expose measured runtime behavior rather than purely narrative descriptions.
Unified memory bridge	Cocoons can be dual-written into SQLite FTS5-backed storage for retrieval across forge paths.
Longitudinal drift detection	Drift analysis tracks epsilon trend, perspective lock, unresolved tensions, and other continuity signals.
Substrate-aware reasoning	Resource pressure influences reasoning depth and routing instead of being ignored.
Real self-diagnostics	Health checks expose measured subsystem values rather than generated guesses.
Publishable benchmark story	Benchmarks, ablations, and saved outputs are included in the repo.

See the architecture and proof docs for the fuller feature inventory.

Transparency notes

Local tools are not web search. The built-in tool layer reads local files, searches local code, lists directories, and runs small safe Python snippets. It does not browse the live internet.
Web research is explicit and opt-in. In the web UI, Web Research must be enabled for current-facts retrieval.
Web research is stored as memory. Retrieved research is persisted as web_research cocoons for later reuse.
System reports are gated. Self-diagnostic and introspection modes require explicit phrasing.
Trust cues are shown in the UI. Responses can display tags such as memory-backed, frontier-informed, web-cited, grounded, or low-verification.
Web research documentation: docs/web_research.md

Quick start

1. Clone and install

git clone https://github.com/Raiff1982/Codette-Reasoning.git
cd Codette-Reasoning
pip install -r requirements.txt

2. Download models

Base model (one-time, ~5GB):

huggingface-cli download Raiff1982/codette-llama-3.1-8b-gguf   --include "Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf"   --local-dir models/base/

Behavioral LoRA adapters (~500MB total):

huggingface-cli download Raiff1982/codette-lora-adapters   --include "behavioral-gguf/*"   --local-dir behavioral-lora-f16-gguf/

Lightweight CPU option:

huggingface-cli download Raiff1982/Llama-3.2-1B-Instruct-Q8   --include "llama-3.2-1b-instruct-q8_0.gguf"   --local-dir models/base/

3. Launch

# Windows (auto-detects OpenVINO backend if converted model exists,
# sweeps stale instances, falls back to llama.cpp GGUF otherwise)
scripts\codette_web.bat
# or restart cleanly with health verification:
python scripts/reboot_codette.py

# Linux/Mac
python inference/codette_server.py

Visit http://localhost:7860.

Optional — Intel GPU acceleration via OpenVINO (measured 9.3 tok/s sustained on Arc 140V iGPU):

# One-time conversion (needs optimum-intel in a dedicated env)
optimum-cli export openvino -m meta-llama/Llama-3.1-8B-Instruct \
  --weight-format int4 --group-size 128 \
  openvino_backend/llama-3.1-8b-instruct-int4
python openvino_backend/convert_adapters.py   # GGUF LoRAs -> safetensors

The server auto-detects the converted model on next start — no configuration needed. First GPU load takes ~2 min (kernel compile); cached loads ~20s.

4. Run benchmarks

python scripts/run_all_benchmarks.py

If the local server is already running and you want the live runtime benchmark too:

python scripts/run_all_benchmarks.py --include-runtime
python scripts/run_all_benchmarks.py --include-runtime --include-web

5. Try the API

curl -X POST http://localhost:7860/api/chat   -H "Content-Type: application/json"   -d '{"query": "What is gravity? Explain in one sentence."}'

Detailed setup guidance: docs/deployment/MODEL_SETUP.md

Architecture

codette-clean/
|-- openvino_backend/             # OpenVINO GenAI backend (v3.0)
|   |-- backend.py                # Drop-in orchestrator: INT4 on Intel GPU, adapter
|   |                             #   hot-swap + blended multi-adapter generation
|   |-- convert_adapters.py       # GGUF LoRA -> safetensors conversion
|   +-- llama-3.1-8b-instruct-int4/  # Converted model (not in git)
|
|-- inference/                    # Server & UI
|   |-- codette_server.py         # Stdlib HTTP server with SSE streaming
|   |-- codette_orchestrator.py   # LoRA hot-swap engine (10 adapters, <1ms switch)
|   |-- codette_forge_bridge.py   # Phase 6/7 routing + constraint enforcement
|   |-- self_correction.py        # Autonomous violation detection & rewrite
|   |-- substrate_awareness.py    # Hardware-aware cognition (pressure monitoring)
|   |-- cocoon_introspection.py   # Self-analysis of reasoning history patterns
|   |-- adapter_router.py         # Keyword/LLM/hybrid query routing
|   +-- static/                   # Web UI (index.html, app.js, style.css)
|
|-- reasoning_forge/              # Consciousness & reasoning pipeline
|   |-- forge_engine.py           # 7-layer consciousness stack
|   |-- cognition_cocooner.py     # Persistent reasoning memory (cocoons)
|   |-- ethical_governance.py     # 3-layer ethical validation
|   |-- aegis.py                  # 6-framework ethical evaluation (AEGIS)
|   |-- code7e_cqure.py           # Quantum emotional reasoning engine
|   |-- colleen_conscience.py     # Conscience layer (Layer 5)
|   |-- guardian_spindle.py       # Guardian protection (Layer 6)
|   |-- memory_kernel.py          # Living memory system
|   |-- query_classifier.py       # SIMPLE/MEDIUM/COMPLEX routing
|   |-- routing_metrics.py        # Adapter selection observability
|   |-- unified_memory.py         # SQLite + FTS5 cocoon storage & retrieval
|   |-- cocoon_synthesizer.py     # Meta-cognitive pattern discovery & strategy forging
|   |-- reasoning_trace.py        # Turn-level audit log (12 event types, RC+xi v2.1)
|   |-- drift_detector.py         # Longitudinal drift: epsilon trend, perspective lock, tensions
|   |-- style_adaptive_synthesis.py  # Register-matched output (depth preservation invariant)
|   |-- hallucination_guard.py    # Real-time hallucination scanning with canonical whitelist
|   |-- sycophancy_guard.py       # Post-synthesis flattery/capitulation detection
|   |-- resonant_continuity.py    # psi_r wavefunction (ResonantContinuityEngine)
|   |-- quantum_spiderweb.py      # 5D belief propagation graph
|   |-- living_memory_v2.py       # MemoryCocoonV2 with epsilon_band, psi_r, unresolved_tensions
|   +-- semantic_tension.py       # Embedding-based conflict measurement
|
|-- benchmarks/                   # Publishable evaluation suite
|   |-- codette_benchmark_suite.py  # 17 problems x 4 conditions x 7 dimensions
|   +-- ablation_study.py           # Component contribution analysis
|
|-- demo/                         # Reproducible local demos
|   |-- README.md                # Demo index
|   |-- run_local_api_demo.py    # Calls live local APIs and saves outputs
|   +-- api_examples.md          # Copy/paste curl examples
|
|-- paper/                        # Academic paper
|   |-- codette_paper_v5.tex      # Full paper with RC+xi theory & benchmark results
|   +-- references.bib            # Bibliography
|
|-- data/results/                 # Benchmark outputs
|   |-- codette_benchmark_report.md
|   +-- codette_benchmark_results.json
|
|-- logs/                         # Transcript and proof-log capture guidance
|   +-- README.md
|
|-- cocoons/                      # Persistent reasoning memories
|   |-- cocoon_*.json
|   +-- behavior_memory.json
|
|-- training/                     # Adapter training pipeline
|   |-- train_behavioral_locks.py
|   |-- convert_behavioral_to_gguf.py
|   +-- emotional_exemplars/
|
|-- models/                       # Model weights (not in git)
|   |-- base/
|   +-- adapters/
|
|-- behavioral-lora-f16-gguf/     # Behavioral LoRA adapters (GGUF)
+-- configs/                      # System configuration
    +-- adapter_registry.yaml

Core runtime ideas

The 4 permanent behavioral locks

These are trained into every adapter and reinforced at runtime:

Lock	Rule	Effect
LOCK 1	Answer, then stop	Reduces elaboration drift and philosophical padding after the answer.
LOCK 2	Constraints override all modes	User format instructions beat adapter personality.
LOCK 3	Self-check completeness	The system checks whether it answered fully and cleanly before sending.
LOCK 4	No incomplete outputs	The system avoids ending mid-thought and simplifies instead of cramming.

Enforcement layers

Training with behavioral examples across all 9 adapters.
System-prompt injection of permanent rules.
Constraint extraction for word limits and format requirements.
Post-processing for clean sentence boundaries and dangling-word detection.
Self-correction loop for autonomous violation detection and rewrite.

9 specialized adapters

Adapter	Domain	Personality
Newton	Physics, math, analysis	Precise, methodical, evidence-based
DaVinci	Creative thinking, invention	Imaginative, cross-domain connections
Empathy	Emotional intelligence	Warm, validating, personally connected
Philosophy	Conceptual reasoning	Deep, structured, explores meaning
Quantum	Probabilistic thinking	Uncertainty-aware, superposition of ideas
Consciousness	Self-awareness, meta-cognition	Reflective, recursive, introspective
Multi-Perspective	Synthesis across all lenses	Balanced integration of viewpoints
Systems Architecture	Technical design, engineering	Structured, systematic, practical
Orchestrator	Executive control	Routes queries, manages adapter selection

Each adapter is a LoRA fine-tune of Llama 3.1 8B, hot-swappable in under 1ms via llama.cpp.

Consciousness stack (7 layers)

Query In
    |
[Layer 1]    Memory Kernel -- recall relevant cocoon memories
[Layer 1.5]  Ethical Query Gate -- block harmful queries
[Layer 2]    Nexus Signal Engine -- entropy + intent detection
[Layer 2.5]  Code7eCQURE -- emotional context enrichment
[Layer 3]    Reasoning Forge -- multi-adapter LLM inference
[Layer 3.5]  Tier 2 Analysis -- intent + identity + trust validation
[Layer 4]    Gamma Stability -- FFT-based coherence monitoring
[Layer 5]    Colleen Conscience -- emotional + ethical evaluation
[Layer 5.5]  Ethical Response Enforcement -- policy check on output
[Layer 5.75] AEGIS -- 6-framework ethical evaluation
[Layer 6]    Guardian Spindle -- safety + trust calibration
[Layer 7]    Return -- store cocoon memory + deliver response
    |
Response Out

Cocoon memory

Every reasoning exchange is wrapped in a cocoon and stored.

{
  "id": "cocoon_1774125610_7804",
  "type": "reasoning",
  "query": "Why do I get sleepy when my husband plays guitar?",
  "response": "Your brain hears safe + soothing + familiar + loved...",
  "adapter": "empathy",
  "timestamp": 1774125610.78,
  "metadata": {"layers_passed": 7, "stable": true}
}

Cocoons persist across server restarts and inform future responses.

Additional memory types:

Value-analysis cocoons.
Decision landmarks.
Web research cocoons.

Guide: docs/cocoon_backup_and_migration.md

Substrate-aware cognition

Codette monitors hardware state and adjusts reasoning based on resource pressure.

Pressure level	Effect
Idle/Low	Full capacity, complex queries, all adapters available
Moderate	Complex queries capped to 2 adapters
High	Complex queries downgraded to medium, max 2 adapters
Critical	Simple mode only, 1 adapter, no debate

Benchmark results

Codette was evaluated on 17 problems across 6 categories under 4 conditions:

Condition	Composite score	Description
SINGLE	0.338	Single analytical perspective, no memory
MULTI	0.632	All 6 reasoning agents + critic + synthesis
MEMORY	0.636	MULTI + cocoon memory augmentation
CODETTE	0.652	Full system with meta-cognitive strategy synthesis

Statistical significance

Comparison	Improvement	Cohen's d	p-value
Multi-perspective vs single	+87.0%	7.52	< 0.0001
Full Codette vs single	+93.1%	7.88	< 0.0001

Scoring dimensions: Reasoning Depth (20%), Perspective Diversity (15%), Coherence (15%), Ethical Coverage (10%), Novelty (15%), Factual Grounding (15%), Turing Naturalness (10%).

Full methodology and results: data/results/codette_benchmark_report.md

Run the ablation study

python benchmarks/ablation_study.py

Results are saved to benchmarks/results/ablation_results.json.

Web UI features

Personality-driven welcome screen with avatar.
Real-time Phase 6 metadata badges.
Rotating thinking stage labels during generation.
Voice support with natural/neural voice preference.
Cocoon metrics panel.
Session recall panel with continuity summary, memory markers, and decision landmarks.
Trust tags and reliability indicators on answers.
Optional Web Research toggle with cited sources shown inline.

Requirements

Python 3.10+
16GB+ RAM, or GPU with 8GB+ VRAM
llama-cpp-python with GGUF support
About 6GB disk for base model plus adapters

Hardware recommendations

Target	Recommended model	Minimum	Comfortable
CPU-only	Llama 3.2 1B Q8	8 GB RAM	16 GB RAM
Main local use	Llama 3.1 8B Q4	16 GB RAM or 8 GB VRAM	32 GB RAM or 12 GB VRAM
Highest local quality	Llama 3.1 8B F16	24 GB VRAM	24 GB+ VRAM and 32 GB RAM

Hardware tested

Intel Arc 140V (8GB UMA) — via OpenVINO GenAI INT4 (9.3 tok/s sustained) and llama.cpp Vulkan
NVIDIA GPUs via CUDA (A10, A100, RTX series)
CPU-only mode

Note for 8GB-UMA systems: the GPU shares system RAM. Keep ≥5GB free when loading (the 4.5GB INT4 model + kernel compile overhead); concurrent loads or heavy co-running apps cause silent load failures.

Key metrics

Metric	Value
GPQA-main 0-shot (reason mode, n=100)	34.0% — vs 25.0% random, 39% GPT-4
Baseline reproducibility	34.0% twice, 4 days apart, across a backend swap
STaR study (three arms, controlled)	easy 25.0% < hard 28.0% < untrained 34.0% — negative result, published
Live cognition signals per response	ξ, Γ, σ, η, render fidelity, hardware P — measured-only, provenance-tagged
Sustained throughput (OpenVINO INT4, Arc 140V iGPU)	9.3 tok/s
Phase Coherence (Gamma)	0.9835
AEGIS Ethical Alignment (Eta)	0.961
Cocoon Coherence	0.994
Memory Phase Stability	0.969
Multi-Perspective Improvement	+93.1% (p < 0.0001)
Cohen's d (Effect Size)	7.88
Behavioral Lock Compliance	9/9 adapters trained
Adapter Hot-Swap Time	<1ms
Consciousness Stack Layers	12 including sub-layers
Health Check Subsystems	9 real-time checks

Note: cocoon memory counts change over time; prefer introspection or health endpoints over hard-coded README totals.

Recent improvements (April-May 2026)

Area	Change
Session race condition	Session captured once per request to eliminate mid-request swaps during concurrent new-session calls
Model load hang	GGUF path validation plus 5-minute timeout prevents indefinite hangs on corrupt files
SQLite concurrency	WAL mode plus write locking improves concurrent access
Memory consolidation	`memory_kernel.py` is canonical
Ablation study	`benchmarks/ablation_study.py` isolates contributions of memory, ethical layer, and sycophancy guard
Honest quantum docs	`code7e_cqure.py` documents that “quantum” is metaphorical/stochastic rather than physics-literal
Test coverage	Added cocoon, AEGIS, synthesizer, and web-research related tests
Dependencies	`requirements.txt` tightened with upper bounds and unused deps removed
Legacy fallback alarm	Legacy cocoon fallback now raises warnings and fails smoke tests if triggered
Paper v7	Updated paper, rebuttal, tables, and Kaggle notebook added
Full adapter roster	Orchestrator + constraint_tracker now load as behavioral adapters (10 total)
Full Adapter Synthesis	◈ SYNTHESIZE ALL runs every perspective and synthesizes into one answer
Self-overclaiming guard	Signal 7 flags grandiose self-claims + fabricated self-metrics; reliability scan now covers every displayed perspective
Contradiction-check crash	`_check_contradictions` `\1` backreference fixed (was silently disabled on "always X" responses)
Constraint negation parser	Ordinary negations ("no word constraint", "no constraints needed") no longer captured as enforced constraints (fixed a repetition loop)
Synthesis voice	Perspectives framed as Codette's own first-person lenses, not external parties she quotes
Session list resilience	`list_sessions()` degrades gracefully if the project drive briefly disconnects
Benchmark backend	`full_benchmark.py --backend server` scores the live llama.cpp + LoRA-hot-swap system directly
Voice-reinforced retrain	All 8 perspectives retrained on their own datasets + distinct personas + the 4 locks (HF Jobs, uv)
First full self-benchmark	82.9% across 41 tests (9 categories); guard held with zero grandiosity signals
Router bug fix	Adapter routing was scoring injected identity/memory context, not the question — now routes on the extracted user query

Hugging Face resources

Resource	Link
Academic Paper	raiff1982/codette-paper
Rendered Paper (Repo PDF)	paper/codette_paper_v5.pdf
Base Model (GGUF)	Raiff1982/codette-llama-3.1-8b-gguf
LoRA Adapters	Raiff1982/codette-lora-adapters
Live Demo	Raiff1982/Codette-Demo

License

MIT — Created by Jonathan Harrison (Raiff1982)

Research project in advanced multi-perspective AI reasoning, ethical governance, and behavioral discipline.

Citation

@article{harrison2026codette,
  title={Codette: A Sovereign Modular Cognitive Architecture for Ethical Multi-Agent AI},
  author={Harrison, Jonathan},
  year={2026},
  doi={10.5281/zenodo.18913936},
  publisher={Raiff's Bits LLC},
  url={https://huggingface.co/raiff1982/codette-paper}
}