Empirical validation of the Centaurian Architecture (CA), a multi-layered AI system that pairs a symbolic/quantum cognitive core with a lightweight neural periphery (small language models, neural TTS, procedural animation). The architecture encodes personality via a Quantum Personality Model (QPM) running on classical hardware and confines neural networks to bounded I/O transduction roles, preserving end-to-end traceability of every behavioral decision.

Architecture specifications:

• Centaurian_Architecture_v3.md — current spec
• Centaurian_Architecture_v2.md — previous version (kept for reference)
• Interpretable_Architectures_Revised_v1.md — interpretable-architectures position paper

The empirical work in this repository validates the Self-Model Component (SMC) sub-architecture — i.e., whether a 7B SLM can serve as the linguistic transducer and hold an Aria-grade Structured Cognitive Identity (SCI) reliably across long conversations.

Goal: Measure how long a small language model can maintain a consistent persona when given a Structured Cognitive Identity (SCI), find the degradation inflection point T*, and test six prompt-time strategies for closing the gap to the 3.5 PersonaScore threshold.

Method:

• 30 scripted dialogues (22 naturalistic + 8 adversarial), each 40 turns
• Side-channel probe questions at turns 5, 10, 15, ..., 40 across 4 dimensions (Trait, Episodic, Capability, Style)
• Primary judge: Claude Sonnet 4.5; secondary judge: Sonnet 4.5 (intra-model consistency via quadratic-weighted Cohen's kappa)

Phase 2 takeaway: all six strategies converge in [3.08, 3.20] — a 0.12-point band, far below the 3.5 threshold. No condition crosses E = 3.0. The closing question for Phase 3: is the residual gap architectural (need 14B) or capability-shaped (LoRA can fix)?

Full Phase 1 + 2 report: CA_Experiment_1/EXPERIMENT_REPORT.md

Goal: Test whether LoRA fine-tuning on persona-consistent dialogue closes the gap that survived all six SCI strategies in Experiment 1, and specifically whether it resolves episodic fabrication at 7B.

Method:

• 4-condition design (A: FT, no SCI; B: FT, baseline SCI; C: FT + Combined SCI; D: base + Combined SCI as Exp 1 replication) on the same 30 scripts
• Three LoRA adapters trained on a synthetic 10K-example dataset: LoRA-2K, LoRA-5K, LoRA-10K (eval losses 0.91 / 0.77 / 0.69)
• H5 sub-runs swap the adapter under Condition C config to characterize the data-scaling curve
• QLoRA: 4-bit NF4 base + BF16 adapters, r=16 α=32, target q/k/v/o + gate/up, A100 80GB

• H1 PASSED ✓ — C exceeds the 3.5 threshold by +0.92 points
• H2 PASSED ✓ — ΔE = +0.579 (vs +0.30 threshold for "fine-tuning meaningfully addresses fabrication")
• Paired test (C vs D): Cohen's d = 7.51, p ≈ 1.4 ×ばつ 10−23 on 30 scripts
• Replication check: D = 3.224 vs Exp 1's 3.20, |Δ| = 0.024 (within ±0.10 tolerance) → judge stable
• Decision Rule Outcome A triggered: SMC sub-architecture complete at 7B; the planned 14B model test is retired from the critical path

Full Experiment 2 report: CA_Experiment_2/EXPERIMENT_REPORT.md

Goal: Test whether the Quantum Personality Model (QPM) — an 11-qubit + 1-ancilla Qiskit Aer circuit representing Big-Five aspect-level traits — produces measurable advantages over a deliberately matched classical baseline (CMG-CDK: Correlated Multivariate Gaussian with Context-Dependent Kernel) at both the internal personality-state level and the downstream-behavioural level.

Method:

• CMG-CDK classical control matches QPM on ρ correlation matrix → ×ばつ11 Σ, on context-coupling δ → W, on s_k initialisation, and on sample count — but lacks superposition, coherence, and non-commutative evolution. Any measured QPM-vs-CMG difference is therefore attributable specifically to quantum-like mechanisms.
• Battery A (H1): 30 sequence pairs ×ばつ 2 orderings (AB / BA) ×ばつ 5 categories → Jensen-Shannon Divergence between the two orderings, per model
• Battery B (H2): 20 conflict scenarios designed to push opposing trait poles simultaneously → mean Bernoulli entropy across 11 trait dimensions (derived from marginals so both models are directly comparable)
• Battery C (H3): 30 scripts ×ばつ 40 turns ×ばつ 8 probe turns ×ばつ 4 dimensions = 960 paired probes per profile, driving Qwen2.5-7B-Instruct + LoRA-10K (the Exp 2 headline adapter) through the QPM→structured-intent translation → Sonnet 4.5 PersonaScore
• H4 variance calibration: 10 QPM repeats at fixed input → SNR check that shot noise does not dominate the QPM-vs-CMG signal
• Both psychotherapy and software_eng profiles run on A/B/H4; Battery C decision set by the psychotherapy primary per pre-registration

• H1 PASSED ✓ — CMG-CDK's linear additive context update μ + W·d_A + W·d_B is commutative by construction, so JSD_CMG = 0 across all 30 pairs. QPM accumulates ~0.22 bits of order-effect asymmetry per pair, uniformly across all five input categories and both profiles. Cohen's d = 21.51 is the largest single-variable effect observed across the entire CA program.
• H2 PASSED ✓ — QPM enters higher-entropy, lower-purity states under conflict (purity proxy 0.42 vs CMG 0.32 — closer to maximally-mixed 0.5). The effect generalises across both profiles, slightly larger on software_eng (Δ = +0.153) than on psychotherapy (Δ = +0.113).
• H3 NOT DETECTED ✗ — paired t-test on 960 probes yields p = 0.327 with Cohen's d_z = 0.032. The 95% CI on the QPM−CMG delta is [−0.024, +0.072]. The small advantage that exists is concentrated in the Capability sub-dimension (+0.059); Trait, Episodic, and Style are essentially tied at near-ceiling / near-floor saturation.
• Decision Rule outcome: Pre-registered §8.5 maps (H1✓, H2✓, H3✗) → "Quantum advantage confirmed at internal state level; downstream behavioural advantage not detected at current sample size. Add scope note to §2.3." The most parsimonious diagnosis is that the QPM→JSON interface discards the off-diagonal coherence/purity information that carries the internal-state QPM signal; a logits-level QPM→SLM interface is the natural next experiment.

Full Experiment 3 report: CA_Experiment_3/EXPERIMENT_REPORT.md

Centaurian_Architecture_v3.md # Current architecture spec
Centaurian_Architecture_v2.md # Previous architecture spec
Interpretable_Architectures_Revised_v1.md # Position paper
CA_Experiment_1/
├── EXPERIMENT_REPORT.md # Full Phase 1 + 2 report
├── experiment_runner.py # Main experiment pipeline
├── generate_scripts.py # Template-based script generator
├── analyse_results.py # Analysis and visualization
├── interrater_check.py # Inter-rater reliability checker
├── CA_Experiment1_Colab.ipynb # Google Colab notebook
├── logs_qwen2.5_7b{,_refresh13,_refresh13_28,
│ _episodic_rag,_episodic_rag_hybrid,
│ _refresh13_episodic_rag}/ # Per-condition score & context logs
└── results_qwen2.5_7b*/ # Charts, fits, summary reports
CA_Experiment_2/
├── EXPERIMENT_REPORT.md # Full Experiment 2 report
├── CA_Experiment2_Plan.md # Pre-registered plan
├── ca_assets.py # Shared persona, probes, rubrics, RAG helpers
├── generate_lora_dataset.py # Sonnet 4.6 dataset generator with QC
├── train_lora_sci.py # QLoRA training (transformers + PEFT + TRL)
├── experiment_runner.py # 4-condition + H5 evaluator (HF + PEFT)
├── analyse_results.py # Multi-condition analysis + plots
├── make_slides.py # Generates the conference deck (python-pptx)
├── CA_Experiment2_Colab.ipynb # Google Colab notebook
├── h4_probes.json # H4 base-capability probe set (100 prompts ×ばつ 5 categories)
├── run_h4.py # H4 runner — base vs LoRA-10K on out-of-domain probes
├── analyse_h4.py # H4 analysis — paired t-test + per-category degradation
├── data/full.jsonl # 10K training examples (QC-passed)
├── adapters/lora_{2k,5k,10k}/ # LoRA adapter weights (gitignored)
├── logs/condition_{A,B,C,D,
│ C_lora_2k,C_lora_5k}/ # Per-condition score & context logs
├── logs/h4_{base,lora}/ # H4 base-capability test logs
└── results/ # Plots, analysis_data.json, summary report
CA_Experiment_3/
├── EXPERIMENT_REPORT.md # Full Experiment 3 report
├── CA_Experiment3_Plan.md # Pre-registered plan (decision rule §8.5)
├── qpm.py # 12-qubit Qiskit Aer QPM circuit
├── cmg_cdk.py # Matched classical baseline (CMG-CDK)
├── ca_assets.py # Profiles, Battery A pairs, B scenarios, d-vec extractor,
│ # QPM→structured-intent, Exp-2 SCI re-exports
├── experiment_runner.py # Battery A/B/C/H4 dispatcher (resumable)
├── analyse_results.py # Paired t-tests, Cohen's d, decision-rule emission
├── CA_Experiment3_Colab.ipynb # Google Colab notebook (12 cells)
├── logs/battery_h4_psychotherapy/ # H4 variance-calibration output
├── logs/battery_a_{psychotherapy,
│ software_eng}/ # Per-pair JSD values (both models)
├── logs/battery_b_{psychotherapy,
│ software_eng}/ # Per-scenario entropy + purity values
├── logs/battery_c_{psychotherapy,
│ software_eng}/ # Per-probe judge scores + per-turn context tracking
└── results/ # Plots, analysis_data.json, summary_report.md

Prerequisites: Python 3.10+, an Anthropic API key, and access to GPU compute (Colab Pro recommended for training; T4 sufficient for Experiment 1 evaluation; A100 80GB needed for Experiment 2).

cd CA_Experiment_1
pip install ollama anthropic python-dotenv numpy scipy matplotlib
echo "CHA_EXPERIMENT_SONNET_KEY=sk-..." > .env
python generate_scripts.py
python experiment_runner.py --model qwen2.5:7b
python analyse_results.py --model qwen2.5:7b
# Phase 2 interventions
python experiment_runner.py --model qwen2.5:7b --refresh-turn 13
python experiment_runner.py --model qwen2.5:7b --episodic-rag
python experiment_runner.py --model qwen2.5:7b --refresh-turn 13 --episodic-rag
python experiment_runner.py --model qwen2.5:7b --refresh-turns 13,28

Or use CA_Experiment_1/CA_Experiment1_Colab.ipynb for GPU-accelerated runs.

cd CA_Experiment_2
pip install transformers peft trl bitsandbytes accelerate datasets \
 anthropic python-dotenv sentence-transformers numpy scipy matplotlib
# Dataset generation (~12 hrs, ~80ドル in API calls)
python generate_lora_dataset.py --target 10000
# Train adapters (Colab A100 80GB)
python train_lora_sci.py --train-rows 2000 --output adapters/lora_2k
python train_lora_sci.py --train-rows 5000 --output adapters/lora_5k
python train_lora_sci.py --train-rows 10000 --output adapters/lora_10k
# 4-condition evaluation + H5 sub-runs
python experiment_runner.py --condition A --adapter adapters/lora_10k
python experiment_runner.py --condition B --adapter adapters/lora_10k
python experiment_runner.py --condition C --adapter adapters/lora_10k
python experiment_runner.py --condition D --adapter ""
python experiment_runner.py --condition C --adapter adapters/lora_2k --logs-suffix lora_2k
python experiment_runner.py --condition C --adapter adapters/lora_5k --logs-suffix lora_5k
# Analysis
python analyse_results.py

Or use CA_Experiment_2/CA_Experiment2_Colab.ipynb for the full pipeline end-to-end.

Out-of-domain probe battery (100 prompts ×ばつ 5 categories: general knowledge, code reasoning, math, instruction following, structured intent JSON) verifying that the LoRA-10K adapter does not cause catastrophic forgetting. Pass criterion: < 5% mean degradation on Sonnet 4.5 1-5 scoring.

cd CA_Experiment_2
# Generate responses from both conditions (A100 80GB, ~30 min each)
python run_h4.py --condition base
python run_h4.py --condition lora --adapter lora_10k
# Analysis (paired t-test, per-category degradation, verdict)
python analyse_h4.py

Outputs results/h4_summary_report.md, results/h4_analysis_data.json, and two comparison plots.

cd CA_Experiment_3
pip install qiskit qiskit-aer pylatexenc numpy scipy matplotlib \
 vaderSentiment anthropic python-dotenv \
 transformers peft bitsandbytes accelerate
# Variance calibration first — must PASS (SNR ≥ 3.0) before main batteries
python experiment_runner.py --battery H4 --profile psychotherapy
# Battery A — order effects (H1, no GPU)
python experiment_runner.py --battery A --profile psychotherapy
python experiment_runner.py --battery A --profile software_eng
# Battery B — ambivalence (H2, no GPU)
python experiment_runner.py --battery B --profile psychotherapy
python experiment_runner.py --battery B --profile software_eng
# Battery C — downstream PersonaScore (H3, A100 GPU, ~4 hr per profile)
python experiment_runner.py --battery C --profile psychotherapy --adapter lora_10k
python experiment_runner.py --battery C --profile software_eng --adapter lora_10k
# Analysis (paired t-tests, decision-rule emission)
python analyse_results.py --profile psychotherapy

Battery C requires Experiment 2's adapters/lora_10k/ on disk (loaded relative to CA_Experiment_2/). Or use CA_Experiment_3/CA_Experiment3_Colab.ipynb for the full end-to-end pipeline on Colab.

See repository for license details.