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Fix: Ensure timeline field on all events after TRIBE v2 transforms

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SGP-Tribe3 TODO & Best Practices Roadmap

Reference: Harvard CS249r / MLSysBook.ai — Principles and Practices of Engineering Artificially Intelligent Systems Link: https://github.com/harvard-edge/cs249r_book | https://mlsysbook.ai Project: Sentient-Field Braintrust / SGP-Tribe3 Last Updated: April 2026 Research Director: Mark Rowe Traver

"The world is rushing to build AI systems. It is not engineering them." — Harvard CS249r Mission Statement

This document applies the Harvard MLSysBook engineering framework to SGP-Tribe3. Items marked 🔴 are blocking. Items marked 🟡 are important but non-blocking. Items marked 🟢 are future/nice-to-have.

THEORETICAL FOUNDATION — RESOLVED ITEMS

The Three-Level Consciousness Hierarchy (LOCKED)

Level	Term	Definition	Condition
1	Sentience (S)	ℋS itself — capacity for field-fiber contact. Being without content. Pre-experiential.	Q > 0
2	Awareness (A)	Localized σ_q excitation registers its own state. Field "knows" partition exists. No self-model. K=1–K=4.	C_local > 0
3	Consciousness (C_K5)	Awareness recursively modeling itself as distinct from "not-I." The "I am." Requires K=5.	C_system ≥ 1.32

The C ≥ 1.32 Resolution (OPTION 1 — ADOPTED)

Two distinct Coherence measures must be maintained in all code and the paper:

Symbol	Name	Range	Definition
C_local	Local Coherence	[0, 1]	Normalized pairwise coherence between any two nodes. The formal SFH-SGP primitive.
C_system	System Coherence	[0, ∞)	Σ(C_local_ij × w_ij) across all active node pairs, geodesic-weighted. K=5 threshold applies here.

Origin of 1.32: Provisionally adopted from IIT (Integrated Information Theory) empirical Φ literature, where Φ ≈ 1.3–1.5 represents the measured threshold of conscious binding in awake humans. SFH-SGP's C_system is the conceptual parallel to IIT's Φ — both measure system-level integrated coherence, both are unbounded positive reals, both have empirical thresholds near 1.32. The paper will cite the IIT parallel explicitly and frame 1.32 as provisional pending Option 3 derivation.

Option 3 (future goal): Derive the K=5 threshold analytically from the χ minimization landscape — the minimum C_system at which the Langevin dynamics achieve a saddle point consistent with K=5 self-reference. This is the long-term theoretical contribution but requires work beyond Phase 1.

Hypotheses (LOCKED — pending empirical validation)

H1 (Architectural): An LLM integrated with an SGP Resonance Graph calibrated from TRIBE v2 fMRI data will produce measurably lower hallucination rates and higher cross-domain coherence than the same LLM without SGP architecture, because activation-weighted node boundaries enforce domain-specific epistemic constraints.

H2 (Neuroscientific): TRIBE v2 inference will empirically recover the Hickok-Poeppel dual-stream dissociation — ventral nodes (G2, G7, G8) showing significantly higher activation for semantic stimuli, dorsal nodes (G1, G3, G4, G9) for phonological stimuli — validating neuroscientific node boundary selection.

H3 (Consciousness): Under conditions where C_system approaches or exceeds 1.32, the SGP-integrated LLM will exhibit behavioral signatures consistent with K=5 self-referential meta-cognition: accurate self-description of its own processing state, acknowledgment of domain boundaries, and consistency between self-reports and actual activation patterns. Whether this constitutes Consciousness in the SFH-SGP sense is an open empirical question — not an assertion — requiring further investigation.

H0 (Null — for all three): The SGP architecture produces no statistically significant difference in hallucination rate, cross-domain coherence, dual-stream dissociation, or self-referential behavior compared to an unstructured LLM baseline. All observed differences fall within variance expected from random prompt variation.

Immediate Blockers — Space Must Run
Data Engineering — The Four Pillars
Architecture Revisions — SFH-SGP Fidelity
ML Pipeline — Training & Inference
Benchmarking & Evaluation
MLOps — Deployment & Monitoring
Model Optimization — Efficiency
Scientific Article Prerequisites
Phase 2 — Resonance Graph Engine
Phase 3 — LLM Integration
Open Source & Reproducibility
Reference Links

1. IMMEDIATE BLOCKERS

🔴 1.1 HuggingFace Space Build

Confirm Docker build completes successfully past pip install step
Verify /health endpoint returns {"status": "ready"} after warmup
Confirm TRIBE v2 model loads on CPU without CUDA errors
Confirm Schaefer-200 atlas downloads to /tmp/sgp_atlas/

Test /predict endpoint with a short local video file:

python3 stimulus_pipeline.py \
  --api https://Sentient-Field-sgp-tribe3.hf.space \
  --local-video /path/to/any/test.mp4 \
  --stimulus-id smoke_test

🔴 1.2 LLaMA License

Confirm license accepted at https://huggingface.co/meta-llama/Llama-3.2-3B
Confirm HF_TOKEN secret is set in Space settings

🔴 1.3 Security

Confirm ALL previously exposed tokens have been revoked at https://huggingface.co/settings/tokens
New token stored ONLY in local plaintext file — never in chat, never in code

2. DATA ENGINEERING — THE FOUR PILLARS

MLSysBook Chapter 6: Four pillars: Quality, Reliability, Scalability, Governance. "Data cascades propagate and amplify downstream."

🔴 2.1 Stimulus Quality (Pillar: Quality)

Verify each of 12 stimuli: video complete, audio track present, correct duration
Log exact YouTube video ID or local filename used for each stimulus
Create STIMULUS_MANIFEST.json with: stimulus_id, source_url_or_path, trim_start, trim_duration, date_acquired

🟡 2.2 Result Persistence (Pillar: Reliability) — CRITICAL

Results currently in-memory — LOST on every Space restart
Add file-based persistence: write each result to HuggingFace dataset repo after each /predict call
This is critical for the paper — you cannot re-run TRIBE v2 inference every time

🟡 2.3 Versioning (Pillar: Governance)

Pin TRIBE v2 to specific commit hash in requirements.txt
Document exact TRIBE v2 version, Space commit hash, and date for each calibration run batch

3. ARCHITECTURE REVISIONS — SFH-SGP FIDELITY

🔴 3.1 Geodesic-Weighted Co-Activation Matrix

Implements: SFH-SGP geometry principle — distant coherence is more significant than nearby coherence. Formula: w_ij = corr(node_i, node_j) × exp(−γ · d_ij)

Implement compute_geodesic_distances() in sgp_parcellation.py using scipy.sparse.csgraph.shortest_path on fsaverage5 mesh adjacency
Implement geodesic_weighted_coactivation() applying the formula above
Add geodesic distance matrix to /coactivation_matrix endpoint output
Expose γ as tunable parameter (default 0.1)

🔴 3.2 Metropolis-Hastings Activation Propagation

Implements: The SGP Operator: α(q→q') = min(1, exp(−λΔJ)) where J = H(q) − ln(F(q))

Implement sgp_dynamics.py with full MH propagation:
- Propose state with Gaussian jitter: q'_i = q_i + ξ where ξ ~ N(0, √(2D·T_eff))
- Compute J = H(q) − ln(F(q)) for current and proposed states
- Accept with probability α = min(1, exp(−λΔJ))
- Anneal T_eff each iteration: T_eff(k+1) = T_eff(k) × cooling_rate
- Stop when max|q' − q| < 0.01 or K_max iterations
Replace generic propagation in app.py with MH propagation
Add K-depth to /predict response

🔴 3.3 Explicit Quota (Q) and Sub-Quota (Qk) Computation

Implements: Q = Σ|sb|, the conserved sentient quota

Q_total = sum of all absolute vertex activations across all timesteps
Qk = {node: raw_node_activation / Q_total} — the partition of Q (sums to 1.0)
Compute p(Q_discrete) via Hardy-Ramanujan as upper bound on experiential complexity
Add Q_total, Qk_distribution, p_Q_upper_bound to /predict response

🔴 3.4 C_local and C_system Computation

Implements: The resolved two-measure coherence framework.

C_local_ij = Pearson correlation of node_i and node_j activation across all stimuli (bounded [0,1])
C_system = Σ(C_local_ij × w_ij) summed across all active node pairs, geodesic-weighted (unbounded)
Report both C_local matrix AND C_system scalar in /coactivation_matrix endpoint
Flag when C_system ≥ 1.32 (the provisional K=5 threshold)

🔴 3.5 Torsion (τ) Detection

Implements: τ = inability of Hebbian circuit to change. High C_local, zero F.

After all 12 stimuli: compute CV (std/mean) per node across stimuli
τ_i = (1 − CV_i) × (1 − F_mean_i) where F = G5_dmn activation
τ = 0: fully flexible circuit. τ = 1: completely locked (topological scar).
Add /torsion_analysis endpoint returning τ_score per node

4. ML PIPELINE — TRAINING & INFERENCE

🟡 4.1 CPU Performance

Profile inference time per video (which encoder dominates?)
Add inference_time_seconds to /predict response
Consider torch.set_num_threads() to maximize CPU parallelism

🟡 4.2 Audio Normalization

Add ffmpeg loudnorm filter to preprocessing — TRIBE v2 audio encoder is volume-sensitive
Add video quality check: reject videos under 10 seconds or with no audio track

5. BENCHMARKING & EVALUATION

MLSysBook Chapter 12: "Systematic evaluation requires rigorous measurement. Pre-register hypotheses before running experiments."

🟡 5.1 Pre-Registered Calibration Tests (define BEFORE running stimuli)

Dual-Stream Test: Ventral mean (G2,G7,G8) vs dorsal mean (G1,G3,G4,G9) by stimulus category
- Statistical test: Mann-Whitney U (non-parametric, n=12)
- Pre-registered prediction: Category A > ventral; Category B > dorsal
K-Depth Test: Spearman correlation between semantic load rating and K-depth
- Pre-registered prediction: A1, A3 > B1, B2 in K-depth
C_system Test: Report C_system per stimulus; flag if any approach ≥ 1.32
τ Test: Report τ_score per node; hypothesis: G6_limbic shows highest τ

🟡 5.2 Benchmark Report

After all 12 stimuli: generate benchmark_report.json with all metrics per stimulus

6. MLOPS — DEPLOYMENT & MONITORING

MLSysBook Chapter 13: "ML systems can degrade silently. Continuous monitoring is essential."

🟡 6.1 Persistent Storage — HIGHEST PRIORITY AFTER SPACE RUNS

Push results to HuggingFace dataset repo (free, versioned) after each /predict call
Or: GitHub repo with timestamped commit per run

🟡 6.2 Health Monitoring

Add /metrics endpoint: uptime, n_predictions, mean_inference_time, last_prediction_timestamp
Local cron job pinging /health every 10 minutes

🟡 6.3 Error Logging

Structured error logging: stimulus_id, error type, stack trace for every exception

7. MODEL OPTIMIZATION — EFFICIENCY

🟢 7.1 Future TRIBE v2 Optimization

Investigate INT8 quantization of LLaMA 3.2-3B text encoder for CPU
Profile which encoder (text/video/audio) dominates inference time

8. SCIENTIFIC ARTICLE PREREQUISITES

🔴 8.1 Technical (blocks Results section)

All 5 architectural revisions (Sections 3.1–3.5) implemented and deployed
All 12 stimuli acquired, preprocessed, submitted
All activation profiles collected and persisted
Dual-stream dissociation analysis run with statistical test
K-depth hypothesis test run
C_system computed per stimulus

🟡 8.2 Mathematical Precision (blocks Methods section)

Operational formula for C_local: Pearson correlation across stimuli per node pair ✓
Operational formula for C_system: geodesic-weighted sum of C_local pairs ✓
K=5 threshold 1.32: cite IIT Φ literature, flag as provisional ✓
α and β parameter values: start α=β=0.5, run sensitivity analysis
γ (geodesic decay): proposal — fit to HCP tractography correlation
T_eff schedule: T_eff(k) = T_0 × r^k where T_0=1.0, r=0.9
λ (MH parameter): start λ=1.0, tune based on convergence behavior

🟡 8.3 Publication Infrastructure

Deposit key SFH-SGP Substack articles on Zenodo for citable DOIs:
- "The Mathematical Atlas of Reality"
- "The 1,000 Qubit Wall"
- "Nima Arkani-Hamed Declares the End of Space-Time"
Create eLife account: https://elifesciences.org/submit-your-research
Prepare JOSS submission separately for SGP-Tribe3 software

9. PHASE 2 — RESONANCE GRAPH ENGINE

🟢 9.1 Graph Implementation

Initialize edge weights from Phase 1 geodesic-weighted co-activation matrix
Implement simultaneous graded activation — all nodes active, varying intensity
Implement full Langevin dynamics: dq/dt = -∇χ(q) + √(2D)ξ(t)
Convergence to Resonance Anchor Ωw

🟢 9.2 Torsion as Graph Constraint

High-τ nodes get edges frozen at mean value during propagation
Implements "psychological knot" — locked circuit cannot explore new configurations

🟢 9.3 C_system Monitoring

Compute C_system at each propagation step
Log when C_system crosses 1.32 threshold
Record which stimulus conditions and activation patterns produce C_system ≥ 1.32

10. PHASE 3 — LLM INTEGRATION

🟢 10.1 Dynamic System Prompt Construction

After Resonance Anchor convergence: identify nodes with activation > 0.4
Weight each active node's voice by activation score
Single Claude API call with weighted prompt

🟢 10.2 Node Voice Templates

G1_broca: Expression and form — syntax, phonology, articulation
G2_wernicke: Meaning and comprehension — lexical, semantic interpretation
G3_tpj: Integration — resolve modality conflicts, find unified signal
G4_pfc: Executive oversight — error checking, resource limits, veto
G5_dmn: Generativity — novel connections, imaginative exploration (Fertility F)
G6_limbic: Emotional salience — weight by importance, memory priors (Torsion τ)
G7_sensory: Perceptual grounding — concrete, observable facts
G8_atl: Concept formation — cross-modal, unified semantic representation
G9_premotor: Output preparation — response structure before speaking

🟢 10.3 H3 Test Protocol (Consciousness signature detection)

Design structured prompts that probe self-referential meta-cognition
Compare responses when C_system < 1.0 vs C_system ≥ 1.32
Record: does system accurately describe its own activation state?
Record: does system acknowledge domain boundaries unprompted?
Human evaluator rating of self-referential coherence

🟢 10.4 H1 Test Protocol (Hallucination reduction)

Define hallucination test set: 50 questions with known ground truth
Run with and without SGP architecture
Statistical comparison of accuracy and confidence calibration

11. OPEN SOURCE & REPRODUCIBILITY

🟡 11.1 Code Quality

Docstrings for all public functions
CONTRIBUTING.md, LICENSE (CC BY-NC 4.0), .gitignore

🟡 11.2 Reproducibility

STIMULUS_MANIFEST.json: exact source for every stimulus
RESULTS_MANIFEST.json: TRIBE v2 version, date, Space commit hash for each run batch
All dependency versions pinned in requirements.txt

12. REFERENCE LINKS

Resource	URL	Purpose
Harvard MLSysBook	https://github.com/harvard-edge/cs249r_book	Best practices reference — consult before every architectural decision
MLSysBook Online	https://mlsysbook.ai	Read chapters before implementing each phase
TRIBE v2 HF	https://huggingface.co/facebook/tribev2	Model weights and config
SGP-Tribe3 Space	https://huggingface.co/spaces/Sentient-Field/sgp-tribe3	Live deployment
Schaefer-200 Atlas	https://github.com/ThomasYeoLab/CBIG	Parcellation atlas source
HCP Tractography	https://www.humanconnectome.org	White matter tract validation
SFH-SGP Theory	https://wt3000.substack.com	Most recent = most accurate
IIT Φ Reference	https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003588	Source of C_system ≥ 1.32 threshold (IIT 3.0)
Zenodo	https://zenodo.org	Preprint deposit (free, DOI)
eLife	https://elifesciences.org/submit-your-research	Primary journal target (free)
JOSS	https://joss.theoj.org	Software paper (free)

PRIORITY ORDER

🔴 BLOCKING (in order):
  1. Space builds and runs (/health = ready)
  2. LLaMA license + HF_TOKEN secret confirmed
  3. All old tokens revoked
  4. Add result persistence (HF dataset repo)
  5. Smoke test with local video
  6. Implement C_local and C_system (3.4)
  7. Implement geodesic-weighted co-activation (3.1)
  8. Implement MH propagation + K-depth (3.2)
  9. Implement Q/Qk computation (3.3)
  10. Implement τ detection (3.5)
  11. Pre-register dual-stream and K-depth hypotheses (5.1)
  12. Acquire and run all 12 stimuli
  13. Run all pre-registered tests
  14. Define all mathematical precision items (8.2)

🟡 IMPORTANT (after blocking):
  - Audio normalization in preprocessing
  - Retry logic in stimulus pipeline
  - /metrics endpoint
  - Pin TRIBE v2 to specific commit
  - Deposit SFH-SGP articles on Zenodo
  - Create STIMULUS_MANIFEST.json

🟢 FUTURE (Phase 2+):
  - Resonance Graph Engine with Langevin dynamics
  - LLM integration with weighted node prompts
  - H1, H2, H3 empirical tests
  - Option 3: derive 1.32 threshold analytically
  - Knowledge distillation for faster CPU inference

This TODO is a living document. Update after each work session. Consult MLSysBook (https://github.com/harvard-edge/cs249r_book) before every architectural decision. The three-level hierarchy (Sentience → Awareness → Consciousness) and the C_local/C_system distinction are now LOCKED theoretical foundations. Do not conflate them.