docs: session handover — 6D NeuronPrint, loose ends, Rosetta exploration Complete handover prompt for next session: - What was built (serve.rs SPO pipeline, hydrate partitions, neuron.rs, docs) - Key epiphanies (6D = two SPO triads, palette = cleanup memory, golden-step = JL) - Loose ends (DataFusion UDFs, per-role palettes, real model hydration) - Rosetta exploration questions (Q archetype semantics, Gate importance, layer progression) - Architecture map with file paths - 7 commits this session - External references (Hyperprobe, Monosemanticity, SwiGLU, AriGraph) https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK#78
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AdaWorldAPI merged 8 commits intomainfrom Mar 31, 2026
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docs: session handover — 6D NeuronPrint, loose ends, Rosetta exploration
Complete handover prompt for next session:
- What was built (serve.rs SPO pipeline, hydrate partitions, neuron.rs, docs)
- Key epiphanies (6D = two SPO triads, palette = cleanup memory, golden-step = JL)
- Loose ends (DataFusion UDFs, per-role palettes, real model hydration)
- Rosetta exploration questions (Q archetype semantics, Gate importance, layer progression)
- Architecture map with file paths
- 7 commits this session
- External references (Hyperprobe, Monosemanticity, SwiGLU, AriGraph)
https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK#78AdaWorldAPI merged 8 commits intomainfrom
AdaWorldAPI merged 8 commits intomainfrom
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… + Lance write - serve.rs: PalettePipeline built at startup from bgz7 weight rows (Palette k=256, DistanceMatrix 128KB, SimilarityTable σ-calibrated CDF) - palette_score() maps incoming messages through palette.nearest() then scores via similarity_table.similarity(distance_matrix.distance(q, c)) - Threshold sim > 0.3 → Palette HIT, else MISS → LLM fallthrough - hydrate.rs: write_to_lance() + hydrate_to_lance() for LanceDB persistence - chat_bundle.rs: palette_indices field on AutocompleteCache https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
serve.rs: WeightStore with direct L1 nearest neighbor on 34-byte vectors. No palette indirection for query path — 17 subtractions is sub-microsecond. hydrate.rs: both vector (f32, for Lance ANN/RaBitQ) and base17 (i16, for direct L1 and palette assignment) columns. palette_s/p/o columns kept for the SPO triple store path (bgz17 Palette→DistanceMatrix→SimilarityTable). The palette infrastructure (bgz17 crate, 121 tests) is not dropped — it serves the million-edge SPO triple store where O(1) precomputed 256×256 distance lookups matter. For the REST query path, raw vectors are better. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Request flow is now: message → extract_triplets() → (S, P, O) strings → triplet_to_headprint(S, P, O) → HeadPrint (S:6, P:6, O:5 planes) → headprint_to_spo() → SpoHead (palette indices + NARS truth) → nars_engine.score() with StyleVector → f32 → nars_infer() deduction/abduction against knowledge base No more brute-force vector search. Messages are decomposed at SPO level like AriGraph does, scored via NARS inference rules, and matched against knowledge base of ingested weight tensors. hydrate.rs: dual columns (f32 vector for Lance ANN, i16 base17 for direct L1), palette_s/p/o for SPO triple store path. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Parse tensor names (HuggingFace + GGUF conventions) into: - TensorRole: QProj/KProj/VProj/OProj/GateProj/UpProj/DownProj/Embedding/Norm - layer_idx: u16 layer number (None for non-layer tensors) Stored as Arrow columns for Lance partition pruning. Enables per-role palettes (256 archetypes of "query behavior" vs "gating decisions") and per-layer search (only search gate_proj in layer 12, not all 5M vectors). No re-extraction from models needed — partition key was always in the bgz7 tensor names. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
… representation NeuronPrint (204 bytes): Q/K/V/Gate/Up/Down — complete behavior of one neuron. bundle() → 34-byte holographic fingerprint (all 6 roles superposed) attention() → Q ⊕ K (what it attends to) retrieval() → K ⊕ V (what it retrieves when matched) mlp() → Gate ⊕ Up ⊕ Down (the nonlinear transform) NeuronQuery: selective role probing with Optional fields. attention(q) → probe Q against K store retrieval(k) → probe K against V store gating(gate) → probe Gate role_mask() → 6-bit Pearl-like mask (Q/K/V/Gate/Up/Down) score(neuron) → L1 distance on active roles only NeuronTrace: NARS truth derived from role ratios. frequency → Gate magnitude (how often this neuron fires) confidence → Up/Down ratio (evidence strength) attention → Q·K alignment (activation strength) coherence → K·V alignment (retrieval quality) expectation → c * (f - 0.5) + 0.5 https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
The 6 weight tensor roles (Q/K/V/Gate/Up/Down) are 6 dimensions of a single neuron's complete behavior. 204 bytes per neuron, aligned by row index across all 6 tables. The CAM position IS preserved. Key epiphanies: - Q/K/V = attention triad (who asks, what matches, what's retrieved) - Gate/Up/Down = MLP triad (fires?, amplifies?, compresses?) - K+V = key-value retrieval store, Q = query against it - Gate/Up/Down = NARS truth hydration (frequency, confidence) - Two triads = 6D SPO: each triad is an S/P/O decomposition - Cross-role distances are meaningful (Q·K = attention sharpness) - Same structure across Llama/Qwen/GPT-2/GGUF with naming map Rosetta exploration needed: - Do Q archetypes cluster by semantic role? - Does Gate magnitude predict neuron importance? - Does Up/Down ratio detect polysemanticity? - Layer-wise NeuronTrace progression (feature → concept gradient) No re-extraction from models needed — partition key was always in the bgz7 tensor names. Just needs grouping by tensor role. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
…rint
Extends lance-graph's Cypher parser with 6D NeuronPrint-aware queries:
MATCH (n:Neuron {layer:15})-[:ATTENDS]->(m:Neuron)
WHERE l1(n.q, m.k) < 50
RETURN n.feature, m.v, n.trace.confidence
Maps to DataFusion SQL over partitioned Lance datasets:
- Partition prune by tensor_role + layer_idx
- RaBitQ ANN on vector column
- UDFs: l1, magnitude, xor_bind, bundle, neuron_trace, nars_revision
4-phase implementation plan:
Phase 1: DataFusion UDFs (pure SQL, no Cypher changes)
Phase 2: Cypher extension (parser + planner)
Phase 3: Cross-layer tracing (residual stream paths)
Phase 4: Model comparison (multi-model diff queries)
https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Complete handover prompt for next session: - What was built (serve.rs SPO pipeline, hydrate partitions, neuron.rs, docs) - Key epiphanies (6D = two SPO triads, palette = cleanup memory, golden-step = JL) - Loose ends (DataFusion UDFs, per-role palettes, real model hydration) - Rosetta exploration questions (Q archetype semantics, Gate importance, layer progression) - Architecture map with file paths - 7 commits this session - External references (Hyperprobe, Monosemanticity, SwiGLU, AriGraph) https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
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AdaWorldAPI
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Per procedure-bookkeeping.md Pass 2: classify each "none" row from Pass 1 as superseded / live / archived. Result: 25 open → 13 superseded, 6 live, 6 archived. Superseded (shipped under overlapping PRs): FINAL_MAP (#65), session_A_v3 (Phase 1 #29), session_B_v3 (Phase 2), session_6d (#78), session_bgz17_similarity (#40), session_unified_26_epiphanies (#60), session_ontology_layer_audit (#155), research_quantized_graph_algebra (#186-198), session_MASTER_map_v3, session_{integration,master,model}_plan (elegant-herding-rocket) Live (aligned to active phases): P18_INTERNAL_LLM (Phase 8 D2), SCOPED_PROMPTS (refresh candidate), arxiv (governance), session_C_v3 (Phase 3 Lane A), session_D_v3 (Phase 4), session_epiphany_integration (Phase 8), session_unified_vector_search (Phase 3 cross-repo) Archived (moved to prompts/archive/ in prior commit): 6 audio/codec/fisher-z files https://claude.ai/code/session_01SbYsmmbPf9YQuYbHZN52Zh
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