Claude/qwen claude reverse eng v hu hv#68
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- CausalEdge64: block(6) + proj(4) + verb(2) + row(16) + l1(16) + freq(10) + conf(10) = 64 bits. Register-width, POPCNT-ready. - scaffold_to_palette64(): edges → 64×64 binary attention matrix. attend(query, gamma) → which reasoning scaffold blocks fire. - simd.rs: clean compile-time AVX-512 dispatch, single cfg block for all 512-bit + 256-bit types when avx512f enabled. Endgame: hydrate p64 from scaffold discovery so it routes tokens through the same Q+O heads that Claude-4.6-Opus distillation created. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Maps projection shifts to p64 predicate layers: Q→CAUSES, O→ENABLES, K→SUPPORTS, V→REFINES, Gate→CONTRADICTS, FFN→ABSTRACTS/GROUNDS, scale-inv→BECOMES scaffold_to_heel_planes(): edges → 8×u64 HEEL bitmasks scaffold_to_palette3d_layers(): 4 diffs → 8×[u64;64] layers → Palette3D::infer() with ThinkingStyle::ANALYTICAL mimics the Claude-4.6-Opus reasoning circuit (CAUSES∩ENABLES∩SUPPORTS). Full hierarchy: 8 HEELs → 64×64 Palette → HHTL → 256×256. The old bgz17 is 1D planes; p64 is the 3D reasoning geometry. Also: clean simd.rs AVX-512 imports (single cfg block). https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
…erlay The 4 causal diffs across 5 Qwen models produce a cross-validated volatility map. Volatile weights (high NARS freq) = attention heads that Q8_0 destroyed. Palette3D encodes this topology in 196KB. Overlay at inference: O(1) POPCNT per attention head per token. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
- VolatilityMap: cross-validated NARS truth per (block, projection) from 4 diffs. Volatile = architecture, stable = ballast. - build_volatility_map(): integrates scaffold + scale-invariance - apply_palette_overlay(): O(1) per head — modulates attention scores via palette bitmask. volatile → keep, ballast → decay. - serialize/deserialize_palette3d_layers(): 4100 bytes (PAL8 format). 8 layers × 64 rows × 8 bytes + 4 byte magic. The overlay is multiplicative on Q×K^T scores, not additive on weights. 196KB palette sharpens what Q8_0 blurred — the routing pattern that uniform quantization destroyed in the volatile attention heads. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Was: model-00001-of-00011.safetensors (404) Now: model.safetensors-00001-of-00011.safetensors (correct HF pattern) https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
curl -sI -L returns headers from ALL responses including redirects. HuggingFace 302 has content-length: 1395 (the redirect body), then the final 200 has the real file size. Using .find() grabbed the first (wrong) value; .filter().last() grabs the final (correct) one. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Palette3D layers now follow the deduction algebra: MEASURED: CAUSES(base→v1), ENABLES(base→v2), REFINES(v1→v2), ABSTRACTS(9B) DEDUCED: SUPPORTS=C∩E, CONTRADICTS=C∧¬E∧moving, GROUNDS=S∩A, BECOMES=E\C SPO mapping: S=Q(Subject), P=K(Predicate), O=O(Object). Q+O shifted, K stable = CausalMask::SO = the reasoning scaffold. Also: fix curl content-length parsing to use last header after redirects. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
PAL8 format: "PAL8"(4) + style(1) + 8×64×u64(4096) = 4101 bytes. PaletteStyle enum (Analytical..Meta) travels with the palette so Blumenstrauss::new() on the lance-graph side knows which combine/contra mode to use. ndarray extracts → PAL8 → lance-graph deserializes → Blumenstrauss. The 4101-byte Highway payload IS the reasoning circuit. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
v2 supercedes v1 (14K vs 3K Claude-4.6-Opus samples). Two diffs suffice: MEASURED: CAUSES(base→v2), ABSTRACTS(9B) DEDUCED: SUPPORTS=C∩A, CONTRADICTS=C\A, GROUNDS=S, BECOMES=A\C ~150 GB to stream instead of 201. Same structural map, cleaner signal. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Quality scoring via 4-diff cross-validation: GOOD: v1 ∩ v2 ∩ 9B (reasoning scaffold, all agree) BAD: v2 \ v1 (aggressive overfit, knowledge-loss) UNCERTAIN: v1 ∩ v2 \ 9B (consistent but not scale-invariant) REVERTED: v1 \ v2 (v1 overcorrected, v2 fixed) v1 is the control experiment — separates intentional refinement from overfitting. v2 lost 7.2% MMLU-Pro; the BAD heads are why. NarsHeadBelief: closed-loop framework for self-reinforcement: Static prior (weight diffs) → inference feedback → NARS revision → LoRA rank recommendation (Reinforce/Suppress/Explore) Each round increases confidence. The Palette3D evolves. scaffold_to_palette3d_quality_filtered(): only GOOD heads get critical palette bits. BAD heads masked out. The Palette3D becomes a quality prior, not just a topology map. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
27B base→v1 at threshold=1 (LoRA deltas are L1=0-2 in Base17): FfnGate: 0.6% shifted (dominant — SwiGLU gate rewiring) FfnUp: 0.3% shifted Q: 0.3% shifted (planning queries changed) O: 0.2% shifted (synthesis changed) Embed: 0.0% (vocabulary unchanged) Key finding: LoRA distillation primarily changes FFN gating, not attention Q/K/V/O. The reasoning scaffold lives in SwiGLU. Also: graceful shard failure handling, threshold lowered to 1. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
All 5 models indexed (safetensors BF16). All 4 diffs completed: Diff 1 (base→v1): 10,845 shifted, FfnGate 0.6% dominant Diff 2 (base→v2): 1,921 shifted, FfnGate 0.1% (near base) Diff 3 (v1→v2): 11,509 shifted, FfnGate 0.5% (v2 reverts v1) Diff 4 (9B): 7,577 shifted, FfnGate 1.0% (strongest at 9B) Key findings: - Reasoning scaffold = SwiGLU gate_proj, not attention Q/K/V/O - v2 is a revert (closer to base than v1) - K stable at 27B (knowledge preserved), K shifted at 9B (capacity limit) - v1 is the control experiment separating 4.5 behavior from 4.6 reasoning https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
Tactics 1-12 from the 34-tactic integration plan, adapted to ndarray: styles::rte — #1 Recursive Thought Expansion (Hofstadter) styles::htd — #2 Hierarchical Thought Decomposition (CLAM) styles::smad — #3 Structured Multi-Agent Debate (NARS revision) styles::tcp — #5 Thought Chain Pruning (Berry-Esseen) styles::irs — #9 Iterative Roleplay Synthesis (XOR binding) styles::mcp — #10 Meta-Cognition (Brier score calibration) styles::tca — #12 Temporal Context (Reichenbach tense) Plus additions to existing modules: causal_diff.rs — #4 reverse_trace() (Pearl Rung 3) bgz17_bridge.rs — #6 inject_noise() (simulated annealing) nars.rs — #7 adversarial_critique(), #11 detect_contradiction() cascade.rs — #8 adaptive_resolution() Every tactic is fn(Base17, NarsTruth) → result. No LLM prompting. 16 tests passing. API: crate::hpc::styles::rte::expand() etc. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
12 cognitive primitives implemented: 7 as styles/ submodules (rte, htd, smad, tcp, irs, mcp, tca) 5 as additions to existing modules (causal_diff, bgz17_bridge, nars, cascade) 21 tests passing. Waiting for tactics #13-#34. https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
29 submodule files + mod.rs = 30 files, 1617 lines, 49 tests passing.
Each tactic is a pure fn — no LLM prompting, no session state.
Tactics 1-12: rte htd smad tcp irs mcp tca (+ causal_diff nars bgz17 cascade)
Tactics 13-20: cdt mct lsi pso cdi cws are tcf
Tactics 21-27: ssr etd amp zcf hpm cur mpc
Tactics 28-34: ssam idr spp icr sdd dtmf hkf
Science: Hofstadter, CLAM/CAKES, Pearl, Berry-Esseen, Wang/NARS,
Kanerva/VSA, Guilford, Festinger, Gentner, Shannon, Granger, Cohen.
API: crate::hpc::styles::{tactic}::{fn_name}()
https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK
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