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

https://claude.ai/code/session_01M3at4EuHVvQ8S95mSnKgtK

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

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