## InferenceBackend trait (crates/thinking-engine/src/inference_backend.rs)

Runtime-switchable dispatch across all codec/inference paths. Nothing
killed — every research path coexists as a backend variant.

Two key axes documented in the trait module:

Axis 1 — full-path vs leaf-only quantization:
  Full-path QJL/PolarQuant: entire row → JL sign+magnitude (~20 B/row)
  Leaf-only I8 hybrid: HEEL+HIP location (6b) + i8 JLQ residual (9 B/row)
  Passthrough: exact (2×n_cols B/row)

Axis 2 — reconstruction-grade vs signature-grade:
  Reconstruction: SafetensorsRaw, BurnFwd, CandleFwd, HhtlF32+SlotL
  Signature: RaBitQ, SpiralEncoding, CodecCascade, Base17
  Hybrid: I8Hybrid (location + JLQ leaf)

7 backend structs registered in all_backends(). EncodedState enum
carries opaque per-backend state. Trait methods: encode, score,
reconstruct, bytes_per_row, shared_overhead_bytes, grade.

## TurboQuant P5 results (run on Qwen3-TTS-0.6B k_proj [2048,1024])

CRITICAL FINDING: all 4 correction methods (direct i8, Fisher z,
QJL corrected, TurboQuant) hit rho >= 0.997 at single-layer, but
ALL collapse to rho = 0.000 by layer 5 in a 33-layer chain.

  Single layer: Fisher z best (rho=0.999), all >= 0.997
  Chain L=5:    ALL 0.000
  Drift/layer:  QJL 6x lower bias than direct i8 (doesn't help)

Root cause: variance, not bias. Repeated multiplication of quantized
score matrices amplifies noise beyond recovery. QJL bias correction
is correct but irrelevant when variance dominates.

Implications:
  - Path B (cascade inference through 33 layers) NOT VIABLE as
    chained score multiplication
  - Single-layer cascade IS viable (rho >= 0.997)
  - I8 hybrid (HEEL+HIP + JLQ leaf) does f32 reconstruction, not
    chained scoring — different quality model, not refuted by this
  - Hybrid strategy: cascade per-layer, f32 GEMM between layers

P5 status updated in docs/CODEC_INVARIANTS_AND_EXPERIMENTS.md:
  MEASURED — chain collapses, single-layer passes.

https://claude.ai/code/session_01NYGrxVopyszZYgLBxe4hgj

Codifies 7 anti-patterns (AP1-AP7) learned from PRs #176-#188 into
an agent card that fires flags when the session repeats them:

  AP1: "225/225 feels like success" without gate 2 (#178)
  AP2: Projecting quality from docs instead of measuring (#177)
  AP3: Building new codec before benching existing ones (#184)
  AP4: Centroid-residual framing on near-orthogonal data (#177/#183)
  AP5: Python in the inference hot path
  AP6: Chained score multiplication without chain-collapse check (P5)
  AP7: Modifying ndarray without explicit permission (#176)

Invoked by adk-coordinator when pattern repetition is suspected, or
by human directly. Output: list of fired flags, max 7 lines.

Also audited all 29 agent cards across both repos:
  - All pin model: opus or model: sonnet (no hardcoded versions)
  - opus → Opus 4.7 automatically, sonnet → Sonnet 4.6
  - 3 ndarray agents on sonnet (l3-strategist, migration-tracker,
    product-engineer) — intentional for speed-over-depth roles
  - adk-coordinator missing Bash tool (by design — delegates)
  - sentinel-qa missing Edit/Write (by design — audit-only)

No agent changes needed for Opus 4.7 compatibility — model: opus
resolves correctly.

https://claude.ai/code/session_01NYGrxVopyszZYgLBxe4hgj

## P7: PolarQuant HIP family probe — REFUTED for pure direction split

Measured on Qwen3-TTS-0.6B k_proj [2048,1024], 256 rows:

  Base17 L1 (current):   16.8% within-family NN recall  (16/16 families)
  PolarQuant normalized:  7.8% within-family NN recall  (16/16 families)
  Delta: -9.0%  ← PolarQuant is WORSE

Root cause: stripping magnitude before clustering loses informative
signal. For k_proj rows, magnitude variation correlates with NN
structure — rows with similar magnitudes tend to be nearest neighbors.
Base17 L1 already encodes a JOINT direction+magnitude opinion through
the golden-step fold. Pure-direction families throw away half the
coupling.

Insight: the "opinion as address" framing is correct, but the opinion
must be JOINT direction+magnitude (like BF16's mantissa+exponent),
not direction alone. This confirms the logarithmic-scale bgz17
philosophy: u8 encodes both axes simultaneously.

Status: P7 REFUTED for PolarQuant-only normalization on k_proj.
Base17 L1 families are already sufficient for this tensor shape.
May differ for other roles (gate, up, down) — per-role probing is
a follow-up.

## InferenceBackend trait (inference_backend.rs)

Runtime-switchable dispatch design. 7 backend variants documented
with two classification axes:
  Axis 1: full-path QJL vs leaf-only I8 hybrid vs passthrough
  Axis 2: reconstruction-grade vs signature-grade vs hybrid

Trait: encode → EncodedState, score(i,j), reconstruct(i), grade().
Not yet wired into lib.rs (needs feature gate design for heavy deps).

https://claude.ai/code/session_01NYGrxVopyszZYgLBxe4hgj

## I9: BF17 shapeshifting

Same 16-17 bit wire width carries different constructs at different
HHTL levels: BF17 float at HEEL (joint direction+magnitude opinion),
4-bit partition at HIP, 8×i8 PolarQuant coefficients at LEAF. The
"shapeshifting" is: exponent bits at HEEL become direction bits at
LEAF; mantissa bits at HEEL become magnitude bits at LEAF. Explains
WHY PolarQuant-only splitting hurts (P7 result): the coupling between
direction and magnitude IS the information at HEEL/HIP level.

## P8: Cronbach's α codec bench — psychometric measurement model

Reframes the R&D bench from "horse race" to "psychometric instrument
validation." Codec candidates are test items; we measure internal
consistency (α) to discover factor structure.

### Epiphany × population correlation matrix

Cross-tabulates every invariant (I1-I9) and probe finding (P1-P7)
against 6 data populations: attention k_proj, MLP gate, vocab
embedding, Jina v5 output, audio codec embeddings, BGE-M3 output.
Each cell predicts what should happen if the invariant holds on
that population. The bench FILLS the cells.

### Populations chosen for cross-validation

Different distribution signatures (near-orthogonal vs unit-normalized
vs vocab-sparse vs SiLU-gated vs discrete-latent) ensure the factor
structure is real, not artifact of one tensor's shape.

### Metrics

9 metrics per (codec × population) cell. 4 already in
bgz_tensor::quality (pearson, spearman, top_k_recall, mae/rmse).
4 NEW to implement (Cronbach's α, Cohen's κ, bias, ICC).

https://claude.ai/code/session_01NYGrxVopyszZYgLBxe4hgj

https://claude.ai/code/session_01NYGrxVopyszZYgLBxe4hgj