russell@unturf.com
951002c372
Three new tickets carve up the open questions from §9 of docs/soft-hash-channel-analysis.md (#000018): #000034 — Hessian alignment under φ_linear ============================================ Computational. Measure spectrum of W^T W (the v7 reference embed_hard_to_vec frozen-seed projection matrix) vs typical training-loss Hessian eigenvalue distributions on representative checkpoints. Determines whether the linear projection has structural alignment with low-eigenvalue directions, which would let T2 adversaries amplify covert-channel steerage beyond the random-oracle baseline established in #000018 §4. Deliverable: bench/scripts/phi_alignment_probe.py + verdict (STRUCTURAL_ALIGNMENT / NO_ALIGNMENT / ANTI_ALIGNED) per representative checkpoint. Parks until a v7 reference checkpoint is available; the analysis is empirical-only and useless without representative training data. #000035 — PRG choice for φ_PRG ================================ Cryptographic. Pin a specific PRG construction for the M1 mitigation (PRG-based anchor map) proposed in #000018 §5.2. Recommended: HMAC-SHA-512(seed, digest ∥ counter) → uniform-random floats in [-1, 1]. Reasons: - Tightest dependency surface (stays in SHA family already committed via SHA-256). - NIST-approved PRF construction (SP 800-108 KDF in counter mode). - Speed parity with AES at v7 cadence; PRG cost negligible. - Provable security reduction: PRF security from SHA-512 collision-resistance, structurally stronger than SHA-256. Deliverable: arborist/v7/anchor_prg.py + tests + known-answer-test fixture + v7 § 9.10 amendment text. Lands when v7 plastic-training has a deployment target. #000036 — T3 per-window budget bound ===================================== Formal. Derive an explicit closed-form upper bound on the covert- channel capacity under threat model T3 (hyperparameter adversary) when M2 (per-checkpoint nonce) is in place. #000018 §6 lists "bounded by per-window budget" without giving the bound. Three control bandwidths combine: B1 — gradient bias (g · W · log₂ N_directions) B2 — LR selection (⌈W/K⌉ · log₂ R) B3 — batch order (Bottou-Bousquet conjecture: ≪ log₂ N!) Deliverable: docs/soft-hash-channel-t3-bound.md (§§1-8 formal derivation) + bench/scripts/t3_bound_calculator.py (operator- parametrized closed-form). Operators plug in (g, K, R, W, batch_count, gradient_norm) → read off bits/window residual; pick nonce-window length to drive residual under their target. Index updated; Next ID 000037. Cross-reference added to #000018's status line so future readers see the three follow-ups in line. |
||
|---|---|---|
| .. | ||
| _source | ||
| diagrams | ||
| tickets | ||
| bench-maxing.md | ||
| benchmarks.md | ||
| cti-architecture.md | ||
| mesh.md | ||
| pi-star-composition.md | ||
| qa-modes-bench.md | ||
| seven-point-program.md | ||
| soft-hash-channel-analysis.md | ||
| spec-methodology.md | ||
| TICKETS.md | ||
| tool-action-dag-design.md | ||
| v8-fork-score.md | ||