Skip to content
This repository was archived by the owner on Aug 3, 2026. It is now read-only.

op4 HOSB: blanked-grid scoring primitives (stage 2) - #78

Merged
bitzic merged 1 commit into
mainfrom
fix/op4-hosb-primitives
Jun 28, 2026
Merged

op4 HOSB: blanked-grid scoring primitives (stage 2)#78
bitzic merged 1 commit into
mainfrom
fix/op4-hosb-primitives

Conversation

@bitzic

@bitzic bitzic commented Jun 28, 2026

Copy link
Copy Markdown
Contributor
  • host_reduce: BlankedCell, NonCausalModelError, reduce_blanked_nlls — HOST verdict over a (M,L) blanked-grid NLL array with two-filler + wrong-target witnesses; STRATUM-WEIGHTED mean (head/tail by true size) so forced tail coverage does not bias val_bpb vs compute_val_bpb
  • val_bpb: build_blanked_grid (real-prefix-then-filler rows, one host-secret scored position each; stratified head/tail sampling) + per_position_nlls_blanked
  • look-ahead is useless BY CONSTRUCTION: the answer (input[t+1]) is filler at every scored position; a causal model's score is identical to single-pass
  • wrong-target witness is FRACTION-based (reject only if most wrong cells are sub-floor) so a confident honest model isn't false-rejected on a collision
  • empty/degenerate grid + bad fractions fail loud (ValueError), never inf
  • pure additive library, NO live-path wiring yet (compute_val_bpb / op4 unchanged) — stage 3 threads block-hash entropy + shadow->fail-closed
  • 15 CPU tests: answer absent, honest==single-pass, look-ahead gains nothing, stratum-weighted unbiasedness, both witnesses incl collision tolerance, tail

- host_reduce: BlankedCell, NonCausalModelError, reduce_blanked_nlls —
  HOST verdict over a (M,L) blanked-grid NLL array with two-filler +
  wrong-target witnesses; STRATUM-WEIGHTED mean (head/tail by true size)
  so forced tail coverage does not bias val_bpb vs compute_val_bpb
- val_bpb: build_blanked_grid (real-prefix-then-filler rows, one host-secret
  scored position each; stratified head/tail sampling) + per_position_nlls_blanked
- look-ahead is useless BY CONSTRUCTION: the answer (input[t+1]) is filler at
  every scored position; a causal model's score is identical to single-pass
- wrong-target witness is FRACTION-based (reject only if most wrong cells are
  sub-floor) so a confident honest model isn't false-rejected on a collision
- empty/degenerate grid + bad fractions fail loud (ValueError), never inf
- pure additive library, NO live-path wiring yet (compute_val_bpb / op4
  unchanged) — stage 3 threads block-hash entropy + shadow->fail-closed
- 15 CPU tests: answer absent, honest==single-pass, look-ahead gains nothing,
  stratum-weighted unbiasedness, both witnesses incl collision tolerance, tail

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
@bitzic
bitzic merged commit 7d22561 into main Jun 28, 2026
4 checks passed
Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.

Labels

None yet

Projects

None yet

Development

Successfully merging this pull request may close these issues.

1 participant