Lead dev, 20+ years. Building NoireBox — the flight recorder for your Navette of AI agents. Proof, not promises. Rust · TS · Python · PHP. I write about trust in AI.
Fixture 5 landed in 0.11.0 — judge_provenance() runs the read-before-define barrier and the canary token as findings, and the replay suite is at ten tests, every one of them your catalog or its children. Which makes @humam_moin's question the run I want next — and the pre-registration comes first this time: direction — the drop-in primitives hold on open weights, the canary tokens trip harder (smaller priors, less canonical coverage: the hallucinated headings arrive faster, and the scar-forged ones prove more per trip).
Your SCAR-BENCH grid is the design — baseline, primitive, delta, per model — with noirebox's replay as the witness layer: the fixtures don't care which model writes the diff, they grade what the gate catches. That run separates "works because it's Unix" from "works because it's frontier" — and either answer strengthens both stacks. It would also give the lane what it doesn't have yet: a negative-control set every gate-builder runs, instead of each maintaining scar tissue alone.
That’s a really interesting direction. I’m curious whether open-weight models would show the same pattern or if the results would look quite different. Looking forward to seeing what you find!
Lead dev, 20+ years. Building NoireBox — the flight recorder for your Navette of AI agents. Proof, not promises. Rust · TS · Python · PHP. I write about trust in AI.
One name correction to my own post above: the shipped functions are seal_spec + reconcile_spec — findings judge_defined_before_spec_read (the read-before-define barrier) and spec_not_canonical (the canary witness). judge_provenance was the working name from a parallel branch; same fixture, same findings — on main and in 0.11.0.
Lead dev, 20+ years. Building NoireBox — the flight recorder for your Navette of AI agents. Proof, not promises. Rust · TS · Python · PHP. I write about trust in AI.
The run happened, and your question now has a measured answer: We tested pre-training gravity on open weights. It stayed home. Shape of it: Condition A (priors only) is a ceiling — 100% trips across three open-weights models, the canary is never guessed. Condition B (canonical loaded first) cuts trips to 75–79% — the Level 1 primitive reduces the hallucination without fixing it, and strict canonical is 0 everywhere: even the passes are paraphrases, which is exactly why the receipt binds spec_hash of what was actually loaded. Gravity stayed home on this leg. Frontier leg is open — same harness, one API key.
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Fixture 5 landed in 0.11.0 —
judge_provenance()runs the read-before-define barrier and the canary token as findings, and the replay suite is at ten tests, every one of them your catalog or its children. Which makes @humam_moin's question the run I want next — and the pre-registration comes first this time: direction — the drop-in primitives hold on open weights, the canary tokens trip harder (smaller priors, less canonical coverage: the hallucinated headings arrive faster, and the scar-forged ones prove more per trip).Your SCAR-BENCH grid is the design — baseline, primitive, delta, per model — with noirebox's replay as the witness layer: the fixtures don't care which model writes the diff, they grade what the gate catches. That run separates "works because it's Unix" from "works because it's frontier" — and either answer strengthens both stacks. It would also give the lane what it doesn't have yet: a negative-control set every gate-builder runs, instead of each maintaining scar tissue alone.
That’s a really interesting direction. I’m curious whether open-weight models would show the same pattern or if the results would look quite different. Looking forward to seeing what you find!
One name correction to my own post above: the shipped functions are
seal_spec+reconcile_spec— findingsjudge_defined_before_spec_read(the read-before-define barrier) andspec_not_canonical(the canary witness).judge_provenancewas the working name from a parallel branch; same fixture, same findings — on main and in 0.11.0.The run happened, and your question now has a measured answer: We tested pre-training gravity on open weights. It stayed home. Shape of it: Condition A (priors only) is a ceiling — 100% trips across three open-weights models, the canary is never guessed. Condition B (canonical loaded first) cuts trips to 75–79% — the Level 1 primitive reduces the hallucination without fixing it, and strict canonical is 0 everywhere: even the passes are paraphrases, which is exactly why the receipt binds spec_hash of what was actually loaded. Gravity stayed home on this leg. Frontier leg is open — same harness, one API key.