[verhash]

Not satire, though I get why the terminology looks odd. The language comes from materials science because the math is the same: deterministic state updates with hard thresholds. In most AI systems, exclusion relies on probabilistic sampling (temperature, top-k, nucleus), which means you can’t replay decisions exactly. This explores whether exclusion can be implemented as a deterministic state machine instead—same input, same output, verifiable by hash.

“Mechanical” is literal here: like a beam fracturing when stress exceeds a yield point (σ > σᵧ), candidates fracture when accumulated constraint pressure crosses a threshold. No randomness, no ranking. If that framing is wrong, the easiest way to test it is to run the code or the HF Space and see whether identical parameters actually do produce identical hashes.

[foobarbecue]

What do you mean by "exclusion"?

[verhash]

Here “exclusion” just means a deterministic reject / abstain decision applied after a model has already produced candidates. Nothing is generated, ranked, or sampled here. Given a fixed set of candidate outputs and a fixed set of verified constraints, the mechanism decides which candidates are admissible and which are not, in a way that is replayable and binary. A candidate is either allowed to pass through unchanged, or it is excluded from consideration because it violates constraints beyond a fixed tolerance.

In practical terms: think of it as a circuit breaker, not a judge. The model speaks freely upstream; downstream, this mechanism checks whether each output remains within a bounded distance of verified facts under a fixed rule. If it crosses the threshold, it’s excluded. If none survive, the system abstains instead of guessing. The point isn’t semantic authority or “truth,” it’s that the decision process itself is deterministic, inspectable, and identical every time you run it with the same inputs.