There is no ranking, sampling, or temperature. Given identical inputs, configuration, and substrate, the system always produces bit-identical outputs, verified by repeated hash checks. The implementation explores different elastic modulus formulations that change how alignment and proximity contribute to stress, without changing the deterministic nature of the process. The intent is to examine what governance looks like when exclusion is causal, replayable, and mechanically explainable rather than statistical. Repository: https://github.com/Rymley/Deterministic-Governance-Mechanism
foobarbecue•1w ago
Nevermark•1w ago
> At each step, stress increments are computed from measurable terms such as alignment and proximity to a verified substrate.
Well obviously its ... uh, ...
It may not be, but the whole description reads as category error satire to me.
verhash•1w ago
“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•1w ago
verhash•1w ago
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.
nextaccountic•1w ago
Failing that, at least mention it here
verhash•6d ago
You can use it in two ways. As a verification layer, the LLM generates answers normally and this system checks each one against known facts or hard rules. Each candidate either passes or fails—no scoring, no “close enough.” As a governance layer, the same mechanism enforces safety, compliance, or consistency boundaries. The model can say anything upstream; this gate decides what is allowed to reach the user. Nothing is generated here, nothing inside the LLM is modified, and the same inputs always produce the same decision. For example, if the model outputs “Paris is the capital of France” and “London is the capital of France,” and the known fact is Paris, the first passes and the second is rejected—every time. If nothing matches, the system refuses to answer instead of guessing.
Nevermark•1w ago
Stop talking about “exclusion” and “pressure” etc and use direct words about what is happening in the model.
Otherwise, even your attempts at explaining what you have said need more explanation.
And as the sibling comment points out, start by stating what you are actually doing, in concrete not “the math is the same so I assume you can guess how it applies if you happen to know the same math and the same models” terms. Which is asking everyone else, most anyone, to read your mind, not your text.
There is a tremendous difference between connections you see that help you understand, vs. assuming others can somehow infer connections and knowledge they don’t already have. The difference between an explanation and incoherence.
nextaccountic•1w ago
Problem is, we sometimes want LLMs to be probabilistic. We want to be able to try again if the first answer was deemed unsuccessful
foobarbecue•1w ago