3 ms·
Deterministic Governance: mechanical exclusion / bit-identical
This repository implements a deterministic exclusion engine where governance decisions are treated as a mechanical process rather than a probabilistic one. Candidates exist as stateful objects that accumulate strain under a scheduled constraint pressure. Pressure is applied across explicit phases—nucleation, quenching, and crystallization—and exclusion occurs only when accumulated stress exceeds a fixed yield threshold. Once fractured, a candidate cannot re-enter; history matters.
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 https://github.com/Rymley/Deterministic-Governance-Mechanism
- deleted 8mo ago[deleted]
- foobarbecue 8mo agoI don't even understand what discipline we're talking about here. Can someone provide some background please?
- Nevermark 8mo ago> Quenching is higher-frequency pressure application that amplifies contradictions and internal inconsistencies. > 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 8mo agoNot 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 8mo agoWhat do you mean by "exclusion"?
- verhash 8mo agoHere “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.
- nextaccountic 8mo agoYou really really need to be upfront in the first paragraph or your docs that you are talking about the inner workings of LLMs and other machine learning stuff Failing that, at least mention it here
- nextaccountic 8mo agoThe thing that lets LLMs select the next token is probabilistic. This proposed a deterministic procedure 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 8mo agoAh, LLMs. I should have guessed.
- gwern 8mo agoOK, this is AI slop ("fracture" alone gives it away). But maybe there's still something of value here? Can you explain it in actual human terms, give a real example, and explain what you did to test this and why I shouldn't flag this like I did https://news.ycombinator.com/item?id=46701114 https://news.ycombinator.com/item?id=46701114 ?
- verhash 8mo agoVerified facts: “The sky is blue” “Water is wet” Candidate outputs: “The sky is blue” “The sky is green” Each sentence is embedded deterministically (in the demo, via a hash-based mock embedder so results are reproducible). For each candidate, I compute: similarity to the closest verified fact distance from that fact a penalty function based on those values Penalty accumulates over a fixed number of steps. If it exceeds a fixed threshold, the candidate is rejected. In this example, “The sky is blue” stays below the threshold; “The sky is green” crosses it and is excluded. What I tested: Identical inputs + identical config always produce identical outputs (verified by hashing a canonical JSON of inputs + outputs). Re-running the same scenario repeatedly produces the same decision and the same hash. Changing a single parameter (distance, threshold, steps) predictably changes the outcome. Why this isn’t “AI slop”: There’s no generative model here at all. The terminology is unfortunate but the code is explicit arithmetic. The entire point is removing non-determinism, not adding hand-wavy intelligence. If you think the framing obscures that rather than clarifies it, that’s useful feedback—I’m actively dialing the language back. But the underlying claim is narrow: you can build governance filters that are deterministic, replayable, and auditable, which most current AI pipelines are not. If that’s still uninteresting, fair enough—but it’s not trying to be mystical or persuasive, just mechanically verifiable. You can test it here if you like, https://huggingface.co/spaces/RumleyRum/Deterministic-Governance-Mechanism https://huggingface.co/spaces/RumleyRum/Deterministic-Govern...
- gwern 8mo agoI don't get it. Embeddings don't prioritize, or even necessarily encode, truth value as a dimension. And even if they did, if you simply accept based on some hyperparameter of distance, it sounds like this procedure just leaves you vulnerable to problems like salami-slicing where you reach 'the sky is green' (which after all, it is sometimes) by multiple steps just below the tolerance.