This is an automated rejection. No LLM generated, assisted/co-written, or edited work.
Read full explanation
Epistemic status: I've built working implementations of what I describe, but the method is patent-pending and I can't open it. I know that's in tension with the norms here, so I'm not asking anyone to take the implementation on faith — I'm putting the ideas up for criticism, and I'll discuss the conceptual claims in as much depth as anyone wants. Medium confidence in the framing; low confidence that I'm not missing prior work.
I want to argue that two levers are underweighted in the current safety conversation, and get told where the reasoning breaks.
1. Auditability via provably-faithful re-expression. Take a trained transformer and re-express it as an explicitly inspectable, component-by-component system — every component named and individually addressable, run in the open rather than as a sealed forward pass — and then prove the re-expression is behaviorally identical to the original: byte-identical for mixture-of-experts models, and greedy-answer-preserving to floating-point precision for dense ones (checked token-for-token, with the single divergence on the dense case being a documented floating-point near-tie, not a mechanism difference). No retraining, no approximation.
The claim I actually want examined is not "I can do this" but "this is worth doing": an equivalence receipt turns intervention into something you can reason about exactly. When you ablate, swap, or instrument a component, you know precisely what you perturbed relative to a known-faithful baseline — the counterfactual is pinned. Most interpretability tooling works on approximations or proxies; does an exact-equivalence substrate buy enough to justify the constraint? Where does byte-exact faithfulness stop being the useful property?
The honest limit: faithfulness is not understanding. Re-expressing a model into an addressable, verified form proves what is running and that it is unchanged; it does not tell you what the weights mean. I think it's a useful substrate to do interpretability on, not a substitute for it. Tell me if that's wrong in either direction.
2. Epistemics: gating output on justification instead of probability. Separately, I've built a language system that won't assert what it can't justify — it returns an explicit "I can't ground that" rather than a fluent guess. The structural claim: hallucination-prevention by construction (block un-justified emission) is a different and stronger thing than hallucination detection after generation. The obvious objection is coverage — a system that only speaks when it can justify may refuse a lot. I think that trade is correct for high-stakes settings and wrong for open-domain chat. Is the by-construction framing actually distinct from well-known retrieval/verification approaches, or am I re-describing them?
What I'm asking for: where does each argument fail? What prior work am I not citing — especially on equivalence-preserving re-expression for interpretability, and on emission-gating vs. post-hoc verification? I'd rather be corrected here than anywhere else.
I'm deliberately not claiming any of this is "part of the alignment solution." I don't believe that. I think they're primitives, and I want to know if they're good ones.
Epistemic status: I've built working implementations of what I describe, but the method is patent-pending and I can't open it. I know that's in tension with the norms here, so I'm not asking anyone to take the implementation on faith — I'm putting the ideas up for criticism, and I'll discuss the conceptual claims in as much depth as anyone wants. Medium confidence in the framing; low confidence that I'm not missing prior work.
I want to argue that two levers are underweighted in the current safety conversation, and get told where the reasoning breaks.
1. Auditability via provably-faithful re-expression. Take a trained transformer and re-express it as an explicitly inspectable, component-by-component system — every component named and individually addressable, run in the open rather than as a sealed forward pass — and then prove the re-expression is behaviorally identical to the original: byte-identical for mixture-of-experts models, and greedy-answer-preserving to floating-point precision for dense ones (checked token-for-token, with the single divergence on the dense case being a documented floating-point near-tie, not a mechanism difference). No retraining, no approximation.
The claim I actually want examined is not "I can do this" but "this is worth doing": an equivalence receipt turns intervention into something you can reason about exactly. When you ablate, swap, or instrument a component, you know precisely what you perturbed relative to a known-faithful baseline — the counterfactual is pinned. Most interpretability tooling works on approximations or proxies; does an exact-equivalence substrate buy enough to justify the constraint? Where does byte-exact faithfulness stop being the useful property?
The honest limit: faithfulness is not understanding. Re-expressing a model into an addressable, verified form proves what is running and that it is unchanged; it does not tell you what the weights mean. I think it's a useful substrate to do interpretability on, not a substitute for it. Tell me if that's wrong in either direction.
2. Epistemics: gating output on justification instead of probability. Separately, I've built a language system that won't assert what it can't justify — it returns an explicit "I can't ground that" rather than a fluent guess. The structural claim: hallucination-prevention by construction (block un-justified emission) is a different and stronger thing than hallucination detection after generation. The obvious objection is coverage — a system that only speaks when it can justify may refuse a lot. I think that trade is correct for high-stakes settings and wrong for open-domain chat. Is the by-construction framing actually distinct from well-known retrieval/verification approaches, or am I re-describing them?
What I'm asking for: where does each argument fail? What prior work am I not citing — especially on equivalence-preserving re-expression for interpretability, and on emission-gating vs. post-hoc verification? I'd rather be corrected here than anywhere else.
I'm deliberately not claiming any of this is "part of the alignment solution." I don't believe that. I think they're primitives, and I want to know if they're good ones.
— John "Mark" Kuykendall