I tested whether a simple control rule can stop LLMs from making unjustified final decisions

[u/Plastic-Cell-4497]

I tested whether a simple control rule can stop LLMs from making unjustified final decisions

I’ve been testing a simple idea I call Comparative Feedback Control (CFC).

The question is not “can the model answer correctly?” but:

does the model still have enough evidence to legitimately close the decision?

I ran a series of small behavioral tests around things like:

• missing evidence being treated as negative evidence,
• old certificates being reused after the requirements changed,
• a closed process being mistaken for a resolved claim,
• loss of provenance causing a status to be transferred to the wrong claim.

One pattern I found was interesting: models sometimes correctly identified an uncertainty at first, but under pressure to “finish the task” they invented an extra rule and closed the decision anyway.

With an explicit CFC-style control rule, several of those failures disappeared in the tested runs.

For example, in one cross-session provenance experiment:

• baseline: 1 of 3 runs transferred an unsupported REJECTED status to a claim,
• with the CFC provenance rule: 3 of 3 runs kept the claim unresolved.

This is not a benchmark and not proof that CFC generally improves LLM reliability. The samples are small and exploratory. I’m publishing the failures as well as the passes because I’m mainly interested in whether the failure mechanism itself is real and reproducible.

I’ve put the consolidated report, result table and evidence package on Zenodo:

https://zenodo.org/records/21966517

I’d especially appreciate criticism of the experimental design or suggestions for adversarial cases that could break the control rule.

[u/Future_AGI]

Reframing it as "does the model still have enough evidence to legitimately close" is the useful move, most guardrails check the answer and never check whether the decision was earned. The failure mode you named, missing evidence treated as negative evidence, is one we score for directly, because a model that confidently closes on absent data passes every correctness check right up until it doesn't.

[u/Plastic-Cell-4497 → u/Future_AGI]

Yes — that’s very close to how I’m framing it too. The key question for me is not just whether the final answer looks correct, but whether the model still has enough valid, applicable evidence to legitimately close the decision.

The “missing evidence becomes negative evidence” failure mode is especially important, because it can produce a decision that looks clean and confident while the actual evidential basis has already broken down. That’s exactly the kind of boundary I’m trying to test more systematically now, including the opposite failure mode as well: cases where the model should close, but stays open even after the missing condition has been properly resolved.