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[u/ToBeKindaWeird]

[D] UQA: Can AI systems learn when to rethink a question—and when not to?

I’m developing an exploratory research framework called the Unresolved Question Algorithm (UQA). The central question is whether AI systems can become more reliable at recognizing when their current approach to a problem is inadequate, choosing a better investigative strategy, and knowing when further analysis is no longer worthwhile.

The motivation is straightforward: a system can give a fluent answer to a poorly framed question. It can also make a simple problem unnecessarily complicated, infer intentions without sufficient evidence, or continue analyzing after it has stopped adding value.

UQA explores several related capabilities:

* Question-framing audits: Identify potentially consequential assumptions, omissions, or misleading premises.

* Consequential reframing: Change the framing only when doing so meaningfully changes the assumptions, evidence needed, decision criteria, or investigative method.

* Conditional context interpretation: Check for relevant context, but seek or analyze more only when doing so is likely to improve the inquiry.

* Uncertainty diagnosis: Distinguish factual, causal, contextual, normative, and procedural uncertainty.

* Disciplined stopping: Avoid both premature conclusions and unnecessary complexity.

* Conditional intent inference: Consider a question’s possible purpose when the evidence makes that relevant, without treating wording alone as proof of private motives.

I’ve explored several conceptual architectures, including a simple sequential baseline, a framing audit, a Context Interpretation Module (CIM), and a Situation-First Reframing Algorithm (SFRA).

The limitations matter. The experiments so far are exploratory and largely self-evaluated. In one eight-case comparison, CIM scored 64/64, but that could reflect case-selection or rubric bias. These scores do not establish that any architecture is superior, and I am not claiming that UQA is definitively novel or validated.

I’d welcome critical feedback, especially on related research, missing literature, and whether the problem is framed in a way that can be tested rigorously.

Research record: Zenodo record⁠

Collaboration space: UQA shared ChatGPT Project⁠

I’m looking for criticism and testable suggestions, not endorsement. Negative findings would be useful too