Learned systems have contributed to conjecture generation and to proof search in specific settings. The open question is whether they can surface structure that is genuinely new and mathematically useful, rather than recombining material already present in their training data. Answering it requires a criterion for "new structure" that mathematicians would accept in advance, which does not currently exist.
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Randomisation establishes internal validity within a trial population. Extending the result to a different population requires assumptions that are rarely stated and almost never tested. This asks which characteristics of a trial predict whether its effect estimate survives in routine practice.
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Some evaluations report sharp capability changes with scale. Others argue the sharpness is an artefact of discontinuous scoring rather than a property of the model. Both positions have supporting analyses. This is a measurement question, which means it is resolvable — and resolving it would change how a large body of capability claims should be read.
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Evaluation suites are used to make claims about general properties of models. Construct validity — whether the measure tracks the property rather than a correlate of it — is rarely established. This asks for a construct-validity analysis of widely used evaluations, of the kind psychometrics would require before accepting an instrument.
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Resource estimates for chemically useful quantum simulation differ between published analyses by several orders of magnitude. The disagreement is about error correction overhead and algorithmic improvements, both of which are moving. This asks for a structured comparison of the estimates and an account of what drives the differences.
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- $5.6k
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