Experiments, cultural preparation and research judgment [ftip-00NO]

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Informative experiments can improve either representation discovery or forecasts of future demand. A contributor may learn which intervention distinguishes plausible decompositions or exposes a misleading progress measure. In the public-rule setting, both campaigns can execute the same permitted experiments; the question is the cost of choosing and interpreting them. Criticality-guided learning learns to target failure-prone conditions from execution outcomes, showing why rarity under random sampling is too weak an argument. Its predefined state representations remain a relevant limitation of that implementation. A physical experiment with unequal access may support a different, explicitly informational or interaction-rate result.

Cultural preparation can supply earlier exploration, tested concepts and teaching practices. Model it as a bounded population that generates, selects and transmits methods, or disclose its products as inherited endowments. Failed discoveries, selection and transmission count; so do analogous shared data, model populations and libraries on the closed side. The study of AI-discovered strategies in human culture separates difficulty of discovery, ease of transmission and recognizable advantage. Its AI-to-human direction supports an origin-neutral contribution. Neither that experiment nor its population simulations establish a general discovery lower bound. Prior cultural work can explain a marginal benefit, while lifetime and amortized claims require the accounting in § [ftip-00ND].

Research judgment can help choose a fruitful subproblem, recognize when an abstraction loses an essential distinction, or improve an intermediate progress measure. Such choices affect how computation is spent; they do not change the final mathematical task or checker. The study of divergence and negation in scientific ideas reports differences between expert ratings and several automated evaluations, while also showing benefits from learning a reward model on human ratings. This motivates acquired judgment without treating it as a human monopoly. Expert ratings of ideas do not directly certify mathematical discovery. For this construction, any learned progress measure must justify its value through fresh checked outcomes, with its own training and validation charged.

These three contributions have distinct roles: experiments produce useful evidence, culture explains how prior useful structure was generated and transmitted, and judgment guides selection and evaluation. They can support both major constructions, but their benefits do not automatically add. A curriculum learned from cultural demonstrations and selected by an experimental progress measure may share preparation across all three. Ablations should vary one mechanism at a time, and total accounting should charge shared work once.