SOURCE-LINKED INTELLIGENCE
Revelation Control
Revelation Control is the problem of choosing priced interventions that reveal hidden state only insofar as the revealed distinctions can change a consequential decision, while accounting separately for any useful progress created by the intervention itself. We develop this theory for learning systems, where states equivalent under declared current information can respond differently to future training and favor different actions. The framework defines decision-sufficient revelation and revelation depth, separates pure information value from productive reuse, embeds static Bayes refinement int
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-24T22:07:57.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.