Self-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss
Agents that act on a compressed representation of their history face a structural risk: if the representation aliases histories with different optimal actions, no rule measurable with respect to the representation can avoid an irreducible per-round loss, and the agent may be unable to detect this from its own transcript. This paper develops a four-layer theory of self-certification of representation adequacy. The static layer defines decision-theoretic adequacy through a Bayes-risk grouping identity and prices a one-shot external verification by an exact total-variation threshold. The sequential layer poses certification as an optimal-stopping problem in the currency of task loss: we defi...
| Authors | Zijie Huang |
|---|---|
| Primary category | cs.AI |
| Published | 3 August 2026 |
| Last updated at source | 12 September 2026 |
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