ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps
[Sztuczna inteligencja] Autorzy: Miaobo Hu, Shuhao Hu, Xiaobo Guo i 5 więcej
A multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. We study this problem under a strict free-metadata/paid-observation boundary, stochastic vector costs, and a frozen execution trace, and treat it as decision-certifiable active evidence acquisition. A Counterfactual Evidence Interaction Graph (CEIG) represents unary evidence value, signed second-order interaction, and directed conditional harm while maintaining explicit score enclosures for unresolved interactions; a decision-weighted enclosure objective tightens the bounds that can change action identity. Gap-directed refinement evaluates only interactions that can displace the incumbent acquisition and stops once an $ε$-decision certificate shows that unresolved terms cannot change the selected action beyond …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja