Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
{Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under strict time constraints.} {The core challenge lies in the belief-space objective: valid uncertainty estimation requires expensive Bayesian inference, whereas using fast learned belief model leads to reward hacking, in which the policy exploits approximation errors rather than actually reducing uncertainty.} {We propose \textbf{Distill-Belief}, a teacher--student framework that decouples correctness from efficiency. A Bayes-correct particle-filter teacher maintains the posterior and supplies a dense information-gai...
| Authors | Yiwei Shi, Zixing Song, Mengyue Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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