Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914515098009600 |
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| author | Shi, Yiwei Song, Zixing Yang, Mengyue Liu, Cunjia Liu, Weiru |
| author_facet | Shi, Yiwei Song, Zixing Yang, Mengyue Liu, Cunjia Liu, Weiru |
| contents | {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-gain signal, while a compact student distills the posterior into belief statistics for control and an uncertainty certificate for stopping. At deployment, only the student is used, yielding constant per-step cost.} {Experiments on seven field modalities and two stress tests show that Distill-Belief consistently reduces sensing cost and improves success, posterior contraction, and estimation accuracy over baselines, while mitigating reward hacking.} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26095 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields Shi, Yiwei Song, Zixing Yang, Mengyue Liu, Cunjia Liu, Weiru Artificial Intelligence {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-gain signal, while a compact student distills the posterior into belief statistics for control and an uncertainty certificate for stopping. At deployment, only the student is used, yielding constant per-step cost.} {Experiments on seven field modalities and two stress tests show that Distill-Belief consistently reduces sensing cost and improves success, posterior contraction, and estimation accuracy over baselines, while mitigating reward hacking.} |
| title | Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.26095 |