{"type":"ScholarlyArticle","title":"Belief-Contraction-Driven Active Inverse Source Localization and Characterization","authors":[{"name":"Yiwei Shi","slug":"yiwei-shi","given":"Yiwei","family":"Shi","affiliation":""},{"name":"Mengyue Yang","slug":"mengyue-yang","given":"Mengyue","family":"Yang","affiliation":""},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":""},{"name":"Cunjia Liu","slug":"cunjia-liu","given":"Cunjia","family":"Liu","affiliation":""},{"name":"Weinan Zhang","slug":"weinan-zhang","given":"Weinan","family":"Zhang","affiliation":""},{"name":"Weiru Liu","slug":"weiru-liu","given":"Weiru","family":"Liu","affiliation":""}],"abstract":"Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, where a mobile sensor must infer latent source parameters from sparse, noisy readings. We introduce a belief-contraction-driven approach that unifies inference, stopping, and control. An attention-augmented particle filter stabilizes Bayesian belief updates through ESS-based resampling, feature-aware sparse attention smoothing, and Metropolis–Hastings rejuvenation that preserves the filtering posterior. Belief contraction (posterior dispersion) defines both a termination rule and a goal-aligned intrinsic reward, enabling reinforcement learning without distance-to-source shaping. Across seven field modalities, spatial out-of-distribution tests, and nonstationary source shifts, our agent (ATT-PFRL) achieves higher completion, faster convergence, and more accurate localization than planning and RL+Bayes baselines under similar computation. Fixed-trajectory studies also show improved ESS and lower RMSE, isolating the benefit of the inference layer.","doi":"10.24963/ijcai.2026/726","url":"https://ijcai.org/proceedings/2026/726","pdf":"https://ijcai.org/proceedings/2026/0726.pdf","markdown":"https://ijcai.org/proceedings/2026/726.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/726","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":726,"pages":{"first":6528,"last":6535},"keywords":["Advanced AI4Tech: Deep AI4Tech","Advanced AI4Tech: AI4Tech foundations","Advanced AI4Tech: Data-driven AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}