Verifiable abstention makes AI leak diagnosis accountable in urban water distribution networks
Leak localization is usually evaluated as forced-choice prediction, although sparse hydraulic observations may not justify excavation. Here, we quantify a pressure-information limit and use it to recast localization as selective, evidence-gated decision-making. A physics-grounded executor falsifies competing leak, demand, sensor and valve hypotheses in a hydraulic twin. Deterministic code computes every number and every acceptance predicate; an independent large language model auditor may add a rejection but never overturn a failed check. Forced retrieval placed only 95 of 300 leaks in the correct zone. Across 550 mixed events, the gate acted on 223 (214 correct); on a third-party 33-leak...
| Authors | Tianwei Mu, Yue Wang, Mingzhe Yuan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 August 2026 |
| Last updated at source | 1 September 2026 |
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