One Observation, One Identity: Joint Association and Truth Discovery for Remote-Sensing Agents

Interpreting objects across optical, SAR, and temporal observations requires resolving two interdependent questions: which reports describe the same physical object, and which reported attribute values are correct. Incorrect associations can mix evidence from different objects and distort attribute estimates, while informative attributes can help resolve otherwise ambiguous matches. We introduce OneID-Agent, which assigns a single shared latent identity to all claims attached to the same physical observation, coupling object association with attribute truth inference. This formulation preserves the common observational origin of related claims and allows evidence from multiple attributes to inform a consistent association. Attribute beliefs jointly refine the shared identity, while the resulting association determines how each claim contributes to object-specific truth estimates. Physical constraints, contextual reliability, and provenance budgets constrain this feedback and keep inference grounded in the available observations. To support query answering, the agent integrates identity and attribute beliefs with evidence coverage and conflict checks to decide whether the available evidence supports an answer or further observations are needed. We evaluate agent execution on RSTIBench and joint inference against an independently annotated reference. Under matched inputs, shared identity improves assignment accuracy and attribute truth prediction compared with assigning separate identities to individual claims. Auxiliary reliability fitting further improves probability quality and answer coverage at the evaluated risk threshold.