EviDiag: Posterior-Guided Evidence Seeking for Auditable Time-Series Diagnosis

Time-series diagnosis commonly maps observed signals directly to a fault or root-cause label, leaving evidence acquisition and decision making implicit. Although large language models (LLMs) have enabled readable diagnostic reasoning, existing approaches typically lack explicit mechanisms for sequentially planning evidence acquisition and quantitatively revising fault beliefs. This challenge is compounded in multivariate time series, where fault evidence is often buried in context-dependent temporal dynamics rather than directly available in text or logs. We present EviDiag, a posterior-guided evidence-seeking agent that turns diagnosis into an auditable process of examination and belief revision. EviDiag recovers a context-conditioned healthy trajectory from a pre-fault anchor to ground subsequent examinations, and organizes diagnosis into a within-case inquiry loop and an across-case learning loop. The within-case inquiry loop uses the fault posterior and finite-horizon information value to guide an LLM planner in selecting the next examination, whose outcome revises the fault belief via Bayesian updating. The across-case loop distills resolved state-action-outcome transitions into retrievable experience and recalibrates evidence likelihoods to improve future inquiry. Across microservice and industrial-process benchmarks, Across microservice and industrial-process benchmarks, EviDiag attains 1.06× and 1.90× the top-1 diagnostic accuracy of the best baseline, respectively, while producing auditable evidence-to-posterior traces.