TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent

arXiv cs.AISeptember 24, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2609.27277v1 Announce Type: new Abstract: Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However, we identify two failures in this setup. Human-Agent Tool Misalignment: a library of 21 expert-curated tools helps on some tasks and hurts on others, dropping anomaly accuracy under every backbone we test. Silent Harm: one round of generic self-revision changes 147 answers and breaks 56 of them, while the final score moves by less than a point. Both follow from the same gap: whether a tool helps is decided question by question at runtime, while tools are supplied in advance and judged by a sing

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