Hugging Face Daily PapersJie Yang, Yan Zheng, Jiarui Sun1 min readpaperadvanced
TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent
Summary
TimeEvo is a framework that enables time series agents to self-evolve their toolkits by diagnosing failures and synthesizing new, task-specific tools. It addresses issues like tool misalignment and silent harm, where pre-selected tools can degrade performance or introduce errors unnoticed. Experiments show TimeEvo significantly improves accuracy across various time series QA tasks and models.
- Pre-selected tools for time series agents often lead to "Human-Agent Tool Misalignment" and "Silent Harm" due to task-dependent utility.
- TimeEvo diagnoses agent failures, clusters them into capability gaps, and plans measurements to address them.
- It synthesizes "evidence-only tools" to fill identified gaps, rather than relying on a fixed, human-curated library.
- A "paired admission gate" mechanism is used to selectively admit new tools, ensuring they provide a net benefit.
Engineers developing or deploying LLM agents for time series analysis should consider TimeEvo's approach to dynamically evolving toolkits to overcome limitations of static tool selection and improve agent robustness and performance.
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