Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails. We present AgentChaosBench, a benchmark for detecting and local...
Chen-Kai Zhang, Yi-Ran Li, Yi-Fang Tian et al.· Proceedings of the 1st Inter...· 0 citations
Large language models (LLMs) can generate clinical narratives that are insufficiently grounded in patient-specific evidence. In traditional Chinese medicine (TCM), errors can propagate from etiology and pathogenesis through syndrome diagnosis and treatment principles to prescription generation. We developed TCMClinical...
Ji Dai, Chen-Kai Zhang, Yan Jia et al.· 0 citations
AGENTCHAOSBENCH is presented, a benchmark for detecting and localizing runtime faults in agentic systems from their execution telemetry, and its held-out labels and compact prediction format support reproducible comparison of LLM-based and non-LLM diagnosis methods.
Chenkai Zhang, Yiran Li, Yifang Tian et al.· 0 citations
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