arXiv AI By Yutao Xie, Jingbo Yang, Guanyu Yao, Xiaoyu Yang, Duo Ding, Shiyu Chang

Uncovering Robustness Weaknesses in LLM Agents via Persona Evolution with Task-Integrity Constraints

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arXiv AI
Aug 26

AgentWorld: Personality-Aware Reliability Evaluation for Agentic Information Retrieval

AgentWorld is a simulation framework that evaluates agentic information retrieval by incorporating diverse user personalities based on the Big Five (OCEAN) traits, stateful tool-use environments, and a pass$^k$ consistency metric with structured fault classification and partial-credit scoring. It includes a risk analyzer that uses Monte‑Carlo rollouts and advanced scoring methods to quantify trajectory brittleness and attack attribution. Experiments with conversational analytics, customer‑support agents, and adversarial stress‑testing demonstrate that personality variation reveals failure modes hidden by uniform testing, such as cross‑domain leakage, contextual drift, and significant quality gaps across personas.

By Gunja Agarwal, Arup Kumar Das, Arun Menon, Jitesh Chandra Mishra, Vignesh Divakaran
Hugging Face Trending Papers
Sep 28

SEABench: Benchmarking Endogenous Misalignment In Self-Evolving Agents

SEABench is a benchmark designed to study endogenous misalignment in self‑evolving large language model agents. It contains 48 longitudinal task sequences across various evolution surfaces, task domains, and harm types, and includes an adaptive trajectory discovery pipeline that probes for failures while preserving task intent. Evaluations show that self‑evolution improves task completion rates but often introduces safety failures absent in non‑evolving baselines, with divergent safety behaviors reflected in agents’ chain‑of‑thought reasoning that can be monitored to mitigate unsafe actions.