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

Key points

• Interactive-agent evaluations commonly rely on standardized LLM-based user simulators that exhibit broadly cooperative behavior, potentially overlooking failures that arise under diverse but realistic user interactions.1
• Existing approaches largely rely on predefined user behaviors, which may miss agent- or domain-specific weaknesses, while failure-oriented search can reduce task success by invalidating the underlying task rather than exposing genuine robustness failures.1
• Further analysis yields a behavior-to-failure taxonomy that connects discovered user behaviors to recurring agent failure modes.1
• Together, these results show that TIPER enables more diagnostic robustness evaluation of interactive agents and provides informative failure analysis for future agent robustness improvement.1

Sources (1)

• [1]Uncovering Robustness Weaknesses in LLM Agents via Persona Evolution with Task-Integrity Constraints
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:30 AM

  To fill this gap, we introduce TIPER, a task-integrity-preserving persona evolution method that automatically discovers agent-specific user policies exposing robustness weaknesses while keeping the underlying task valid and solvable.

  Interactive-agent evaluations commonly rely on standardized LLM-based user simulators that exhibit broadly cooperative behavior, potentially overlooking failures that arise under diverse but realistic user interactions.

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