CRAB-Bench: Evaluating LLM Agents under Complex Task Dependencies and Human-aligned User Simulation
arXiv:2606.01815v1 Announce Type: new Abstract: Evaluating LLM agents in realistic service scenarios requires complex task dependencies, imperfect user behavior, and an evaluation that accommodates multiple valid solutions. We introduce CRAB-Bench (Constraint-based Realistic Agent Benchmark) and RUSE (Realistic User Simulation Engine) to address this gap. CRAB-Bench generates tasks via a constraint graph over multiple interdependent entities with structured distractors, requiring agents to reason carefully over thousands of misleading candidates where only a tiny fraction of solutions are vali
The rapid advancement and deployment of LLM agents across various applications necessitate more robust evaluation methodologies to ensure their reliability and real-world applicability.
Evaluating LLM agents under complex, realistic conditions is critical for their safe, effective, and widespread deployment, impacting their integration into enterprise and consumer workflows.
The introduction of CRAB-Bench and RUSE provides a standardized benchmark for evaluating LLM agents that accounts for complex task dependencies and human-aligned user simulation, changing how agent performance is measured.
- · AI agent developers
- · AI safety researchers
- · Enterprises deploying agents
- · LLM agent benchmarks lacking realism
- · Developers unable to validate agent performance rigorously
Improved evaluation leads to more reliable and capable LLM agents being developed and deployed.
Increased trust in autonomous agents accelerates their adoption across critical sectors, transforming labor markets and service industries.
The enhanced capability and reliability of agents contribute to the broader 'AI agents' narrative, potentially accelerating the automation of white-collar work.
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Read at arXiv cs.CL