arXiv:2605.07926v2 Announce Type: replace Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar workflows and short-range interactions. We introduce AgentEscapeBench, an escape-room-style benchmark that tests whether agents can infer, execute, and revise novel tool-use procedures under explicit long-range dependency constraints. Each task defines a directed acyclic dependency graph over tools and items, requiring agents to invoke real external functions, track hidden state revealed increme
The rapid advancement and deployment of LLM-based agents necessitates robust evaluation benchmarks to understand their capabilities and limitations in complex, real-world scenarios.
Evaluating LLM agents' out-of-domain reasoning is crucial for their reliable deployment, determining their true utility beyond simple tasks and accelerating their integration into diverse applications.
The introduction of AgentEscapeBench provides a standardized method to assess agent capabilities in 'escape-room-style' challenges, moving beyond familiar workflows to test novel tool-use procedures and long-range dependencies.
- · LLM agent developers
- · Organizations deploying autonomous agents
- · AI safety researchers
- · Tool developers
- · Companies relying on simplistic agent evaluations
- · Agents with poor generalization capabilities
Improved understanding of LLM agent limitations and capabilities.
Faster development and deployment of more robust and adaptable AI agents across industries.
Accelerated adoption of AI agents for complex, mission-critical tasks currently handled by humans.
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Read at arXiv cs.AI