AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints
arXiv:2606.05622v1 Announce Type: new Abstract: Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual constraints. To address this gap, we introduce AdaPlanBench, a dynamic interactive benchmark for evaluating whether Large Language Model (LLM) agents can adaptively plan and re-plan under progressively revealed world and user constraints. AdaPlanBench i
The rapid advancement and deployment of Large Language Models necessitate robust evaluation benchmarks for their practical application in complex, dynamic environments.
Evaluating LLM agents' adaptive planning capability under evolving real-world constraints is crucial for their reliable and autonomous deployment in critical white-collar workflows.
The introduction of AdaPlanBench provides a standardized, dynamic tool for assessing a key limitation in current LLM agents, pushing towards more resilient and capable AI systems.
- · AI developers
- · Enterprises adopting AI agents
- · Cloud AI providers
- · Researchers in LLM planning
- · Developers of brittle or non-adaptive AI agents
- · Sectors reliant on static AI solutions
Improved benchmarks accelerate the development of more robust and adaptable AI agents.
More reliable AI agents lead to faster automation of complex tasks across various industries.
Increased trust in autonomous AI systems could further consolidate power for companies with advanced agentic technology.
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Read at arXiv cs.CL