Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
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Computer Science > Computation and Language
Title:Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
Abstract:Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent evaluations often report only end-to-end success, making it difficult to determine whether failures stem from planning or execution. We introduce \textbf{Agent Planning Benchmark (APB)}, a planning-specific diagnostic benchmark with 4,209 multimodal cases across 22 domains and five settings, covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs, APB reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. We further validate APB on 200 ToolSandbox tasks and 200 $\tau^2$-bench tasks, where APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics across three representative models. APB thus serves as an upstream diagnostic complement to execution benchmarks.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.04874 [cs.CL] |
| (or arXiv:2606.04874v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04874
arXiv-issued DOI via DataCite (pending registration)
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