Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents

2024-05-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The Agent Planning Benchmark (APB) is introduced as a diagnostic framework to evaluate planning capabilities in LLM agents, addressing the limitations of end-to-end evaluations. Comprising 4,209 multimodal cases across 22 domains and five settings, APB assesses holistic planning, feedback-conditioned step-wise planning, and robustness to extraneous tools, broken tools, and unsolvable tasks. Evaluations across 12 MLLMs, including GPT-5 and Claude Sonnet 4.5, reveal systematic weaknesses in long-horizon planning, tool-noise robustness, and calibrated refusal. APB-guided refinement consistently improved plan correctness and downstream execution metrics on 200 ToolSandbox and 200 \tau^{2}-bench tasks for models like GPT-4o, Qwen3-VL-235B-A22B, and Gemini 2.5 Flash, positioning APB as a crucial upstream diagnostic tool.

Key takeaway

For AI Scientists and Machine Learning Engineers developing LLM agents, relying solely on end-to-end benchmarks obscures critical planning failures. You should integrate diagnostic frameworks like APB to pinpoint specific weaknesses in holistic planning, tool-noise robustness, and calibrated refusal. Utilize its E1–E6 error taxonomy and inference-time refinement strategies to systematically enhance your agents' reliability and cost-efficiency, particularly for complex, long-horizon tasks.

Key insights

APB diagnostically evaluates LLM agent planning across diverse scenarios, revealing systematic weaknesses and guiding refinement.

Principles

• Planning quality is not monolithic.
• Holistic planning requires global consistency.
• Inference-time refinement improves holistic plans.

Method

APB uses 4,209 multimodal cases across holistic, step-wise, and robustness settings. It employs Plan Correctness, Plan Grade, and an E1–E6 error taxonomy for fine-grained root-cause analysis.

In practice

• Diagnose long-horizon planning failures.
• Improve agent robustness to tool noise.
• Refine plans using error taxonomy guidance.

Topics

• LLM Agents
• Agent Planning
• Diagnostic Benchmarks
• Multimodal LLMs
• Tool Use
• Inference-Time Refinement

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.