arXiv:2606.04874v1 Announce Type: new 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 robustnes
The rapid advancement and deployment of LLM agents have exposed limitations in evaluating their planning capabilities, necessitating more robust diagnostic tools.
This benchmark provides a critical framework for understanding and improving the core planning functions of LLM agents, which are essential for their autonomous operation and broader adoption.
The ability to accurately diagnose planning failures, rather than just end-to-end success, allows for targeted improvements in agent design and performance.
- · AI researchers
- · LLM developers
- · Agentic AI platforms
- · Automation software providers
- · Inefficient LLM agents
- · Undifferentiated agent evaluation methods
Improved planning capabilities will lead to more reliable and effective LLM agents.
More reliable agents will accelerate the automation of complex white-collar tasks, impacting various industries.
The increased sophistication of agents could lead to new forms of human-AI collaboration and potentially altered labor markets.
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