PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems

Analysis

Why This Paper Matters

As LLMs are increasingly deployed as autonomous agents, their ability to plan over long horizons and select from vast tool ecosystems becomes paramount. This paper introduces PlanBench-XL, a benchmark that directly tests these capabilities at scale. The finding that most models remain below two-thirds accuracy underscores a fundamental limitation in current LLM-based planning, which is critical for applications like automated software development, scientific discovery, and enterprise workflows.

The significance lies in exposing the gap between simple tool use (e.g., single API calls) and complex, multi-step planning over hundreds or thousands of tools. This work provides a much-needed stress test for the community, pushing beyond existing benchmarks that often use small, curated tool sets.

Technical Contributions

• PlanBench-XL benchmark: A new evaluation suite featuring a large-scale tool ecosystem with diverse, realistic tools.
• Long-horizon planning tasks: Multi-step problems requiring sequential tool selection, parameterization, and error recovery.
• Systematic evaluation: Tests multiple LLM families (e.g., GPT, Llama) under controlled settings to isolate planning challenges.
• Default setting analysis: Establishes a baseline showing that even state-of-the-art models struggle with scale and horizon.

Results

• Most models achieve less than 66.7% accuracy on the default setting.
• Performance degrades sharply as the number of available tools increases beyond a few hundred.
• No model consistently outperforms others across all task complexities, indicating a general planning bottleneck.

Significance

This paper sets a new challenge for the AI community, highlighting that scaling tool ecosystems exposes fundamental weaknesses in LLM planning. It will likely spur research into hierarchical planning, retrieval-augmented tool selection, and memory-augmented agents. For practitioners, it suggests that current LLM agents are not yet reliable for complex, real-world tool orchestration tasks, guiding investment in more robust planning frameworks.