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PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents

Research LLM Agents

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TL;DR - PeakBench evaluates whether LLM agents can correctly parallelize multi-tool workflows while respecting resource limits. It matters because sound dependency planning alone does not ensure safe, low-latency execution.

  • Provides executable workflows with execution-grounded dependencies and measured resource profiles.
  • Separately evaluates logical dependency planning and physical resource-constrained scheduling.
  • Finds that strong planners can still execute inefficiently or trigger avoidable resource overflows.
  • Shows that exposing resource information can reduce overflows and improve utilization.

Sources (1)

PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents

arXiv cs.AI Zhi-Kai Chen, Xu-Xiang Zhong, Song-Yan Li, De-Chuan Zhan, Han-Jia Ye 2026-08-25 arXiv:2608.24509
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-17 14:28:17.244534 UTC

TL;DR - PeakBench evaluates whether LLM agents can correctly parallelize multi-tool workflows while respecting resource limits. It matters because sound dependency planning alone does not ensure safe, low-latency execution.

  • Provides executable workflows with execution-grounded dependencies and measured resource profiles.
  • Separately evaluates logical dependency planning and physical resource-constrained scheduling.
  • Finds that strong planners can still execute inefficiently or trigger avoidable resource overflows.
  • Shows that exposing resource information can reduce overflows and improve utilization.
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