Benchmark Test-Time Scaling of General LLM Agents

• URL: http://arxiv.org/abs/2602.18998v1
• Date: Sun, 22 Feb 2026 01:08:02 GMT
• Title: Benchmark Test-Time Scaling of General LLM Agents
• Authors: Xiaochuan Li, Ryan Ming, Pranav Setlur, Abhijay Paladugu, Andy Tang, Hao Kang, Shuai Shao, Rong Jin, Chenyan Xiong,
• Abstract summary: General AgentBench is a benchmark for evaluating general LLM agents across search, coding, reasoning, and tool-use domains.<n>We study performance degradation when moving from domain-specific evaluations to this general-agent setting.<n>We find that neither scaling yields effective performance improvements in practice, due to two fundamental limitations.
• Score: 27.756239376314294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM agents are increasingly expected to function as general-purpose systems capable of resolving open-ended user requests. While existing benchmarks focus on domain-aware environments for developing specialized agents, evaluating general-purpose agents requires more realistic settings that challenge them to operate across multiple skills and tools within a unified environment. We introduce General AgentBench, a benchmark that provides such a unified framework for evaluating general LLM agents across search, coding, reasoning, and tool-use domains. Using General AgentBench, we systematically study test-time scaling behaviors under sequential scaling (iterative interaction) and parallel scaling (sampling multiple trajectories). Evaluation of ten leading LLM agents reveals a substantial performance degradation when moving from domain-specific evaluations to this general-agent setting. Moreover, we find that neither scaling methodology yields effective performance improvements in practice, due to two fundamental limitations: context ceiling in sequential scaling and verification gap in parallel scaling. Code is publicly available at https://github.com/cxcscmu/General-AgentBench.

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