通用大语言模型代理的基准测试时扩展研究

摘要

随着LLM智能体日益被期待成为能够处理开放式用户请求的通用系统，现有基准测试主要聚焦于开发专用智能体的领域感知环境，而评估通用智能体则需要更贴近现实的设定，以检验其在统一环境中跨越多重技能与工具的操作能力。我们推出General AgentBench基准测试框架，为评估通用LLM智能体在搜索、编程、推理及工具使用等领域的表现提供统一平台。通过该框架，我们系统研究了顺序扩展（迭代交互）与并行扩展（多轨迹采样）两种测试时扩展模式下的性能变化规律。对十款主流LLM智能体的评估表明，从领域特定评估转向通用智能体设定时存在显著的性能衰减现象。进一步分析发现，由于顺序扩展中的上下文天花板效应与并行扩展中的验证鸿沟这两大根本性局限，实践中两种扩展方法均未能实现有效的性能提升。相关代码已开源：https://github.com/cxcscmu/General-AgentBench。

English

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.

通用大语言模型代理的基准测试时扩展研究

Benchmark Test-Time Scaling of General LLM Agents

摘要

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