Benchmark Test-Time Scaling of General LLM Agents
arXiv:2602.18998v1 Announce Type: new 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 yield
arXiv:2602.18998v1 Announce Type: new 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.
Executive Summary
This study introduces General AgentBench, a unified benchmark for evaluating general-purpose large language model (LLM) agents across various skills and tools. The evaluation reveals a significant performance degradation in a general-agent setting compared to domain-specific evaluations. The authors investigate test-time scaling behaviors under sequential and parallel scaling methodologies, identifying context ceiling and verification gap as fundamental limitations. The findings have significant implications for the development and deployment of general-purpose LLM agents.
Key Points
- ▸ General AgentBench provides a unified framework for evaluating general LLM agents.
- ▸ Existing LLM agents experience significant performance degradation in general-agent settings.
- ▸ Sequential and parallel scaling methodologies have limitations, including context ceiling and verification gap.
Merits
Strength of General AgentBench
The study introduces a comprehensive and unified benchmark for evaluating general-purpose LLM agents, addressing the need for more realistic settings in LLM development.
Demerits
Limitation of Sequential Scaling
The context ceiling in sequential scaling restricts effective performance improvements, limiting the applicability of this methodology.
Limitation of Parallel Scaling
The verification gap in parallel scaling hinders the achievement of robust performance improvements, indicating a need for alternative approaches.
Expert Commentary
This study marks a crucial step in the development of general-purpose LLM agents, highlighting the need for more comprehensive and unified benchmarks. The findings demonstrate that existing LLM agents struggle to adapt to diverse tasks and settings, underscoring the importance of addressing these limitations. As AI continues to permeate various aspects of life, the development of robust and general-purpose LLM agents will be essential for ensuring seamless interactions and effective decision-making.
Recommendations
- ✓ Future research should focus on developing novel methodologies for scaling and evaluating general-purpose LLM agents, addressing the limitations identified in this study.
- ✓ Developers should prioritize the creation of more comprehensive and realistic benchmarks, mirroring the unified framework introduced in this study.