Evaluating Test-Time Scaling of General LLM Agents
This study introduced General AgentBench to evaluate general LLM agents across coding, search, and reasoning tasks using a unified framework. It investigated how performance scales when interactions are extended sequentially or sampled in parallel. Findings show sequential scaling hits a context ceiling, while parallel scaling is limited by a gap between generated options and the agent's actual self-selection.