New Benchmark Evaluates LLM Agent Management and Subagent Orchestration.

Researchers have introduced ClawArena-Team, a novel benchmark specifically designed to assess the management capabilities of a single large language model (LLM) when it acts as a leader orchestrating a team of specialized subagents. Unlike existing benchmarks that focus on individual task-solving or fixed multi-agent systems, ClawArena-Team isolates the LLM's ability to create, delegate to, and manage subagents through parallel and asynchronous dynamic workflows across 41 multi-turn, multimodal, multi-directory scenarios. The benchmark constrains the main agent to perceive only text and access only parts of the workspace, ensuring that performance differences reflect management skill rather than raw model capability. Evaluation is execution-based, yielding a Subagent-Management Score (SMS) that factors in task correctness, least-privilege, and modality-routing. Experiments with various models showed that privilege granting is a major bottleneck, with no model exceeding 50% precision. Interestingly, API cost and management quality were decoupled, with cheaper open models sometimes on the Pareto frontier, and overall scores clustering tightly despite divergent orchestration behaviors.