ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
arXiv:2606.31174v1 Announce Type: new Abstract: Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and orchestrates their parallel, asynchronous returns through dynamic workflows. Whether one model can actually run such a team is largely unmeasured: existing benchmarks score a policy's own task-solving or a fixed multi-agent system's emergent behavior, but none isolate the management ability of the single LLM acting as leader. We introduce ClawArena-Team, a benchmark of
The proliferation of language models and agentic systems necessitates robust benchmarking to understand their capabilities, particularly in complex orchestration tasks.
Evaluating an LLM's capacity to manage and orchestrate subagents is critical for the development of effective autonomous AI systems that can execute multi-step, dynamic workflows.
The introduction of ClawArena-Team provides a dedicated benchmark for assessing the 'managerial' abilities of LLMs, shifting focus from individual task-solving to complex team coordination.
- · AI agent developers
- · Companies investing in autonomous workflow automation
- · Researchers in multi-agent systems
- · LLM providers with strong orchestration capabilities
- · AI projects relying solely on single-agent task completion
- · Benchmarking methodologies focused only on individual model performance
Improved understanding and development of LLMs as orchestrators of complex agentic systems.
Accelerated deployment of more sophisticated and autonomous AI agents capable of handling dynamic, multi-stage problems.
Increased efficiency in knowledge work and white-collar automation as AI agents take on more managerial and coordination roles.
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Read at arXiv cs.AI