DPBench: Structural Determinants of Multi-Agent LLM Coordination Under Simultaneous Resource Contention
arXiv:2602.13255v2 Announce Type: replace Abstract: We present DPBench, a benchmark for evaluating coordination in multi-agent systems built from large language models. Existing benchmarks measure task-level success under a fixed protocol; the structural conditions under which coordination succeeds or fails at all have not been characterised. DPBench adapts the Dining Philosophers problem into a controlled testbed where the action protocol, the communication structure, and the group size each vary independently. We evaluate six agents: GPT-5.2, Claude Opus 4.5, Grok 4.1, Gemini 2.5 Flash, Llam
The proliferation of advanced large language models necessitates robust evaluation of their performance and emergent capabilities in multi-agent, dynamic environments. This benchmark addresses a critical gap in understanding how these agents coordinate under realistic constraints.
A strategic reader should care because multi-agent LLM coordination is fundamental to the development of sophisticated AI agents, impacting their reliability, scalability, and deployment in complex real-world applications. The benchmark reveals structural factors influencing their success or failure.
We now have a standardized, controlled testbed (DPBench) to systematically evaluate and compare the coordination capabilities of different LLMs in multi-agent settings, moving beyond task-level success to structural determinants of coordination.
- · AI Agent Developers
- · LLM Providers (e.g., Google, OpenAI, Anthropic)
- · AI Safety Researchers
- · Software Developers
- · LLMs with poor coordination capabilities
- · Developers relying solely on fixed-protocol benchmarks
This benchmark will accelerate research and development into more robust and reliable multi-agent AI systems capable of handling resource contention and complex interactions.
Improved coordination in multi-agent LLMs could lead to the acceleration of autonomous agent deployment in various industries, from logistics to software development, collapsing white-collar workflows faster than anticipated.
As multi-agent systems become more sophisticated and autonomous, societal frameworks for regulation, accountability, and human-AI collaboration will need significant adaptation, potentially leading to new governance models for AI behavior.
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Read at arXiv cs.AI