What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
Quick Answer
This paper shows that The PACT protocol enhances multi-agent systems by optimizing inter-agent communication, reducing token usage while maintaining performance.
Quick Take
The PACT protocol enhances multi-agent systems by optimizing inter-agent communication, reducing token usage while maintaining performance. It improves task performance with fewer tokens across various MAS topologies, achieving a 10% reduction in tokens-per-resolved for OpenHands and halving input tokens for SWE-agent.
Key Points
- PACT treats inter-agent communication as a public state-update problem.
- It consistently improves performance-cost trade-offs in multi-agent systems.
- OpenHands sees a 10% reduction in tokens-per-resolved with PACT.
- SWE-agent maintains resolve rates while halving input tokens.
- The code for PACT is publicly available on GitHub.
Article Content
From source RSS / original summaryarXiv:2606. 05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language. However, this free-form communication can rapidly inflate token usage, consume the shared context window, and ultimately affect both system performance and inference cost.
We analyze five common inter-agent communication strategies across two MAS topologies, finding that no fixed strategy is universally optimal. Instead, effective inter-agent messages consistently preserve action-centered information needed by downstream agents.
Building on this, we propose the PACT (Protocolized Action-state Communication and Transmission), which treats inter-agent communication as a public state-update problem and projects each raw agent output into a compact action-state record before it enters shared history. Across different MAS topologies, PACT consistently improves the performance-cost trade-off, achieving comparable or stronger task performance with substantially fewer tokens.
The gains extend to production coding harnesses: PACT lifts OpenHands' resolve rate at -10% tokens-per-resolved, and is resolve-neutral on SWE-agent while halving input tokens. Our code is publicly available at https://github. com/iNLP-Lab/PACT.
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