Graph of Agents: Principled Long Context Modeling by Emergent Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2509.21848v1
• Date: Fri, 26 Sep 2025 04:15:40 GMT
• Title: Graph of Agents: Principled Long Context Modeling by Emergent Multi-Agent Collaboration
• Abstract summary: We introduce a principled framework that formalizes the model-agnostic long context modeling problem as a compression problem.<n>We propose Graph of Agents (GoA), which dynamically constructs an input-dependent collaboration structure that maximizes this objective.<n>GoA surpasses the 128K context window Llama 3.1 8B on LongBench, showing a dramatic increase in effective context length.
• Score: 9.151759069858924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a model-agnostic approach to long context modeling, multi-agent systems can process inputs longer than a large language model's context window without retraining or architectural modifications. However, their performance often heavily relies on hand-crafted multi-agent collaboration strategies and prompt engineering, which limit generalizability. In this work, we introduce a principled framework that formalizes the model-agnostic long context modeling problem as a compression problem, yielding an information-theoretic compression objective. Building on this framework, we propose Graph of Agents (GoA), which dynamically constructs an input-dependent collaboration structure that maximizes this objective. For Llama 3.1 8B and Qwen3 8B across six document question answering benchmarks, GoA improves the average $F_1$ score of retrieval-augmented generation by 5.7\% and a strong multi-agent baseline using a fixed collaboration structure by 16.35\%, respectively. Even with only a 2K context window, GoA surpasses the 128K context window Llama 3.1 8B on LongBench, showing a dramatic increase in effective context length. Our source code is available at https://github.com/tjoo512/graph-of-agents.

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