Sequential multi-agent reasoning frameworks such as Chain-of-Agents (CoA) handle long-context queries by decomposing inputs into chunks and processing them sequentially using LLM-based worker agents that read from and update a bounded shared memory. From a probabilistic perspective, CoA aims to approximate the conditional distribution corresponding to a model capable of jointly reasoning over the entire long context. CoA achieves this through a latent-state factorization in which only bounded summaries of previously processed evidence are passed between agents. The resulting bounded-memory approximation introduces a lossy information bottleneck, making the final evidence state inherently dependent on the order in which chunks are processed. In this work, we study the problem of chunk ordering for long-context reasoning. We use the well-known Chow-Liu trees to learn a dependency structure that prioritizes strongly related chunks. Empirically, we show that a breadth-first traversal of the resulting tree yields chunk orderings that reduce information loss across agents and consistently outperform both default document-chunk ordering and semantic score-based ordering in answer relevance and exact-match accuracy across three long-context benchmarks.

翻译：顺序多智能体推理框架，例如链式智能体（CoA），通过将输入分解为多个片段，并利用基于大语言模型的工作智能体顺序处理这些片段来处理长上下文查询。这些工作智能体从一个有界的共享内存中读取信息并对其进行更新。从概率角度来看，CoA旨在近似于一个能够对整个长上下文进行联合推理的模型所对应的条件分布。CoA通过一种潜在状态因子分解来实现这一目标，其中仅在智能体之间传递先前处理过的证据的有界摘要。由此产生的有界内存近似引入了一个有损信息瓶颈，使得最终证据状态本质上依赖于片段被处理的顺序。在本工作中，我们研究了长上下文推理中的片段排序问题。我们使用著名的Chow-Liu树来学习一种依赖结构，该结构优先处理强相关的片段。实验表明，对生成的树进行广度优先遍历所产生的片段排序，能够减少智能体间的信息损失，并且在三个长上下文基准测试中，在答案相关性和精确匹配准确率方面，持续优于默认的文档片段排序和基于语义得分的排序。