Computer Science > Computation and Language
arXiv:2603.09835 (cs)
[Submitted on 10 Mar 2026]
Title:Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents
Authors:Naman Gupta, Vaibhav Singh, Arun Iyer, Kirankumar Shiragur, Pratham Grover, Ramakrishna B. Bairi, Ritabrata Maiti, Sankarshan Damle, Shachee Mishra Gupta, Rishikesh Maurya, Vageesh D. C
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Abstract: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.
| Comments: | |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09835 [cs.CL] |
| (or arXiv:2603.09835v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09835
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View a PDF of the paper titled Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents, by Naman Gupta and 10 other authors
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