AI RESEARCH
Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents
arXiv CS.CL
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ArXi:2603.09835v1 Announce Type: new 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.