Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents
arXiv:2603.09835v1 Announce Type: new 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. Empiri
arXiv:2603.09835v1 Announce Type: new 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.
Executive Summary
This article explores the problem of chunk ordering for long-context reasoning in Chain-of-Agents (CoA), a sequential multi-agent reasoning framework. By employing Chow-Liu trees to learn dependency structures, the authors demonstrate that prioritizing strongly related chunks through breadth-first traversal can reduce information loss and improve answer relevance and exact-match accuracy. This work contributes to the development of more efficient and effective long-context reasoning models. The implications of this research are significant, particularly in the context of natural language processing and artificial intelligence applications.
Key Points
- ▸ The authors propose using Chow-Liu trees to learn dependency structures for chunk ordering in CoA.
- ▸ Breadth-first traversal of the resulting tree reduces information loss and improves performance.
- ▸ The approach outperforms default document-chunk ordering and semantic score-based ordering in three long-context benchmarks.
Merits
Improved Performance
The proposed approach demonstrates superior performance in answer relevance and exact-match accuracy compared to existing methods.
Demerits
Limited Generalizability
The results may not generalize to other domains or benchmark datasets, requiring further investigation to establish the robustness of the proposed approach.
Computational Complexity
The use of Chow-Liu trees may introduce additional computational complexity, potentially limiting the scalability of the approach.
Expert Commentary
The work presented in this article demonstrates a significant contribution to the field of long-context reasoning, particularly in the context of Chain-of-Agents frameworks. The use of Chow-Liu trees to learn dependency structures offers a promising approach to reducing information loss and improving performance. However, the limitations of the research, including potential generalizability issues and computational complexity concerns, must be carefully considered. As the field continues to evolve, it is essential to investigate the robustness and scalability of the proposed approach to ensure its practical applicability.
Recommendations
- ✓ Future research should focus on exploring the generalizability of the proposed approach across different domains and benchmark datasets.
- ✓ Investigating the computational complexity of the approach and exploring optimization techniques to enhance scalability is essential.