COSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Context
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918154810163200 |
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| author | Gupta, Naman Gowaikar, Shreeyash Iyer, Arun Shiragur, Kirankumar Bairi, Ramakrishna B Maurya, Rishikesh Maiti, Ritabrata Damle, Sankarshan Gupta, Shachee Mishra |
| author_facet | Gupta, Naman Gowaikar, Shreeyash Iyer, Arun Shiragur, Kirankumar Bairi, Ramakrishna B Maurya, Rishikesh Maiti, Ritabrata Damle, Sankarshan Gupta, Shachee Mishra |
| contents | Reasoning over very long inputs remains difficult for large language models (LLMs). Common workarounds either shrink the input via retrieval (risking missed evidence), enlarge the context window (straining selectivity), or stage multiple agents to read in pieces. In staged pipelines (e.g., Chain of Agents, CoA), free-form summaries passed between agents can discard crucial details and amplify early mistakes. We introduce COSMIR (Chain Orchestrated Structured Memory for Iterative Reasoning), a chain-style framework that replaces ad hoc messages with a structured memory. A Planner agent first turns a user query into concrete, checkable sub-questions. worker agents process chunks via a fixed micro-cycle: Extract, Infer, Refine, writing all updates to the shared memory. A Manager agent then Synthesizes the final answer directly from the memory. This preserves step-wise read-then-reason benefits while changing both the communication medium (structured memory) and the worker procedure (fixed micro-cycle), yielding higher faithfulness, better long-range aggregation, and auditability. On long-context QA from the HELMET suite, COSMIR reduces propagation-stage information loss and improves accuracy over a CoA baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | COSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Context Gupta, Naman Gowaikar, Shreeyash Iyer, Arun Shiragur, Kirankumar Bairi, Ramakrishna B Maurya, Rishikesh Maiti, Ritabrata Damle, Sankarshan Gupta, Shachee Mishra Artificial Intelligence Machine Learning Reasoning over very long inputs remains difficult for large language models (LLMs). Common workarounds either shrink the input via retrieval (risking missed evidence), enlarge the context window (straining selectivity), or stage multiple agents to read in pieces. In staged pipelines (e.g., Chain of Agents, CoA), free-form summaries passed between agents can discard crucial details and amplify early mistakes. We introduce COSMIR (Chain Orchestrated Structured Memory for Iterative Reasoning), a chain-style framework that replaces ad hoc messages with a structured memory. A Planner agent first turns a user query into concrete, checkable sub-questions. worker agents process chunks via a fixed micro-cycle: Extract, Infer, Refine, writing all updates to the shared memory. A Manager agent then Synthesizes the final answer directly from the memory. This preserves step-wise read-then-reason benefits while changing both the communication medium (structured memory) and the worker procedure (fixed micro-cycle), yielding higher faithfulness, better long-range aggregation, and auditability. On long-context QA from the HELMET suite, COSMIR reduces propagation-stage information loss and improves accuracy over a CoA baseline. |
| title | COSMIR: Chain Orchestrated Structured Memory for Iterative Reasoning over Long Context |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.04568 |