Reuse, Don't Recompute: Efficient Large Reasoning Model Inference via Memory Orchestration
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914363803172864 |
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| author | Patel, Daivik Patel, Shrenik |
| author_facet | Patel, Daivik Patel, Shrenik |
| contents | Large reasoning models (LRMs) achieve strong accuracy through test-time scaling, generating longer chains of thought or sampling multiple solutions, but at steep costs in tokens and latency. We argue that memory is a core ingredient for efficient reasoning: when evidence already exists, models should think less by reusing structured memory instead of recomputing derivations. We present ENGRAM-R, an inference-time memory layer that integrates typed retrieval with compact fact card representations and explicit citation control. On the LoCoMo benchmark, ENGRAM-R reduces input tokens by 85% and reasoning tokens by 75% compared to full context while maintaining high accuracy. On a multi-hop slice of the LongMemEval benchmark, it achieves similar efficiency with substantial accuracy gains. These results show that memory is not only critical for long-horizon correctness but also a practical lever for efficient reasoning under tight compute, memory, and latency budgets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12987 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reuse, Don't Recompute: Efficient Large Reasoning Model Inference via Memory Orchestration Patel, Daivik Patel, Shrenik Multiagent Systems Large reasoning models (LRMs) achieve strong accuracy through test-time scaling, generating longer chains of thought or sampling multiple solutions, but at steep costs in tokens and latency. We argue that memory is a core ingredient for efficient reasoning: when evidence already exists, models should think less by reusing structured memory instead of recomputing derivations. We present ENGRAM-R, an inference-time memory layer that integrates typed retrieval with compact fact card representations and explicit citation control. On the LoCoMo benchmark, ENGRAM-R reduces input tokens by 85% and reasoning tokens by 75% compared to full context while maintaining high accuracy. On a multi-hop slice of the LongMemEval benchmark, it achieves similar efficiency with substantial accuracy gains. These results show that memory is not only critical for long-horizon correctness but also a practical lever for efficient reasoning under tight compute, memory, and latency budgets. |
| title | Reuse, Don't Recompute: Efficient Large Reasoning Model Inference via Memory Orchestration |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2511.12987 |