OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913072882384896 |
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| author | Li, Jinze Zhang, Yang Yang, Xin Qu, Jiayi Xu, Jinfeng Yang, Shuo Ding, Junhua Ngai, Edith Cheuk-Han |
| author_facet | Li, Jinze Zhang, Yang Yang, Xin Qu, Jiayi Xu, Jinfeng Yang, Shuo Ding, Junhua Ngai, Edith Cheuk-Han |
| contents | Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended histories. However, existing agent memory systems are fundamentally constrained by text-context budgets: storing or revisiting raw trajectories is prohibitively token-expensive, while summarization and text-only retrieval trade token savings for information loss and fragmented evidence. To address this limitation, we propose Optical Context Retrieval Memory (OCR-Memory), a memory framework that leverages the visual modality as a high-density representation of agent experience, enabling retention of arbitrarily long histories with minimal prompt overhead at retrieval time. Specifically, OCR-Memory renders historical trajectories into images annotated with unique visual identifiers. OCR-Memory retrieves stored experience via a \emph{locate-and-transcribe} paradigm that selects relevant regions through visual anchors and retrieves the corresponding verbatim text, avoiding free-form generation and reducing hallucination. Experiments on long-horizon agent benchmarks show consistent gains under strict context limits, demonstrating that optical encoding increases effective memory capacity while preserving faithful evidence recovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26622 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory Li, Jinze Zhang, Yang Yang, Xin Qu, Jiayi Xu, Jinfeng Yang, Shuo Ding, Junhua Ngai, Edith Cheuk-Han Computation and Language Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended histories. However, existing agent memory systems are fundamentally constrained by text-context budgets: storing or revisiting raw trajectories is prohibitively token-expensive, while summarization and text-only retrieval trade token savings for information loss and fragmented evidence. To address this limitation, we propose Optical Context Retrieval Memory (OCR-Memory), a memory framework that leverages the visual modality as a high-density representation of agent experience, enabling retention of arbitrarily long histories with minimal prompt overhead at retrieval time. Specifically, OCR-Memory renders historical trajectories into images annotated with unique visual identifiers. OCR-Memory retrieves stored experience via a \emph{locate-and-transcribe} paradigm that selects relevant regions through visual anchors and retrieves the corresponding verbatim text, avoiding free-form generation and reducing hallucination. Experiments on long-horizon agent benchmarks show consistent gains under strict context limits, demonstrating that optical encoding increases effective memory capacity while preserving faithful evidence recovery. |
| title | OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.26622 |