AgentOCR: Reimagining Agent History via Optical Self-Compression

• URL: http://arxiv.org/abs/2601.04786v1
• Date: Thu, 08 Jan 2026 10:10:20 GMT
• Title: AgentOCR: Reimagining Agent History via Optical Self-Compression
• Abstract summary: We introduce AgentOCR, a framework that exploits the superior information density of visual tokens.<n>By decomposing history into hashable segments and maintaining a visual cache, AgentOCR eliminates redundant re-rendering.<n>Results demonstrate that AgentOCR preserves over 95% of text-based agent performance while substantially reducing token consumption.
• Score: 25.87457777208574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) enable agentic systems trained with reinforcement learning (RL) over multi-turn interaction trajectories, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token budgets and memory usage. We introduce AgentOCR, a framework that exploits the superior information density of visual tokens by representing the accumulated observation-action history as a compact rendered image. To make multi-turn rollouts scalable, AgentOCR proposes segment optical caching. By decomposing history into hashable segments and maintaining a visual cache, this mechanism eliminates redundant re-rendering. Beyond fixed rendering, AgentOCR introduces agentic self-compression, where the agent actively emits a compression rate and is trained with compression-aware reward to adaptively balance task success and token efficiency. We conduct extensive experiments on challenging agentic benchmarks, ALFWorld and search-based QA. Remarkably, results demonstrate that AgentOCR preserves over 95\% of text-based agent performance while substantially reducing token consumption (>50\%), yielding consistent token and memory efficiency. Our further analysis validates a 20x rendering speedup from segment optical caching and the effective strategic balancing of self-compression.

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