文章背景与核心概要
随着AI智能体（AI Agents）开始处理日益复杂、长周期的任务，它们经常遭受“过度阅读（over-reading）”的困扰——在仅需特定、稀疏信息时却消耗整篇外部文档。这种低效性会导致Token成本膨胀、延迟增加，以及关键证据在模型上下文窗口中的稀释。
为了解决这一问题，本文推出了 SparseRead，这是一种新颖、无需训练且对模型透明的阅读层，旨在通过在内容进入模型之前对其进行准入控制来实现效率提升。通过利用具备机制感知能力的“阅读门控（Read Gate）”以及用于源锚定证据获取的状态化协议，SparseRead使智能体能够执行有针对性的检索、验证和精炼。实验结果表明，该技术取得了显著的性能提升，包括高达92.9%的Token体量缩减以及89.0%的挂钟时间（wall time）减少，同时在各种前沿模型中均保持或增强了任务准确性。
Read Less, Solve More: Token-Efficient Sparse Reading for AI Agents
arXiv: 2608.22237
Date: August 23, 2026
Subject: Artificial Intelligence (cs.AI)
arXiv: 2608.22237
Date: August 23, 2026
Subject: Artificial Intelligence (cs.AI)
Summary
As AI agents tackle increasingly complex, long-horizon tasks, they often suffer from "over-reading"—consuming entire external documents when only specific, sparse information is required. This inefficiency leads to inflated token costs, increased latency, and the dilution of critical evidence within the model's context window.
SparseRead is a novel, training-free, and model-transparent reading layer designed to solve this by controlling content admission before it reaches the model. By utilizing a regime-aware "Read Gate" and a stateful protocol for source-anchored evidence acquisition, SparseRead enables agents to perform targeted retrieval, verification, and refinement. Experimental results demonstrate significant performance gains, including up to 92.9% reduction in token volume and 89.0% reduction in wall time, all while maintaining or enhancing task accuracy across various frontier models.
Summary
As AI agents tackle increasingly complex, long-horizon tasks, they often suffer from "over-reading"—consuming entire external documents when only specific, sparse information is required. This inefficiency leads to inflated token costs, increased latency, and the dilution of critical evidence within the model's context window.
SparseRead is a novel, training-free, and model-transparent reading layer designed to solve this by controlling content admission before it reaches the model. By utilizing a regime-aware "Read Gate" and a stateful protocol for source-anchored evidence acquisition, SparseRead enables agents to perform targeted retrieval, verification, and refinement. Experimental results demonstrate significant performance gains, including up to 92.9% reduction in token volume and 89.0% reduction in wall time, all while maintaining or enhancing task accuracy across various frontier models.
Key Features
- Proactive Admission Control: Prevents unnecessary data from entering the model context, mitigating the "needle-in-a-haystack" dilution problem.
- Training-Free Architecture: A modular, model-transparent design that integrates seamlessly with existing agent frameworks.
- Stateful Protocol: Incorporates explicit refinement, verification, and fallback mechanisms to ensure high-quality evidence extraction.
- Broad Compatibility: Proven effective across six frontier models (including Claude Opus 5) and five distinct workload scenarios.
Key Features
- Proactive Admission Control: Prevents unnecessary data from entering the model context, mitigating the "needle-in-a-haystack" dilution problem.
- Training-Free Architecture: A modular, model-transparent design that integrates seamlessly with existing agent frameworks.
- Stateful Protocol: Incorporates explicit refinement, verification, and fallback mechanisms to ensure high-quality evidence extraction.
- Broad Compatibility: Proven effective across six frontier models (including Claude Opus 5) and five distinct workload scenarios.
Authors
- Zedong Liu, Jiaan Wu, Xinyang Ma, Le Xu, Kai Wang, Yuanchao Hu, Dingwen Tao, Guangming Tan
Authors
- Zedong Liu, Jiaan Wu, Xinyang Ma, Le Xu, Kai Wang, Yuanchao Hu, Dingwen Tao, Guangming Tan
Access & Resources
Access & Resources
Citation
If you find this research useful, please refer to the paper via its arXiv identifier: arXiv:2608.22237 [cs.AI].
Citation
If you find this research useful, please refer to the paper via its arXiv identifier: arXiv:2608.22237 [cs.AI].