Read Less, Solve More: Token-Efficient Sparse Reading for AI Agents

2026-08-23

AI Summary

为解决AI代理过度读取外部信息的问题，提出SparseRead方法，在信息进入模型前控制内容准入，减少令牌和延迟成本，同时保持或提高任务质量。

Abstract

Long-horizon agents increasingly rely on repeated access to external artifacts, yet current reading interfaces often expose entire objects even when only sparse evidence is needed. This over-reading increases token and latency costs and can dilute task-relevant evidence, while existing context-reduction methods mainly intervene after broad content has already entered the trajectory. We present SparseRead, a training-free, model-transparent reading layer that controls content admission before unnecessary evidence reaches the model context. SparseRead combines a regime-aware Read Gate, extensible Reader Backends, and a stateful protocol for bounded, source-anchored evidence acquisition with explicit refinement, verification, stopping, and fallback. Across six frontier models, including Claude Opus 5, and five workload scenarios, SparseRead reduces token volume by up to 92.9% and wall time by up to 89.0%, while preserving or improving task quality. Its consistent gains across three agent frameworks further demonstrate broad portability.

Problem

• long-horizon agents
• external artifacts
• over-reading
• token and latency costs
• task-relevant evidence

Innovation

• SparseRead
• token-efficient
• model-transparent
• evidence acquisition
• context admission control