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

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.

Artificial Intelligence (cs.AI)

arXiv:2608.22237 [cs.AI]

(or arXiv:2608.22237v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.22237 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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