Read Less, Solve More: Token-Efficient Sparse Reading of External Content for AI Agents
SparseRead cut total token use by up to 92.9%, preserving or improving task quality in 97% of tested settings.
The reading control layer limits which external content reaches an AI agent without changing its underlying model. It tracks information needs and source coverage, selects how to retrieve evidence, and caps cumulative raw content exposure while retaining a full-view fallback. Across six models and five scenarios, the authors report wall-time reductions of up to 89.0%. On web deep-search tasks specifically, token use fell 31.0–43.0% and completion time fell 23.0–37.0%.
HF Daily Papers' note
The reading control layer limits which external content reaches an AI agent without changing its underlying model. It tracks information needs and source coverage, selects how to retrieve evidence, and caps cumulative raw content exposure while retaining a full-view fallback. Across six models and five scenarios, the authors report wall-time reductions of up to 89.0%. On web deep-search tasks specifically, token use fell 31.0–43.0% and completion time fell 23.0–37.0%.
HF Daily Papers' note
score 5