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

Zedong Liu, Jiaan Wu, Xinyang Ma, Le Xu, Kai Wang, Yuanchao Hu, Dingwen Tao, Guangming Tan

Read Less, Solve More: Token-Efficient Sparse Reading of External Content for AI Agents: 18 upvotes on Hugging Face Daily Papers, #4 of 21 papers on 2026-11-12. Day-by-day upvote history.

Upvotes over time

It had 18 upvotes by its first day, 18 upvotes by the next day, 18 upvotes by day 3, 18 upvotes by day 7, and has 18 now. It ranked #40 of 133 papers in its week and #116 of 340 in its month.

Web-facing AI agents repeatedly access webpages, documents, repositories, and tool responses, yet reading interfaces often expose entire objects when only sparse evidence is needed. This over-reading increases token and latency costs and can dilute relevant evidence. We present SparseRead, a reusable reading control layer that governs content admission without modifying the agent model. Its Read Intent Protocol tracks evolving needs and source coverage; Adaptive Reader Backends select how evidence is located and exposed; and the Budgeted Read Gate bounds cumulative raw exposure per episode with faithful full-view fallback. A unified Reader interface supports interchangeable evidence acquisition across heterogeneous resources. Across six models, including Claude Opus 5, and five scenarios, SparseRead reduces total tokens by up to 92.9% and wall time by up to 89.0%, preserving or improving task quality in 97% settings. On Web deep-search tasks, it reduces tokens by 31.0--43.0% and task completion time by 23.0--37.0%. Ablations validate the three designs, while integration with three agent frameworks demonstrates portability.

Paper page on Hugging Face · arXiv

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