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SparseRead Reduces Token Usage by Up to 92.9% for Web-Facing AI Agents

·2026.11.14 14:41

Key point

The reusable reading control layer preserves task quality in 97% of settings across six models, including Claude Opus 5.

Details

  • SparseRead is a reusable reading control layer that governs content admission for web-facing AI agents without modifying the underlying agent model.
  • Across six models and five scenarios, the system reduces total tokens by up to 92.9% and wall time by up to 89.0%.
  • On Web deep-search tasks specifically, token usage drops by 31.0–43.0% and task completion time decreases by 23.0–37.0%.
  • The architecture comprises a Read Intent Protocol for tracking needs, Adaptive Reader Backends for evidence location, and a Budgeted Read Gate to bound raw exposure.
  • Integration with three agent frameworks demonstrates portability, while ablations validate the three core design components.
  • The paper is available on arXiv (2608.22237) with code hosted on GitHub under the SparseReading repository.
token-efficiencyai-agentsarxivllm-optimizationweb-searchsparse-reading
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