[2605.16565] Skim: Speculative Execution for Fast and Efficient Web Agents
[Submitted on 15 May 2026]
Title:Skim: Speculative Execution for Fast and Efficient Web Agents
View a PDF of the paper titled Skim: Speculative Execution for Fast and Efficient Web Agents, by Mike Wong and 3 other authors
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Abstract:Skim is a speculative execution framework for web agents that exploits the predictable structure of purpose-built websites. Today's web-agent expense is not intrinsic to the tasks but a property of how agents are composed: frontier-model inference, browser rendering, and ReAct-style planning are applied to every step of every task regardless of complexity. Skim's key observation is that websites enforce stable URL patterns, answer formats, and task-to-trajectory mappings across queries of the same type, so most queries can bypass these heavyweight components entirely. An offline profiler captures these patterns once per site. At runtime, Skim matches each query to a template, synthesizes the destination URL, and extracts the answer with a small model. A lightweight verifier gates each fast-path output against the query and schema; rare misspeculations cascade to the full agent, warm-started by the fast path's final URL to preserve upstream trajectory progress. Across standard web-agent benchmarks paired with three backboneagents (WebVoyager, AgentOccam, BrowserUse), Skim reduces median per-task cost by 1.9x and latency by 33.4% with no accuracy loss.
Comments: 14 pages, 21 figures
Subjects:
Artificial Intelligence (cs.AI); Operating Systems (cs.OS)
Cite as: arXiv:2605.16565 [cs.AI]
(or arXiv:2605.16565v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.16565
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mike Wong [view email] [v1] Fri, 15 May 2026 19:12:43 UTC (4,892 KB)
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