Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents
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Computer Science > Computation and Language
Title:Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents
Abstract:Online web agents often augment a base actor with memory, workflow, or skill modules. These modules can improve performance, but they also consume test-time tokens, a cost rarely reported alongside the actor's inference cost. We study online augmentation, where this overhead is paid on every task, and re-evaluate its benefits under a fixed total inference budget. We compare AWM, ASI, and ReasoningBank with a token-matched vanilla baseline that uses the same budget for additional actor steps. Across three WebArena domains and three models, Gemini 3 Flash, GPT-5.4-mini, and Qwen 3.6-27B, the vanilla baseline matches or surpasses all three augmentation methods in aggregate success rate while often using fewer total tokens. We observe a similar trend on WorkArena-L1 with Qwen 3.6-27B, indicating that the effect extends to enterprise knowledge-work tasks. Our results suggest that skills and workflow memory can be useful in specific domains, but their apparent gains often vanish against a budget-matched actor. We further show that run-to-run variance materially affects outcomes and should be reported as a core evaluation criterion for online web agents.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.15017 [cs.CL] |
| (or arXiv:2606.15017v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.15017
arXiv-issued DOI via DataCite (pending registration)
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