# Paper: Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents

---
type: paper
title: Are Online Skill and Memory Modules Always Worth Their Tokens? A Budget-Constrained Study of Web Agents
authors: Sina Hajimiri, Masih Aminbeidokhti, Jose Dolz, Ismail Ben Ayed, Issam H. Laradji, Spandana Gella, Nicolas Gontier
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.15017
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-12
updated_at: 2026-06-12
status: skimmed
relevance: high
topics:
  - agent-evaluation
  - computer-use
  - memory
  - reasoning
  - workflow-agent
methods:
  - budget-matched-baseline
  - online-augmentation-audit
benchmarks:
  - WebArena
  - WorkArena-L1
models:
  -
datasets:
  - cs.CL
related_concepts:
  - memory-cost-accounting
  - evaluation-hygiene
related_jobs:
  -
related_experiments:
  - KC-001-agent-memory-pilot
related_projects:
  - learning/agent-memory
collection_score: 14
collection_queries: web-gui-agent
---

## One-line Takeaway

在线 memory 或 skill 的收益必须与“把相同推理预算直接交给基础 Agent”比较，不能只对比相同步数。

## Pilot Skim

- problem: augmentation 论文常忽略 induction、retrieval、verification 和 prompt injection 的额外 token。
- method: 用 Vanilla-IB 把近似相同预算用于更多 observe-act steps，并报告多 run 方差。
- evidence: WebArena 三域三模型中，Vanilla-IB 平均 SR 均最高且通常 token 更少；WorkArena-L1 上仍处于 success-cost frontier。
- boundary: 研究对象是 online augmentation；可离线摊销的 skill/memory 需要另一种成本模型。

## Used In

- [Agent Memory evidence matrix](../../learning/agent-memory/evidence-matrix.md)

## Links

- arXiv: https://arxiv.org/abs/2606.15017