EvoWeb：面向持续演化 Web 应用的编程 Agent 基准

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Abstract:Web application development often proceeds through successive requests that extend an existing implementation. Yet web-generation benchmarks primarily evaluate development toward fixed specifications. We introduce EvoWeb, a novel benchmark comprising 320 cumulative requests over 32 web applications, grounded in real-world product features and structured by functional dependency graphs. Agents iteratively modify a persistent codebase, while a multimodal web agent tests each intermediate application in a live browser, assessing newly requested functionality, cross-request integration, and previously requested functionality. To characterize reliability throughout development, we integrate trajectory performance across user-tolerance thresholds rather than impose a fixed stopping criterion. Experiments across open- and closed-source frontier models show that EvoWeb reveals clear performance differences between models, with cross-request integration emerging as a key challenge. Further analysis shows that later requests with deeper or more numerous dependencies are solved less accurately, while runtime errors become more frequent over successive requests. EvoWeb provides a testbed for evaluating and improving coding agents in long-horizon, multi-request development.

Comments:	24 pages, 6 figures. Code: this https URL ; Dataset: this https URL
Subjects:	Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as:	arXiv:2610.16402 [cs.AI]
	(or arXiv:2610.16402v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.16402 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bar Alon [view email]
[v1] Mon, 12 Oct 2026 08:42:42 UTC (679 KB)