arXiv AI
EvoWeb: A Benchmark for Coding Agents on Evolving Web Applications
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arXiv AI
Jun 8
SW-$A^2$-Bench: Benchmarking Autonomous Software Agent Generation for Agentic Web
arXiv:2604. 04226v2 Announce Type: replace-cross Abstract: The Agentic Web is emerging as a paradigm in which autonomous software agents interact with online resources and with each other to accomplish user goals.
arXiv AI
Sep 21
Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
arXiv:2605.06445v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, productio...
arXiv AI
Aug 5
LiveEvalBench: Toward Open-World Evaluation for Web Generation
arXiv:2608. 03689v1 Announce Type: new Abstract: Large language models are increasingly capable of synthesizing executable frontend projects, yet existing benchmarks still treat web generation as a static evaluation problem.
arXiv AI
Sep 23
WebCraftBench: Evaluating Web Application Generation from a Software Testing Perspective
arXiv:2609.15387v3 Announce Type: replace-cross Abstract: Human evaluation provides a direct measure of the quality of LLM-generated web applications. However, fitting human judgments through automat...
arXiv AI
Sep 10
DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents
arXiv:2609.06059v1 Announce Type: new Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to a...
arXiv AI
5d ago
DynaRev: Benchmarking Web Agents under Dynamic Goal Revision
DynaRev is a new benchmark comprising 181 tasks designed to evaluate web agents when users revise their goals during interaction. It introduces semantic milestones to trigger additive, unconditional, and runtime-conditioned revisions, and assesses agents on correctness, responsiveness, and efficiency via trajectory‑grounded evaluation. Experiments with five models show that agents often only partially satisfy revised tasks, with performance varying by task source, revision type, and model, and highlight the benefits of instruction rewriting versus browser resets.