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: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: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: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: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...
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.
arXiv:2601. 02430v3 Announce Type: replace-cross Abstract: Web applications (web apps) have become a key arena for large language models (LLMs) to demonstrate their code generation capabilities and commercial potential.
arXiv:2606. 09426v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools.
arXiv:2603. 26648v3 Announce Type: replace-cross Abstract: Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited.
WatchPoint is a simulated‑user system that generates and runs diagnostic scripts against a live web application, producing structured observations to guide coding model retries. Unlike prior methods that rely on screenshots or non‑executable metrics, WatchPoint operates on Web‑Bench—a benchmark of 50 multi‑file web projects with 1,000 sequential tasks verified by deterministic end‑to‑end tests. It recovers 57.6% of diagnosed tasks, matching a human tester’s 54.5% recovery rate, and identifies when such simulated feedback is beneficial or should be withheld.
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
arXiv:2606. 07682v1 Announce Type: cross Abstract: AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments.