MulFCoder is a framework-conditioned multi-agent system designed to generate runnable front‑end code from UI screenshots across multiple frameworks such as React, Vue, and Angular. It orchestrates four specialized agents—Grounder, Planner, Writer, and Judger—to construct element tables, build a DOM‑like layout tree, produce framework‑specific file contracts, and enforce constraints that prevent drift and deadlocks. Experiments show that MulFCoder significantly improves compilation success rates and reduces framework‑specific errors, especially for Angular.
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: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.
ChatDev 2.0, also called DevAll, is a no-code platform that lets users build, run, and inspect heterogeneous multi‑agent systems (MAS) powered by large language models. It combines a declarative executable graph abstraction with a cycle‑aware execution engine, enabling representation and execution of dynamic, cyclic interactions among diverse agents. The integrated visual interface allows users to author, monitor, and inspect MAS—including human‑in‑the‑loop steps—without writing code, and experiments show it matches state‑of‑the‑art MAS performance across three tasks.
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:2605. 03353v3 Announce Type: replace-cross Abstract: LLM agents increasingly rely on reusable skills (e.
arXiv:2610.15765v1 Announce Type: cross Abstract: Large language model agents are emerging as an important new class of users of desktop applications. However, existing desktop interfaces were design...
The paper introduces Multi-step SWE, a benchmark consisting of 69 software engineering tasks that require a coding agent to perform 5–15 sequential, multi-hour engineering requests on a single codebase. Each step builds on the code produced in the previous step, making early design decisions and errors propagate throughout the chain. The benchmark covers four engineering work families, nine programming languages, and includes both from-scratch builds and existing open-source projects, with each step verified against a source of truth. Across nine state‑of‑the‑art models, the best achieves only an 18.5% solve rate, and 38 tasks remain unsolved by any model.
arXiv:2609.14992v1 Announce Type: new Abstract: Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute...
WebUIProof is a new execution‑oriented benchmark for WebUI code generation that supplies structured specifications and dense, executable interaction tests across general WebUIs and 3D interactive simulations. It employs a UI‑agent harness that runs tests in a headless browser using a plan–act–observe loop to locate DOM elements, perform actions, observe changes, and verify assertions. Evaluations on eight commercial LLMs reveal frequent interaction‑based failures, especially on 3D interfaces, and demonstrate that training compact models with RL rewards from these tests improves functional completion and reduces build failures.
MACGen is a multi‑agent framework designed to produce code that is both functionally correct and secure. It orchestrates four specialized agents—planner, security advisor, coder, and reviewer—each receiving only structured artifacts from the previous stage, thereby enforcing role specialization and limiting context bloat. The approach yields significant improvements on benchmark datasets, outperforming direct prompting by 19.61 and 10.57 percentage points on average.
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
The paper introduces LibraryDesignBench, a benchmark that tests how well agents can design reusable libraries from specifications without prescribed designs. It evaluates libraries by measuring the correctness and simplicity of programs written by three different user agents across 242 programming problems in four languages. Findings show that while agents often replicate human-designed abstractions, downstream agents still tend to reimplement library features due to rigidity or usability issues, and that providing more prescriptive guidance improves reuse and program simplicity.