Large Language Models (LLMs) increasingly generate executable visual artifacts, yet established code benchmarks largely overlook the visual fidelity and interaction behavior that shape user experience. We introduce ArtifactsBench, a benchmark and automated multimodal evaluation framework for visual code generation. The framework executes each artifact and records three staged screenshots—before, during, and after a scripted interaction—then scores the evidence and source code with a checklist-guided Multimodal LLM (MLLM) referee. ArtifactsBench contains 1,825 tasks across nine domains and evaluates more than 30 LLMs. On a 280-query, six-model expert study, three staged screenshots yield 90.95% referee–human Pair ACC. Across the models shared with WebDev Arena, ArtifactsBench reaches 94.4% normalized Footrule consistency with that public human-preference ranking. We release the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/.

Large language models are increasingly deployed as interactive coding agents for end-to-end web development, yet existing benchmarks evaluate narrow slices of this workflow—typically text-conditioned generation with static correctness metrics—overlooking visual fidelity, interactivity, and codebase-level reasoning. We introduce WebCompass, a benchmark that covers the full web development lifecycle by spanning three input modalities (text, image, video) and three task types (generation, editing, repair). To match the diverse nature of these tasks, we pair checklist-guided LLM-as-a-Judge for patch-based evaluation with a novel Agent-as-a-Judge protocol that executes generated websites in a real browser and synthesizes targeted test cases. Experiments on ten models reveal a 25-point gap between the best closed- and open-source models, persistent weaknesses in visual aesthetics even among frontier systems, and divergent performance profiles across task types that challenge the notion of a single "best" web coding agent.

Large language model (LLM)-based agents are increasingly deployed in e-commerce shopping. To perform thorough, user-tailored product searches, agents should interpret personal preferences, engage in multi-turn dialogues, and ultimately retrieve and discriminate among highly similar products. However, existing research has yet to provide a unified simulation environment that consistently captures all of these aspects, and always focuses solely on evaluation benchmarks without training support. In this paper, we introduce ShopSimulator, a large-scale and challenging Chinese shopping environment. Leveraging ShopSimulator, we evaluate LLMs across diverse scenarios, finding that even the best-performing models achieve less than 40% full-success rate. Error analysis reveals that agents struggle with deep search and product selection in long trajectories, fail to balance the use of personalization cues, and to effectively engage with users. Further training exploration provides practical guidance for overcoming these weaknesses, with the combination of supervised fine-tuning (SFT) and reinforcement learning (RL) yielding significant performance improvements.

While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate–diagnose–refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent employs an MLLM-in-the-loop to serve as a visual critic, evaluating code via screenshots and providing actionable feedback. Crucially, we enforce a strict zero-reward policy for invalid renders to guarantee renderability and mitigate reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training–inference decoupling.

Repository-level code completion has drawn great attention in software engineering, and several benchmarks have been introduced. However, existing repository-level code completion benchmarks usually focus on a limited number of languages (<5), which cannot evaluate the general code intelligence abilities across different languages for existing code Large Language Models (LLMs). Besides, the existing benchmarks usually report overall average scores of different languages, where the fine-grained abilities in different completion scenarios are ignored. Therefore, to facilitate the research of code LLMs in multilingual scenarios, we propose a massively multilingual repository-level code completion benchmark covering 18 programming languages (called M2RC-EVAL), and two types of fine-grained annotations (i.e., bucket-level and semantic-level) on different completion scenarios are provided, where we obtain these annotations based on the parsed abstract syntax tree. Moreover, we also curate a massively multilingual instruction corpora M2RC-INSTRUCT dataset to improve the repository-level code completion abilities of existing code LLMs. Comprehensive experimental results demonstrate the effectiveness of our M2RC-EVAL and M2RC-INSTRUCT.