Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimodal, heterogeneous, noisy, and implicitly assumes human executors, making it difficult to use directly as the skills required by agents. To bridge the gap between human-oriented guides and agent-executable skills, we formalize this problem as guide-to-skill learning: converting in-the-wild guides into executable skills and continuously improving them from trajectories observable to the agent. To evaluate the capability of existing agents on this task, we introduce MMG2Skill-Bench, the first benchmark designed for this problem. We further propose MMG2Skill, a closed-loop framework that compiles guides into editable skills, conditions a fixed vision-language model (VLM) agent on these skills during execution, and revises the skills from trajectory-level root-cause feedback without using benchmark scores. Across GUI control, open-ended gameplay, and strategic card play with six VLM backbones, MMG2Skill consistently outperforms vanilla baseline agents in every model–domain setting, achieving macro-average gains of +12.8 to +25.3 percentage points across backbones. Ablation studies show that directly prompting agents with raw guides can degrade performance, while both structured skill construction and trajectory-driven revision are necessary for the observed improvements. On success-inferable tasks, analyzer-based early stopping further prevents late-stage performance regressions and saves 25%–53% of attempts when the success signal is properly calibrated.

Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time Improvement (TTI). However, the mechanisms under how and why TTI succeed or fail remain poorly understood, and existing evaluation metrics fail to capture their task optimization efficiency, behavior adaptation after erroneous actions, and the specific utility of working memory. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), a lightweight, agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions. The framework measures (1) the overall temporal dynamics of task completion and (2) identifies whether performance is constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments, TIDE highlights that improving agent performance calls for explicitly optimizing the interaction dynamics between the agent and the environment.

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.