This paper presents an alternative approach to GUI agents by reformulating world modeling to predict action-induced future information in latent space, improving success rates and reducing inference latency.

A new benchmark measures the readiness of merchants to engage with agentic commerce, evaluating how well their websites allow agents to browse, decide, and prove purchases. The benchmark assesses the determinacy of terms like total cost, cancellation terms, and service dates, and reports on access for different types of browsers.

This paper proposes using Evidence-Centered Design (ECD) as a framework for principled prompt engineering in educational applications of large language models (LLMs). It maps ECD models to prompt design components and presents a workflow for ECD-based prompt engineering, demonstrating its effectiveness in automated scoring tasks.

Researchers audited the assumptions of large language model (LLM) benchmark evaluations, comparing two modalities (chat UI and API) with and without web search enabled. They found that chat UI responses were less accurate than API responses and that enabling web search reduced accuracy by up to 8 percentage points. The study highlights the need for AI safety evaluations to account for modality, multi-run consistency, search conditions, and response-level behaviors to better reflect how deployed AI systems behave in practice.