Top pick  ·  RLHF & Alignment

CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements

CUA-Master introduces a double-loop recursive framework that uses open-world data to continuously improve computer-use agents.

Why it matters: Builders gain a scalable way to generate diverse, controllable interaction data and improve agent performance without manual data curation.

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Trending  ·  LLMs & Language Models

Advances in Agentic Computer Graphics

The paper proposes a taxonomy and a literature analysis system for agentic computer graphics research.

Why it matters: It helps developers quickly understand and leverage agent‑driven graphics tools across many tasks.

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