Huan Yu

Current subject-driven text-to-image models struggle with specific subject categories and prompt scenarios, a problem exposed by a new benchmark that also offers actionable insights for improvement.

OneRanker tackles the optimization tension in generative recommendation systems by deeply integrating generation and ranking into a single architecture, achieving state-of-the-art performance in industrial advertising.

Agentic RAG gets a 7.7 point accuracy boost thanks to Search-P1's path-centric reward shaping, which extracts learning signals even from failed reasoning attempts.

Dramatically reduce hallucination in industrial RAG systems by jointly optimizing retrieval and generation with graph-aware retrieval and reinforcement learning, leading to a 92.7% reduction in URL hallucination in a real-world advertising QA system.

LLMs still struggle with real-world advertising analytics, with even Gemini-3-Pro dropping to 49.4% accuracy on the most complex tasks in the new AD-Bench benchmark.