Jiajun Wu

LLMs that can generate HTML are finally useful: HTMLCure's closed-loop repair engine turns superficially correct but broken pages into high-quality training data, rivaling the performance of much larger models.

Even the best large vision-language models struggle with multi-image reasoning, scoring only 50% on a new benchmark designed to challenge their capabilities.

Industrial code generation gets a reasoning boost: InCoder-32B-Thinking leverages error-driven feedback and a code world model to achieve top-tier performance on complex hardware-aware tasks.

LLMs can generate more accurate motion trajectories by clustering them into geometrically consistent families, even without retraining.

A new 32B code LLM trained specifically for industrial tasks crushes existing models on specialized domains like chip design and GPU kernel optimization, while remaining competitive on general coding benchmarks.

Code LLMs can achieve SOTA performance in agentic tasks by explicitly modeling the dynamic evolution of software logic across different training stages.

A 32B model trained entirely on synthetic data from InfTool outperforms models 10x larger on tool use, rivaling even Claude-Opus.