Yue Wu

Today's best AI agents can only solve 55% of real-world academic tasks that university students find challenging, revealing a significant gap between current AI capabilities and the demands of academic workflows.

LLMs can be powerfully combined with static analysis of crash artifacts to create a feedback loop that dramatically improves fuzzing performance on structured-input programs.

Forget end-to-end training: DexMulti's "retrieve-align-execute" approach lets robots master complex, multi-stage dexterous tasks from just a handful of demonstrations.

By learning visual representations from scene-level semantics down to pixel-level details, C2FMAE overcomes the limitations of both contrastive learning and masked image modeling.