Yu Yin

The paper proposes HTell, a fast and lightweight data-free backdoor detector that analyzes the abnormal response concentration of backdoored models on the target class using random latent probes applied directly to the prediction head.

This paper re-evaluates prompt-injection attacks in realistic RAG settings, finding that most prior attack methods fail to reach the generator, and that current attacks are easily detectable.

QUBRIC introduces a co-design framework that simultaneously optimizes queries and rubrics, overcoming the bottleneck of vague rubrics derived from open-ended questions, leading to significant gains in RL performance.

The paper introduces MedPMC, a framework that transforms permissively licensed literature into high-fidelity infrastructure for medical multimodal models, resulting in improved performance on various benchmarks.