Yifan Wu

DeepGuard introduces a novel multi-layer semantic aggregation framework to enhance secure code generation by collecting vulnerability cues from multiple upper layers of LLMs, significantly improving security while maintaining functional correctness.

This paper introduces a latent attack framework demonstrating that attacks can be embedded into the hidden representations of multi-agent systems, causing performance degradation even during clean, non-adversarial executions.

The paper proposes a novel zeroth-order optimization framework to enhance the robustness of LLM safety alignment, showing that few refinement steps can significantly improve safety while maintaining utility.

OmniOPD introduces a logit-free, chunk-level distillation framework that improves on standard On-Policy Distillation by using semantic similarity and peak-entropy scheduling, achieving state-of-the-art performance even with black-box teachers.

This paper proposes a method called selective prediction to enhance the reliability of large language models by allowing them to only predict for inputs where they are likely to be correct, reducing errors and enabling human-AI collaboration.

The paper introduces a memory agent to improve decision-making in long-horizon tasks by actively updating and intervening with reminders.