Fan 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.

The paper introduces FedRAG, a novel federated RAG framework that enables privacy-preserving cross-institutional knowledge collaboration by decoupling the self-attention mechanism from data localization using a specialized scrambling protocol.

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 introduces Cookie-Bench, a novel, autonomous, and reference-free evaluation framework that significantly improves the assessment of interactive web generation capabilities for frontier LLMs.

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.

The paper introduces WebIGBench, a novel benchmark designed to rigorously evaluate multimodal LLMs' ability to generate code for complex, interactive webpages, addressing the limitations of existing static evaluation methods.

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.