Xinyu Li

This survey analyzes privacy-enhancing encryption technologies (ABE, PRE, SE) for data sharing, proposing a comprehensive framework, identifying potential attacks, and evaluating their multi-dimensional impact on security, performance, and functionality.

The paper introduces Semantic Compliance Hijacking (SCH), a novel payload-less attack that exploits LLM agent supply chains by manipulating compliance rules to force unauthorized code generation, achieving high success rates against current security tools.

The OISD framework improves language model reasoning by distilling on-policy predictive signals from the final output layer to intermediate representations, leading to substantial improvements on mathematical reasoning tasks.

Lumos-Nexus is a training-efficient framework that enhances video generation quality by progressively bridging generation from a lightweight model to a high-fidelity generator in a shared latent space, without sacrificing reasoning capabilities.

MOSAIC introduces a structured agentic framework that treats automated data science as a staged, context-grounded model selection problem, improving performance and traceability over traditional AutoML and unconstrained LLM agents.

This paper introduces a training recipe for sentence-level and long-form streaming speech-to-speech translation using only 2k hours of paired cross-lingual data and auxiliary supervision.