Tao Chen

PragLocker is a novel prompt protection scheme that secures valuable LLM agent prompts against theft and reuse by other proprietary models by making them non-portable.

VITAL introduces a novel latent-space reasoning framework for medical MLLMs, utilizing visual-semantic dual supervision to enhance reasoning capabilities and provide crucial interpretability without sacrificing efficiency.

The paper proposes Skill-RM, a unified framework that treats reward modeling as an agentic task to consistently integrate diverse evaluation criteria, achieving superior performance over traditional methods.

This paper proposes a post-training framework called Retrieval-Augmented Reinforcement Fine-Tuning (RA-RFT) to teach language models to reason by analogy.

This paper proposes a four-layer technical architecture for large model inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion.

This paper proposes EdgeFaaS, a function-based edge computing framework that abstracts distributed and heterogeneous physical resources and provides consistent virtual interfaces for deploying and executing functions and storing and accessing data.

This paper introduces Skill Self-Play (Skill-SP), a co-evolutionary framework for LLM training that bridges the gap between structured verification and open-ended exploration.