4 papers
D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
Bowen Xu, Shaoyu Wu, Hao Jiang +4
Effective tool use and reasoning are essential capabilities for large reasoning models~(LRMs) to address complex real-world problems. Through empirical analysis, we identify that c…
La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation
Kai Liu, Bowen Xu, Shaoyu Wu +4
Activation sparsity can reduce the computational overhead and memory transfers during the forward pass of Large Language Model (LLM) inference. Existing methods face limitations, e…
MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems
Kai Chen, Taihang Zhen, Hewei Wang +7
As large language models (LLMs) are increasingly deployed in healthcare, ensuring their safety, particularly within collaborative multi-agent configurations, is paramount. In this…
Mixture-of-Instructions: Aligning Large Language Models via Mixture Prompting
Bowen Xu, Shaoyu Wu, Kai Liu +1
With the proliferation of large language models (LLMs), the comprehensive alignment of such models across multiple tasks has emerged as a critical area of research. Existing alignm…