7 papers
CLEANER: Self-Purified Trajectories Boost Agentic Reinforcement Learning
Tianshi Xu, Yuteng Chen, Meng Li
Agentic Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to utilize tools like Python interpreters for complex problem-solving. However, for parameter-constra…
Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
Tianshi Xu, Huifeng Wen, Meng Li
LLM agents are shaped not only by their language models, but also by the runtime harness that mediates observation, tool use, action execution, feedback interpretation, and traject…
EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval
Zebin Yang, Sunjian Zheng, Tong Xie +6
Object-goal navigation (ObjNav) tasks an agent with navigating to the location of a specific object in an unseen environment. Embodied agents equipped with large language models (L…
UFO: Unlocking Ultra-Efficient Quantized Private Inference with Protocol and Algorithm Co-Optimization
Wenxuan Zeng, Chao Yang, Tianshi Xu +4
Private convolutional neural network (CNN) inference based on secure two-party computation (2PC) suffers from high communication and latency overhead, especially from convolution l…
CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
Yifan Zhou, Tianshi Xu, Jue Hong +2
Private large language model (LLM) inference based on cryptographic primitives offers a promising path towards privacy-preserving deep learning. However, existing frameworks only s…
Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory Processing
Chenqi Lin, Kang Yang, Tianshi Xu +6
With the wide application of machine learning (ML), privacy concerns arise with user data as they may contain sensitive information. Privacy-preserving ML (PPML) based on cryptogra…