activity
20242026
collaborators

7 papers

cs.LG2026

Self-evolving LLM agents with in-distribution Optimization

Yudi Zhang, Meng Fang, Zhenfang Chen +1

Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments, yet training them to perform reliable long-horizon decisi…

cs.LG2026

DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment

Yi Nian, Tiankai Yang, Yudi Zhang +7

Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically scor…

cs.AI2026

When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing

Yi Nian, Haosen Cao, Shenzhe Zhu +4

When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is oft…

cs.CL2025

InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation

Weilin Zhao, Zihan Zhou, Zhou Su +10

Long-sequence processing is a critical capability for modern large language models. However, the self-attention mechanism in the standard Transformer architecture faces severe comp…

cs.CL2025

Speculative Decoding Meets Quantization: Compatibility Evaluation and Hierarchical Framework Design

Yudi Zhang, Weilin Zhao, Xu Han +4

Speculative decoding and quantization effectively accelerate memory-bound inference of large language models. Speculative decoding mitigates the memory bandwidth bottleneck by veri…

cs.CL2025

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling

Weilin Zhao, Tengyu Pan, Xu Han +9

Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify…

cs.CL2024

Enabling Real-Time Conversations with Minimal Training Costs

Wang Xu, Shuo Wang, Weilin Zhao +6

Large language models (LLMs) have demonstrated the ability to improve human efficiency through conversational interactions. Conventional LLM-powered dialogue systems, operating on…