5 papers
PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding
Shengyin Sun, Yiming Li, Renxi Liu +7
Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions in parallel within each step,…
AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents
Wenbo Gao, Renxi Liu, Xian Wang +8
Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between ex…
DLLM Agent: See Farther, Run Faster
Huiling Zhen, Weizhe Lin, Renxi Liu +15
Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties, yet their implications for…
Towards Efficient Agents: A Co-Design of Inference Architecture and System
Weizhe Lin, Hui-Ling Zhen, Shuai Yang +14
The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making. However, the…
Revisiting Judge Decoding from First Principles via Training-Free Distributional Divergence
Shengyin Sun, Yiming Li, Renxi Liu +5
Judge Decoding accelerates LLM inference by relaxing the strict verification of Speculative Decoding, yet it typically relies on expensive and noisy supervision. In this work, we r…