1 citations · 1 across the 12 of their papers we have counts for
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DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
Xin Cheng, Xingkai Yu, Chenze Shao +30
Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose lo…
Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
Peipei Xu, SiYuan Ma, Yaohua Liu +4
Generating feasible Pareto fronts for constrained bi-objective continuous optimization is central to multi-criteria decision-making. Existing methods usually rely on iterative scal…
From Deferral to Learning: Online In-Context Knowledge Distillation for LLM Cascades
Yu Wu, Shuo Wu, Ye Tao +2
Standard LLM cascades improve efficiency by deferring difficult queries from weak to strong models. However, these systems are typically static: when faced with repeated or semanti…
GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning
Yao Zhang, Yu Wu, Haowei Zhang +6
Process Reward Models (PRMs) aim to improve multi-step reasoning in Large Language Models (LLMs) by supervising intermediate steps and identifying errors. However, building effecti…
Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and Planning
Sai Wang, Yu Wu, Zhongwen Xu
The pursuit of artificial agents that can learn to master complex environments has led to remarkable successes, yet prevailing deep reinforcement learning methods often rely on imm…
WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration
Yao Zhang, Zijian Ma, Yunpu Ma +3
LLM-based autonomous agents often fail to execute complex web tasks that require dynamic interaction due to the inherent uncertainty and complexity of these environments. Existing…