247 citations · 278 across the 15 of their papers we have counts for
6 papers · 1 filter
Position: Universal Time Series Foundation Models Rest on a Category Error
Xilin Dai, Wanxu Cai, Zhijian Xu +1
This position paper argues that the pursuit of "Universal Foundation Models for Time Series" rests on a fundamental category error, mistaking a structural Container for a semantic…
Reasoning Scaffolding: Distilling the Flow of Thought from LLMs
Xiangyu Wen, Junhua Huang, Zeju Li +6
The prevailing approach to distilling reasoning from Large Language Models (LLMs)-behavioral cloning from textual rationales-is fundamentally limited. It teaches Small Language Mod…
Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropy
Zeju Li, Jianyuan Zhong, Ziyang Zheng +5
Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to incre…
Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
Xumeng Wen, Zihan Liu, Shun Zheng +9
Recent advancements in long chain-of-thought (CoT) reasoning, particularly through the Group Relative Policy Optimization algorithm used by DeepSeek-R1, have led to significant int…
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier
Jianyuan Zhong, Zeju Li, Zhijian Xu +3
Large Language Model (LLM) reasoning for complex tasks inherently involves a trade-off between solution accuracy and computational efficiency. The subsequent step of verification,…
Dyve: Thinking Fast and Slow for Dynamic Process Verification
Jianyuan Zhong, Zeju Li, Zhijian Xu +2
We present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman's Systems T…