23 citations · 59 across the 20 of their papers we have counts for
6 papers · 1 filter
Test-time Scaling of LLMs: A Survey from A Subproblem Structure Perspective
Zhuoyi Yang, Xu Guo, Tong Zhang +2
With this paper, we survey techniques for improving the predictive accuracy of pretrained large language models by allocating additional compute at inference time. In categorizing…
Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency
Colin Hong, Xu Guo, Anand Chaanan Singh +2
Recently, Test-Time Scaling (TTS) has gained increasing attention for improving LLM reasoning performance at test time without retraining the model. A notable TTS technique is Self…
Measuring Reasoning Utility in LLMs via Conditional Entropy Reduction
Xu Guo
Recent advancements in large language models (LLMs) often rely on generating intermediate reasoning steps to enhance accuracy. However, little work has examined how reasoning utili…
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
Junru Zhang, Lang Feng, Xu Guo +3
Time-series reasoning remains a significant challenge in multimodal large language models (MLLMs) due to the dynamic temporal patterns, ambiguous semantics, and lack of temporal pr…
SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
Yige Xu, Xu Guo, Zhiwei Zeng +1
Test-Time Scaling (TTS) refers to approaches that improve reasoning performance by allocating extra computation during inference, without altering the model's parameters. While exi…
SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
Yige Xu, Xu Guo, Zhiwei Zeng +1
Chain-of-Thought (CoT) reasoning enables Large Language Models (LLMs) to solve complex reasoning tasks by generating intermediate reasoning steps. However, most existing approaches…