3 papers
cs.AI2026
Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
Bowen Zuo, Yinglun Zhu
Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typically allocate compute uniforml…
cs.AI2026
Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations
Bowen Zuo, Dongruo Zhou, Yinglun Zhu
While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute allocation or sample from fixed generation distributi…
cs.CL2025
Positional Attention for Efficient BERT-Based Named Entity Recognition
Mo Sun, Siheng Xiong, Yuankai Cai +1
This paper presents a framework for Named Entity Recognition (NER) leveraging the Bidirectional Encoder Representations from Transformers (BERT) model in natural language processin…