activity
20242026
most citedPositional Attention for Efficient BERT-Based Named Entity Recognition

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Why On-Policy Distillation Sometimes Fails: Vanishing Learning Signals

Lei Zhao, Qichao Zhao, Bowen Zuo +1

On-policy distillation (OPD) enables effective capability transfer between language models, yet the mechanisms underlying its failures are not fully understood. Across code generat…

cs.LG2026

Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models

Yue Yu, Yue YU, Bowen Zuo +3

Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel. Reinforcement learning with…

cs.AI2026

UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement

Fang Wu, Da Xing, Yanjie Huang +16

Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified…

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.AI2025

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.CL2025★ 1 cited

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…

physics.ins-det2024

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

Zhixing Jiang, Scott Hauck, Dennis Yin +7

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine…