5 citations · 5 across the 4 of their papers we have counts for
4 papers
SWE-Together: Evaluating Coding Agents in Interactive User Sessions
Yifan Wu, Zhuokai Zhao, Songlin Li +8
Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code. Real coding assistance is interactive, with us…
PushupBench: Your VLM is not good at counting pushups
Shengzhi Li, Jiarun Chen, Karun Sharma +2
Large vision-language models (VLMs) can recognize \textit{what} happens in video but fail to count \textit{how many} times. We introduce \textbf{PushupBench}, 446 long-form clips (…
Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities
Shengzhi Li, Kittipat Kampa, Rongyu Lin +2
Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the e…
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models
Shengzhi Li, Rongyu Lin, Shichao Pei
Multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities in production. However, the current MLLMs trained wi…