1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AI2026
Efficient Test-Time Scaling via Temporal Reasoning Aggregation
Jiakun Li, Xingwei He, Kefan Li +3
Test-time scaling improves the reasoning performance of large language models but often results in token-inefficient overthinking, where models continue reasoning beyond what is ne…
cs.CL2025
ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions
Xingwei He, Qianru Zhang, Pengfei Chen +4
Instruction-following is a critical capability of Large Language Models (LLMs). While existing works primarily focus on assessing how well LLMs adhere to user instructions, they of…
cs.CV2024★ 1 cited
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
Xingwei He, Qianru Zhang, A-Long Jin +2
Large Vision-Language Models (LVLMs) have achieved remarkable progress on visual perception and linguistic interpretation. Despite their impressive capabilities across various task…