3 papers
cs.CV2026
Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background
Tianyu Li, Zongqian Wu, Songyue Cai +2
CLIP-based foreground-background (FG-BG) decomposition methods have demonstrated remarkable effectiveness in improving few-shot out-of-distribution (OOD) detection performance. How…
cs.AI2025
Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Zongqian Wu, Baoduo Xu, Tianyu Li +3
Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research ha…
cs.AI2025
Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
Zongqian Wu, Tianyu Li, Baoduo Xu +4
Deep iterative chain-of-thought (CoT) reasoning enables LLMs to tackle complex tasks by progressively activating relevant pre-trained knowledge. However, it faces challenges in ens…