13 citations · 19 across the 17 of their papers we have counts for
9 papers · 1 filter
Reward and Guidance through Rubrics: Promoting Exploration to Improve Multi-Domain Reasoning
Baolong Bi, Shenghua Liu, Yiwei Wang +6
Recent advances in reinforcement learning (RL) have significantly improved the complex reasoning capabilities of large language models (LLMs). Despite these successes, existing met…
A Survey of Vibe Coding with Large Language Models
Yuyao Ge, Lingrui Mei, Zenghao Duan +12
The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology…
Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning
Yuyao Ge, Shenghua Liu, Yiwei Wang +6
Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in complex visual environments. While existing enhanc…
Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in Large Language Models
Yujia Zheng, Tianhao Li, Haotian Huang +8
Prompt-based adversarial attacks have become an effective means to assess the robustness of large language models (LLMs). However, existing approaches often treat prompts as monoli…
A Survey of Context Engineering for Large Language Models
Lingrui Mei, Jiayu Yao, Yuyao Ge +12
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a f…
Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation
Jiayu Yao, Shenghua Liu, Yiwei Wang +5
Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustne…
PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression
Lizhe Chen, Binjia Zhou, Yuyao Ge +2
Large language models (LLMs) have achieved remarkable progress, demonstrating unprecedented capabilities across various natural language processing tasks. However, the high costs a…
a1: Steep Test-time Scaling Law via Environment Augmented Generation
Lingrui Mei, Shenghua Liu, Yiwei Wang +5
Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during com…
Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking
Yuyao Ge, Shenghua Liu, Yiwei Wang +4
Recent advances in Large Language Models (LLMs) have introduced Reasoning Large Language Models (RLLMs), which employ extended thinking processes with reflection and self-correctio…