1 citations · 2 across the 16 of their papers we have counts for
9 papers · 1 filter
Adaptive Latent Agentic Reasoning
Dongwon Jung, Peng Shi, Yi Zhang +2
Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM ag…
Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting
Cheng Wang, Qin Liu, Wenxuan Zhou +1
Group Relative Policy Optimization (GRPO) has emerged as a promising approach for improving the reasoning capabilities of large language models. However, it struggles to effectivel…
DebugLM: Learning Traceable Training Data Provenance for LLMs
Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +3
Large language models (LLMs) are trained through multi-stage pipelines over heterogeneous data sources, yet developers lack a principled way to pinpoint the specific data responsib…
Be My Eyes: Extending Large Language Models to New Modalities Through Multi-Agent Collaboration
James Y. Huang, Sheng Zhang, Qianchu Liu +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in challenging, knowledge-intensive reasoning tasks. However, extending LLMs to perceive and reason over a ne…
OmniStruct: Universal Text-to-Structure Generation across Diverse Schemas
James Y. Huang, Wenxuan Zhou, Nan Xu +5
The ability of Large Language Models (LLMs) to generate structured outputs that follow arbitrary schemas is crucial to a wide range of downstream tasks that require diverse structu…
Optimizing Diversity and Quality through Base-Aligned Model Collaboration
Yichen Wang, Chenghao Yang, Tenghao Huang +3
Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended g…
ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation
Qin Liu, Jacob Dineen, Yuxi Huang +4
Benchmarks are central to measuring the capabilities of large language models and guiding model development, yet widespread data leakage from pretraining corpora undermines their v…
Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models
Shahriar Kabir Nahin, Hadi Askari, Muhao Chen +1
Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best output. A tacit premise behind TTS is tha…
False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize
Cheng Wang, Zeming Wei, Qin Liu +1
Large Language Models (LLMs) can comply with harmful instructions, raising serious safety concerns despite their impressive capabilities. Recent work has leveraged probing-based ap…