2 citations · 5 across the 8 of their papers we have counts for
9 papers
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
Yichen Feng, Zhangchen Xu, Fengqing Jiang +5
Vision language models (VLMs) are expected to perform effective multimodal reasoning and make logically coherent decisions, which is critical to tasks such as diagram understanding…
Temporal Sampling for Forgotten Reasoning in LLMs
Yuetai Li, Zhangchen Xu, Fengqing Jiang +5
Fine-tuning large language models (LLMs) is intended to improve their reasoning capabilities, yet we uncover a counterintuitive effect: models often forget how to solve problems th…
TinyV: Reducing False Negatives in Verification Improves RL for LLM Reasoning
Zhangchen Xu, Yuetai Li, Fengqing Jiang +4
Reinforcement Learning (RL) has become a powerful tool for enhancing the reasoning abilities of large language models (LLMs) by optimizing their policies with reward signals. Yet,…
CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation
Jixuan Leng, Chengsong Huang, Langlin Huang +4
Existing reasoning evaluation frameworks for Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) predominantly assess either text-based reasoning or vision-langua…
SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities
Fengqing Jiang, Zhangchen Xu, Yuetai Li +5
Emerging large reasoning models (LRMs), such as DeepSeek-R1 models, leverage long chain-of-thought (CoT) reasoning to generate structured intermediate steps, enhancing their reason…
Small Models Struggle to Learn from Strong Reasoners
Yuetai Li, Xiang Yue, Zhangchen Xu +5
Large language models (LLMs) excel in complex reasoning tasks, and distilling their reasoning capabilities into smaller models has shown promise. However, we uncover an interesting…
On Memorization of Large Language Models in Logical Reasoning
Chulin Xie, Yangsibo Huang, Chiyuan Zhang +6
Large language models (LLMs) achieve good performance on challenging reasoning benchmarks, yet could also make basic reasoning mistakes. This contrasting behavior is puzzling when…
OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation
Zilong Wang, Yuedong Cui, Li Zhong +4
Office automation significantly enhances human productivity by automatically finishing routine tasks in the workflow. Beyond the basic information extraction studied in much of the…
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
Ge Zhang, Scott Qu, Jiaheng Liu +42
Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most comp…