4 citations · 11 across the 16 of their papers we have counts for
17 papers
Towards Autonomous and Auditable Medical Imaging Model Development
Shengyuan Liu, Jia-Xuan Jiang, Boyun Zheng +8
Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating…
Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding
Lin Chen, Bolin Ni, Qi Yang +5
Despite the remarkable capabilities of Multimodal Large Language Models (MLLMs), they still suffer from visual fading in long-context scenarios. Specifically, the attention to visu…
MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning
Shengyuan Liu, Liuxin Bao, Qi Yang +6
Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as autonomous…
Dual-Phase LLM Reasoning: Self-Evolved Mathematical Frameworks
ShaoZhen Liu, Xinting Huang, Houwen Peng +4
In recent years, large language models (LLMs) have demonstrated significant potential in complex reasoning tasks like mathematical problem-solving. However, existing research predo…
HunyuanOCR Technical Report
Hunyuan Vision Team, Pengyuan Lyu, Xingyu Wan +29
This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Na…
Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs
Yi Zhang, Bolin Ni, Xin-Sheng Chen +7
Fully open multimodal large language models (MLLMs) currently lag behind proprietary counterparts, primarily due to a significant gap in data quality for supervised fine-tuning (SF…
R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning
Qi Yang, Bolin Ni, Shiming Xiang +3
Multimodal Large Language Models (MLLMs) equipped with step-by-step thinking capabilities have demonstrated remarkable performance on complex reasoning problems. However, this thin…
RDPO: Real Data Preference Optimization for Physics Consistency Video Generation
Wenxu Qian, Chaoyue Wang, Hou Peng +3
Video generation techniques have achieved remarkable advancements in visual quality, yet faithfully reproducing real-world physics remains elusive. Preference-based model post-trai…
RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs
Meng-Hao Guo, Xuanyu Chu, Qianrui Yang +12
The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini, and o3, with their capability to process and generate content across modalities s…
R-Bench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation
Meng-Hao Guo, Jiajun Xu, Yi Zhang +14
Reasoning stands as a cornerstone of intelligence, enabling the synthesis of existing knowledge to solve complex problems. Despite remarkable progress, existing reasoning benchmark…
HiTVideo: Hierarchical Tokenizers for Enhancing Text-to-Video Generation with Autoregressive Large Language Models
Ziqin Zhou, Yifan Yang, Yuqing Yang +7
Text-to-video generation poses significant challenges due to the inherent complexity of video data, which spans both temporal and spatial dimensions. It introduces additional redun…
Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning
Hai-Long Sun, Zhun Sun, Houwen Peng +1
Recent advancements in Large Language Models (LLMs) have demonstrated enhanced reasoning capabilities, evolving from Chain-of-Thought (CoT) prompting to advanced, product-oriented…
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws
Ruihang Li, Yixuan Wei, Miaosen Zhang +3
High-quality data is crucial for the pre-training performance of large language models. Unfortunately, existing quality filtering methods rely on a known high-quality dataset as re…
Xwin-LM: Strong and Scalable Alignment Practice for LLMs
Bolin Ni, JingCheng Hu, Yixuan Wei +4
In this work, we present Xwin-LM, a comprehensive suite of alignment methodologies for large language models (LLMs). This suite encompasses several key techniques, including superv…
Common 7B Language Models Already Possess Strong Math Capabilities
Chen Li, Weiqi Wang, Jingcheng Hu +5
Mathematical capabilities were previously believed to emerge in common language models only at a very large scale or require extensive math-related pre-training. This paper shows t…
FP8-LM: Training FP8 Large Language Models
Houwen Peng, Kan Wu, Yixuan Wei +17
In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs). Our key insight is that most variables, such as gradients and optimizer s…
TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance
Kan Wu, Houwen Peng, Zhenghong Zhou +10
In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-scale language-image pre-trained models. The method introduces two core techniques: af…