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Related papers: Apriel-1.5-15b-Thinker

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We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms…

We present Gazal-R1, a 32-billion-parameter language model that achieves state-of-the-art performance in medical reasoning while providing transparent, step-by-step explanations for clinical decision-making. Built upon Qwen3 32B, our model…

Computation and Language · Computer Science 2025-06-30 Ahmed M. Adly , Mostafa Samy , Amr Fawzy

Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as mathematics. Despite the recognized importance, the Multimodal…

Computer Vision and Pattern Recognition · Computer Science 2024-09-20 Xiaotian Han , Yiren Jian , Xuefeng Hu , Haogeng Liu , Yiqi Wang , Qihang Fan , Yuang Ai , Huaibo Huang , Ran He , Zhenheng Yang , Quanzeng You

Contrastively trained vision-language models such as CLIP provide strong zero-shot transfer by aligning images and text in a shared embedding space. However, adapting these models to downstream tasks without degrading their open-vocabulary…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Simone Carnemolla , Salvatore Calcagno , Daniela Giordano , Concetto Spampinato , Matteo Pennisi

We introduce Phi-4-reasoning, a 14-billion parameter reasoning model that achieves strong performance on complex reasoning tasks. Trained via supervised fine-tuning of Phi-4 on carefully curated set of "teachable" prompts-selected for the…

Empowering Large Multimodal Models (LMMs) to deeply integrate image interaction with long-horizon reasoning capabilities remains a long-standing challenge in this field. Recent advances in vision-centric reasoning explore a promising…

Computer Vision and Pattern Recognition · Computer Science 2025-12-19 Runqi Qiao , Qiuna Tan , Minghan Yang , Guanting Dong , Peiqing Yang , Shiqiang Lang , Enhui Wan , Xiaowan Wang , Yida Xu , Lan Yang , Chong Sun , Chen Li , Jing Lyu , Honggang Zhang

In recent months, substantial progress has been made in complex reasoning of Large Language Models, particularly through the application of test-time scaling. Notable examples include o1/o3/o4 series and DeepSeek-R1. When responding to a…

Artificial Intelligence · Computer Science 2025-08-28 Xifeng Yao , Chengyuan Ma , Dongyu Lang , Yinhao Ni , Zhiwei Xu , Huarui Xie , Zihao Chen , Guang Shen , Dandan Tu , Yi Bai , Changzheng Zhang

Information comes in diverse modalities. Multimodal native AI models are essential to integrate real-world information and deliver comprehensive understanding. While proprietary multimodal native models exist, their lack of openness imposes…

Computer Vision and Pattern Recognition · Computer Science 2025-01-13 Dongxu Li , Yudong Liu , Haoning Wu , Yue Wang , Zhiqi Shen , Bowen Qu , Xinyao Niu , Fan Zhou , Chengen Huang , Yanpeng Li , Chongyan Zhu , Xiaoyi Ren , Chao Li , Yifan Ye , Peng Liu , Lihuan Zhang , Hanshu Yan , Guoyin Wang , Bei Chen , Junnan Li

Tool-Integrated Reasoning (TIR) empowers large language models (LLMs) to tackle complex tasks by interleaving reasoning steps with external tool interactions. However, existing reinforcement learning methods typically rely on outcome- or…

Computation and Language · Computer Science 2026-01-16 Changle Qu , Sunhao Dai , Hengyi Cai , Jun Xu , Shuaiqiang Wang , Dawei Yin

Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core bottleneck in making MLLMs more accessible and…

Recent advances in image reasoning methods, particularly "Thinking with Images", have demonstrated remarkable success in Multimodal Large Language Models (MLLMs); however, this dynamic reasoning paradigm has not yet been extended to video…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Shijian Wang , Jiarui Jin , Xingjian Wang , Linxin Song , Runhao Fu , Hecheng Wang , Zongyuan Ge , Yuan Lu , Xuelian Cheng

Large Language Models (LLM) with reasoning capabilities offer a promising path for improving candidate evaluation in planning frameworks, but their relative performance against traditional non-reasoning models remains largely underexplored.…

Machine Learning · Computer Science 2025-05-08 Md Fahim Anjum

We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 86.7 on AIME 2024, 55.0 on Codeforces and 77.3 on GPQA,…

Computation and Language · Computer Science 2025-04-30 ByteDance Seed , : , Jiaze Chen , Tiantian Fan , Xin Liu , Lingjun Liu , Zhiqi Lin , Mingxuan Wang , Chengyi Wang , Xiangpeng Wei , Wenyuan Xu , Yufeng Yuan , Yu Yue , Lin Yan , Qiying Yu , Xiaochen Zuo , Chi Zhang , Ruofei Zhu , Zhecheng An , Zhihao Bai , Yu Bao , Xingyan Bin , Jiangjie Chen , Feng Chen , Hongmin Chen , Riwei Chen , Liangqiang Chen , Zixin Chen , Jinsong Chen , Siyan Chen , Kaiyuan Chen , Zhi Chen , Jin Chen , Jiecao Chen , Jinxin Chi , Weinan Dai , Ning Dai , Jiahui Dai , Shihan Dou , Yantao Du , Zhengyin Du , Jianhui Duan , Chen Dun , Ting-Han Fan , Jiazhan Feng , Junda Feng , Ziyuan Feng , Yuwei Fu , Wenqi Fu , Hanjie Fu , Hao Ge , Hongyi Guo , Mingji Han , Li Han , Wenhao Hao , Xintong Hao , Qianyu He , Jerry He , Feng He , Wen Heng , Zehua Hong , Qi Hou , Liang Hu , Shengding Hu , Nan Hu , Kai Hua , Qi Huang , Ziyue Huang , Hongzhi Huang , Zihao Huang , Ting Huang , Wenhao Huang , Wei Jia , Bin Jia , Xiaoying Jia , Yuhua Jiang , Haobin Jiang , Ziheng Jiang , Kaihua Jiang , Chengquan Jiang , Jianpeng Jiao , Xiaoran Jin , Xing Jin , Xunhao Lai , Zheng Li , Xiang Li , Liyi Li , Hongkai Li , Zheng Li , Shengxian Wan , Ya Wang , Yunshui Li , Chenggang Li , Niuniu Li , Siyu Li , Xi Li , Xiao Li , Aoyan Li , Yuntao Li , Nianning Liang , Xinnian Liang , Haibin Lin , Weijian Lin , Ye Lin , Zhicheng Liu , Guanlin Liu , Guanlin Liu , Chenxiao Liu , Yan Liu , Gaohong Liu , Juncai Liu , Chundian Liu , Deyi Liu , Kaibo Liu , Siyao Liu , Qi Liu , Yongfei Liu , Kang Liu , Gan Liu , Boyi Liu , Rui Long , Weiqiang Lou , Chenwei Lou , Xiang Luo , Yao Luo , Caiping Lv , Heyang Lv , Bole Ma , Qianli Ma , Hongzhi Ma , Yiyuan Ma , Jin Ma , Wenchang Ma , Tingting Ma , Chen Mao , Qiyang Min , Zhe Nan , Guanghan Ning , Jinxiang Ou , Haojie Pan , Renming Pang , Yanghua Peng , Tao Peng , Lihua Qian , Lihua Qian , Mu Qiao , Meng Qu , Cheng Ren , Hongbin Ren , Yong Shan , Wei Shen , Ke Shen , Kai Shen , Guangming Sheng , Jinlong Shi , Wenlei Shi , Guang Shi , Shuai Shuai Cao , Yuxin Song , Zuquan Song , Jing Su , Yifan Sun , Tao Sun , Zewei Sun , Borui Wan , Zihan Wang , Xiaohui Wang , Xi Wang , Shuguang Wang , Jun Wang , Qinlong Wang , Chenyuan Wang , Shuai Wang , Zihan Wang , Changbao Wang , Jiaqiang Wang , Shihang Wang , Xuwu Wang , Zaiyuan Wang , Yuxuan Wang , Wenqi Wang , Taiqing Wang , Chengzhi Wei , Houmin Wei , Ziyun Wei , Shufa Wei , Zheng Wu , Yonghui Wu , Yangjun Wu , Bohong Wu , Shuang Wu , Jingqiao Wu , Ning Wu , Shuangzhi Wu , Jianmin Wu , Chenguang Xi , Fan Xia , Yuqiao Xian , Liang Xiang , Boren Xiang , Bowen Xiao , Zhen Xiao , Xia Xiao , Yongsheng Xiao , Chao Xin , Shulin Xin , Yuwen Xiong , Jingjing Xu , Ziwen Xu , Chenyin Xu , Jiayi Xu , Yifan Xu , Wei Xu , Yufei Xu , Shikun Xu , Shipeng Yan , Shen Yan , Qingping Yang , Xi Yang , Tianhao Yang , Yuehang Yang , Yuan Yang , Ximing Yang , Zeyu Yang , Guang Yang , Yifan Yang , Xuesong Yao , Bairen Yi , Fan Yin , Jianian Yin , Ziqiang Ying , Xiangyu Yu , Hongli Yu , Song Yu , Menghan Yu , Huan Yu , Siyu Yuan , Jun Yuan , Yutao Zeng , Tianyang Zhan , Zheng Zhang , Yun Zhang , Mofan Zhang , Wang Zhang , Ru Zhang , Zhi Zhang , Tianqi Zhang , Xinyi Zhang , Zhexi Zhang , Sijun Zhang , Wenqiang Zhang , Xiangxiang Zhang , Yongtao Zhang , Yuyu Zhang , Ge Zhang , He Zhang , Yue Zhang , Renjie Zheng , Ningxin Zheng , Zhuolin Zheng , Yaowei Zheng , Chen Zheng , Xiaoyun Zhi , Wanjun Zhong , Cheng Zhong , Zheng Zhong , Baoquan Zhong , Xun Zhou , Na Zhou , Huan Zhou , Hang Zhu , Defa Zhu , Wenjia Zhu , Lei Zuo

Although Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse tasks, they encounter challenges in terms of reasoning efficiency, large model size and overthinking. However, existing lightweight…

Artificial Intelligence · Computer Science 2025-11-21 Qixiang Yin , Huanjin Yao , Jianghao Chen , Jiaxing Huang , Zhicheng Zhao , Fei Su

Multimodal Large Language Models (MLLMs) equipped with step-by-step thinking capabilities have demonstrated remarkable performance on complex reasoning problems. However, this thinking process is redundant for simple problems solvable…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Qi Yang , Bolin Ni , Shiming Xiang , Han Hu , Houwen Peng , Jie Jiang

Recent advancements in large reasoning models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual reasoning, the lack of transparent and reproducible data curation…

Artificial Intelligence · Computer Science 2025-12-08 Kaichen Zhang , Keming Wu , Zuhao Yang , Bo Li , Kairui Hu , Bin Wang , Ziwei Liu , Xingxuan Li , Lidong Bing

Recent advancements in Large Language Models (LLMs) have revealed a significant performance gap between closed-source and open-source models, particularly in tasks requiring complex reasoning and precise instruction following. This paper…

Artificial Intelligence · Computer Science 2025-07-01 Ziqi Zhong , Xunzhu Tang

Tool-Integrated Reasoning (TIR) extends LLM capabilities by leveraging external environments. However, existing methods lack the deliberation during sequential tool invocation required for strategic planning and self-correction. While RL…

Artificial Intelligence · Computer Science 2026-05-29 Yang He , Xiao Ding , Bibo Cai , Yufei Zhang , Kai Xiong , Zhouhao Sun , Bing Qin , Ting Liu

This paper presents a compute-efficient approach to pre-training a Language Model-the "1.5-Pints"-in only 9 days, while outperforming state-of-the-art models as an instruction-following assistant.Based on MT-Bench (a benchmark that emulates…

Computation and Language · Computer Science 2024-08-08 Calvin Tan , Jerome Wang

We introduce Motif-2-12.7B, a new open-weight foundation model that pushes the efficiency frontier of large language models by combining architectural innovation with system-level optimization. Designed for scalable language understanding…