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Shelly Bensal, Umar Jamil, Christopher Bryant +5 authors
A method using self-reflection and reinforcement learning improves the performance of large language models, especially with limited feedback, by rewarding self-reflections that lead to better task performance.
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MiniMax, Aili Chen, Aonian Li +124 authors
A hybrid-attention reasoning model called MiniMax-M1, featuring a Mixture-of-Experts architecture and lightning attention mechanism, is introduced for efficient long-input processing and reinforcement learning.
Qingxiu Dong, Li Dong, Yao Tang +4 authors
Reinforcement Pre-Training (RPT) improves language model accuracy through reinforcement learning and offers a scalable method for leveraging text data for general-purpose RL.
Shenzhi Wang, Le Yu, Chang Gao +15 authors
Token entropy patterns are crucial in RLVR, with high-entropy tokens significantly impacting reasoning performance and model optimization.
Mustafa Shukor, Dana Aubakirova, Francesco Capuano +11 authors
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
Mingjie Liu, Shizhe Diao, Ximing Lu +5 authors
Prolonged reinforcement learning training (ProRL) uncovers novel reasoning strategies in language models, outperforming base models and suggesting meaningful expansion of reasoning capabilities.
Sergey Pletenev, Maria Marina, Nikolay Ivanov +6 authors
EverGreenQA, a multilingual QA dataset with evergreen labels, is introduced to benchmark LLMs on temporality encoding and assess their performance through verbalized judgments and uncertainty signals.
Pengyi Li, Matvey Skripkin, Alexander Zubrey +2 authors
Reinforcement Learning via Self-Confidence (RLSC) improves large language model accuracy using the model's confidence as a reward signal, eliminating the need for human labels or reward engineering.
Zhiyuan Liang, Dongwen Tang, Yuhao Zhou +11 authors
Drag-and-Drop LLMs generate task-specific parameters through prompt-conditioned parameter generation, achieving significant efficiency gains and cross-domain generalization without per-task training.
LASA Team, Weiwen Xu, Hou Pong Chan +16 authors
A medical-specialized multimodal large language model, Lingshu, is introduced with enhanced data curation and reinforcement learning to address limitations in medical applications.
Yu Gao, Haoyuan Guo, Tuyen Hoang +41 authors
Seedance 1.0 offers high-performance video generation by integrating advanced data curation, efficient architecture, post-training optimization, and model acceleration, resulting in superior quality and speed.
Yu Sun, Xingyu Qian, Weiwen Xu +7 authors
ReasonMed, a large medical reasoning dataset, enhances the accuracy of medical question answering models by combining detailed reasoning paths with concise summaries, setting new benchmarks for model performance.
MiniCPM Team, Chaojun Xiao, Yuxuan Li +72 authors
MiniCPM4, a highly efficient large language model for end-side devices, achieves superior performance using innovations in sparse attention, pre-training datasets, training algorithms, and inference systems.
Junyu Zhang, Runpei Dong, Han Wang +8 authors
AlphaOne dynamically modulates reasoning in large models by introducing $\alpha$ moment and Bernoulli process for slow thinking, improving efficiency and capability across diverse domains.
Xueqing Peng, Lingfei Qian, Yan Wang +41 authors
MultiFinBen is a multilingual and multimodal benchmark for financial domain tasks, evaluating LLMs across modalities and linguistic settings, revealing challenges in complex cross-lingual and multimodal financial reasoning.
Hong Li, Houyuan Chen, Chongjie Ye +11 authors
Photometric stereo aims to recover high-quality surface normals under arbitrary lighting conditions, addressing challenges related to illumination-surface normal coupling and high-frequency geometric detail preservation.
Vishesh Tripathi, Tanmay Odapally, Indraneel Das +2 authors
A novel multimodal document chunking approach using Large Multimodal Models (LMMs) enhances RAG performance by accurately processing complex PDF documents, including multi-page tables and embedded visuals.
Yanzhao Zhang, Mingxin Li, Dingkun Long +9 authors
The Qwen3 Embedding series, built on Qwen3 foundation models, offers advanced text embedding and reranking capabilities through a multi-stage training pipeline, achieving state-of-the-art performance across multilingual and retrieval benchmarks.
Yuchen Lin, Chenguo Lin, Panwang Pan +4 authors
PartCrafter is a unified 3D generative model that synthesizes multiple semantically meaningful 3D meshes from a single image using a compositional latent space and hierarchical attention mechanism.
Zhenran Xu, Xue Yang, Yiyu Wang +7 authors
ComfyUI-Copilot uses a large language model and multi-agent system to enhance the usability and efficiency of the AI-driven art creation platform ComfyUI.
Ujjwal Upadhyay, Mukul Ranjan, Zhiqiang Shen +1 authors
SpookyBench is a benchmark for temporal pattern recognition in videos that highlights the limitations of vision-language models in processing noise-like frames without spatial information.
Xiaomi LLM-Core Team, Zihao Yue, Zhenru Lin +71 authors
MiMo-VL-7B-SFT and MiMo-VL-7B-RL provide state-of-the-art general visual understanding and multimodal reasoning through four-stage pre-training and Mixed On-policy Reinforcement Learning, outperforming models with up to 78B parameters.
Guilherme Penedo, Hynek Kydlíček, Vinko Sabolčec +7 authors
A new pre-training dataset curation pipeline based on FineWeb supports multilingual LLMs by automatically adapting to any language, improving model performance and balancing dataset quality.
Chenyuan Wu, Pengfei Zheng, Ruiran Yan +19 authors
OmniGen2, a versatile generative model, introduces dual decoding pathways for text and images, preserves original text generation, and achieves competitive results with a new subject-driven benchmark.
Yuhao Zhou, Yiheng Wang, Xuming He +24 authors
Scientists' First Exam (SFE) benchmark assesses scientific cognitive capacities of Multimodal Large Language Models through perception, understanding, and comparative reasoning.
Mingxuan Du, Benfeng Xu, Chiwei Zhu +2 authors
DeepResearch Bench offers a benchmark framework to evaluate the capabilities of Deep Research Agents in terms of research quality and information retrieval accuracy across multiple fields.
Zafir Stojanovski, Oliver Stanley, Joe Sharratt +4 authors
Reasoning Gym provides a library of reasoning environments with verifiable rewards and procedural data generation for reinforcement learning, enabling the evaluation and training of reasoning models at varying difficulty levels.
Ruizhong Qiu, Gaotang Li, Tianxin Wei +2 authors
SAFFRON, a novel inference scaling paradigm, enhances LLM safety by reducing reward model evaluations through a multifurcation reward model and other optimizations.
Yifan Zhang, Chunli Peng, Boyang Wang +8 authors
Matrix-Game, a controllable game world generation model trained in a two-stage process, outperforms existing models by producing high-quality, action-controllable, and physically consistent Minecraft world videos.
Mikhail Salnikov, Dmitrii Korzh, Ivan Lazichny +7 authors
LLMs exhibit significant geopolitical biases in their interpretation of historical events, and simple debiasing methods have limited effectiveness; a novel dataset for further research is provided.
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