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Xiaomi LLM-Core Team, Bingquan Xia, Bowen Shen +61 authors
MiMo-7B, a large language model optimized for reasoning tasks, enhances pre-training with data mixing and Multi-Token Prediction, and post-training with reinforcement learning on math and programming problems, achieving superior performance over larger models.
Ling-Team, Ang Li, Ben Liu +139 authors
Ling 2.0, a reasoning-oriented language model series, achieves high efficiency and accuracy through a Mixture-of-Experts paradigm, sparse activation, and innovative training techniques.
Team Seedream, Yunpeng Chen, Yu Gao +47 authors
Seedream 4.0 is a high-performance multimodal image generation system that integrates text-to-image synthesis, image editing, and multi-image composition using a diffusion transformer and VAE, achieving state-of-the-art results with efficient training and inference.
Nex-AGI Team, Yuxuan Cai, Lu Chen +62 authors
The introduction of NexAU, NexA4A, and NexGAP enables the scaling of complexity, diversity, and fidelity in interactive environments for training large language models as autonomous agents, resulting in superior performance.
Xiyao Wang, Chunyuan Li, Jianwei Yang +4 authors
Reinforcement learning on preference-labeled critic datasets enhances a generative model's performance, creating a unified multimodal system that excels in both evaluation and generation.
Zongxia Li, Wenhao Yu, Chengsong Huang +8 authors
Vision-SR1 uses reinforcement learning to enhance visual reasoning in vision-language models by decomposing the process into visual perception and language reasoning stages, improving accuracy and reducing hallucinations.
Fang Wu, Weihao Xuan, Ximing Lu +2 authors
Reinforcement Learning with Verifiable Rewards (RLVR) enhances precision but may limit exploration and discovery of new solutions, suggesting potential limits to its effectiveness in expanding reasoning capabilities.
Yemin Shi, Yu Shu, Siwei Dong +4 authors
Voila is an end-to-end voice-language model that enables low-latency, emotionally expressive voice interactions and supports multiple applications including ASR, TTS, and multilingual speech translation.
Valerii Startsev, Alexander Ustyuzhanin, Alexey Kirillov +2 authors
A new method using a pre-trained generative model helps construct a high-impact SFT dataset, Alchemist, which improves the generative quality of text-to-image models while maintaining diversity.
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.
Yuhao Zhang, Zhiheng Liu, Fan Bu +3 authors
Soundwave addresses the representation space gap and sequence length inconsistency in end-to-end speech large language models using an efficient training strategy and novel architecture, outperforming Qwen2-Audio with significantly less data.
Ding Chen, Qingchen Yu, Pengyuan Wang +6 authors
xVerify, an efficient answer verifier, demonstrates strong equivalence judgment capabilities in evaluating reasoning models across various objective questions, with F1 scores and accuracy surpassing other methods.
Heeji Yoon, Jaewoo Jung, Junwan Kim +10 authors
VIRAL, a regularization strategy, aligns MLLMs' visual representations with pre-trained VFMs, enhancing performance on vision-centric tasks.
Zhenghai Xue, Longtao Zheng, Qian Liu +4 authors
SimpleTIR stabilizes multi-turn Tool-Integrated Reasoning training by filtering out void turns, achieving state-of-the-art performance on math reasoning benchmarks.
NVIDIA, Niket Agarwal, Arslan Ali +75 authors
The Cosmos World Foundation Model Platform provides a framework for developing customized world models for Physical AI by leveraging video curation, pre-trained models, and tokenizers.
Junyu Luo, Weizhi Zhang, Ye Yuan +23 authors
A survey of large language model agents provides a structured taxonomy, exploring architectural principles, collaboration, evolution, evaluation, and applications, highlighting promising research directions.
Mouxiang Chen, Binyuan Hui, Zeyu Cui +5 authors
Parallel scaling (ParScale) improves inference efficiency by reusing existing parameters and executing multiple transformations in parallel, offering superior performance with reduced memory and latency compared to parameter scaling.
Hao Fei, Yuan Zhou, Juncheng Li +29 authors
A General-Level evaluation framework is introduced to assess multimodal generalist performance in LLMs, using Synergy to measure consistent capabilities across comprehension and generation.
Jiajie Zhang, Nianyi Lin, Lei Hou +2 authors
AdaptThink, an RL algorithm, enhances reasoning models' efficiency and performance by adaptively selecting the optimal thinking mode based on problem difficulty.
Zirui Guo, Xubin Ren, Lingrui Xu +2 authors
RAG-Anything is a unified framework that enhances multimodal knowledge retrieval by integrating cross-modal relationships and semantic matching, outperforming existing methods on complex benchmarks.
Zhaorun Chen, Zhuokai Zhao, Kai Zhang +15 authors
DreamGym is a unified framework that synthesizes diverse experiences for scalable online RL training, improving agent performance and reducing real-world interactions.
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.
Junteng Liu, Yunji Li, Chi Zhang +12 authors
WebExplorer, a data-driven approach for developing advanced web agents, achieves state-of-the-art performance in information-seeking tasks through systematic data generation and reinforcement learning.
Jiakang Yuan, Tianshuo Peng, Yilei Jiang +8 authors
MME-Reasoning evaluates the logical reasoning capabilities of multimodal large language models, revealing significant limitations and performance imbalances across inductive, deductive, and abductive reasoning types.
Runzhe Zhan, Yafu Li, Zhi Wang +5 authors
ExGRPO, a framework that prioritizes valuable reasoning experiences, improves and stabilizes reinforcement learning from verifiable rewards for large language models.
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.
PAN Team, Jiannan Xiang, Yi Gu +31 authors
PAN, a general, interactable, and long-horizon world model, predicts future world states using a Generative Latent Prediction (GLP) architecture that combines autoregressive latent dynamics with a video diffusion decoder, enabling detailed, long-term, and coherent video simulation.
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.
Xinjie Zhang, Jintao Guo, Shanshan Zhao +7 authors
A survey examines the integration of multimodal understanding and image generation models, categorizing them into diffusion-based, autoregressive-based, and hybrid approaches, and discussing challenges and future research directions.
Yilun Zhao, Lujing Xie, Haowei Zhang +16 authors
A comprehensive benchmark for evaluating foundation models in video understanding includes domain-specific questions and expert evaluations, highlighting the gap between current models and human expertise.
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