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Yuchao Gu, Weijia Mao, Mike Zheng Shou
FAR, an autoregressive video model, achieves state-of-the-art results by balancing locality and long-range context with FlexRoPE and long short-term context modeling, addressing challenges in long-context video generation.
Eduard Allakhverdov, Elizaveta Goncharova, Andrey Kuznetsov
A method using autoencoder and Gumbel-Softmax selection identifies and retains only the most informative visual tokens, enabling efficient multimodal pruning with minimal performance loss.
Sambal Shikhar, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly +5 authors
LLMVoX is a lightweight, LLM-agnostic TTS system that decouples speech synthesis from model processing, ensuring high-quality speech with low latency and supporting seamless multimodal dialogue.
WanTeam, Ang Wang, Baole Ai +59 authors
Wan, a comprehensive suite of video foundation models built on the diffusion transformer paradigm, advannces video generation by introducing a novel VAE, scalable pre-training strategies, and large-scale data curation, offering superior performance and versatility across various applications with both large and efficient models.
Haoqi Yuan, Yu Bai, Yuhui Fu +6 authors
Being-0, a hierarchical framework integrating Foundation Models with a modular skill library and a lightweight vision-language model, achieves efficient real-time performance on complex humanoid robot tasks by coordinating high-level cognition with low-level control.
Xun Liang, Hanyu Wang, Huayi Lai +7 authors
Sparse Expert Activation Pruning (SEAP) is a method for pruning large language models that reduces computational overhead while maintaining accuracy by identifying and retaining task-specific expert activations.
Yiran Zhao, Chaoqun Liu, Yue Deng +8 authors
Babel is an open multilingual LLM that expands its parameter count through layer extension, covering numerous languages and achieving superior performance in multilingual tasks compared to other open LLMs.
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo +5 authors
Rule-based reinforcement learning enhances multimodal large language models for GUI action prediction, improving data efficiency and performance.
Zechen Bai, Hai Ci, Mike Zheng Shou
IPV-Bench evaluates video generation and understanding models on creating and interpreting impossible videos, highlighting their limitations and guiding future advancements.
Fanqing Meng, Lingxiao Du, Zongkai Liu +11 authors
MM-Eureka extends rule-based reinforcement learning to multimodal reasoning, achieving strong capabilities in data-efficient multimodal tasks without supervised fine-tuning.
Gemma Team, Aishwarya Kamath, Johan Ferret +213 authors
Gemma 3 introduces vision capabilities, broader language coverage, and extended context length, featuring an optimized architecture and post-training enhancements to outperform previous versions.
Zeyinzi Jiang, Zhen Han, Chaojie Mao +3 authors
VACE, an all-in-one framework for video creation and editing, integrates multiple tasks within a unified model using a Video Condition Unit and Context Adapter for flexible and consistent video synthesis.
Siyin Wang, Zhaoye Fei, Qinyuan Cheng +4 authors
Dual Preference Optimization (D$^2$PO) enhances embodied task planning in large vision-language models by jointly optimizing state prediction and action selection using preference learning and tree search for efficient trajectory collection.
Hengguang Zhou, Xirui Li, Ruochen Wang +3 authors
A non-SFT model replicated emergent reasoning characteristics for multimodal tasks using reinforcement learning, achieving higher accuracy than base and SFT models on CVBench.
Kashun Shum, Yuzhen Huang, Hongjian Zou +5 authors
A lightweight data selection method called PreSelect effectively reduces computational requirements while enhancing language model performance by focusing on data predictive of downstream abilities.
Zorik Gekhman, Eyal Ben David, Hadas Orgad +5 authors
LLMs encode more internal factual knowledge than they express externally, with some knowledge so deeply hidden that it is never generated, despite repeated sampling.
Jian Zhang, Zhiyuan Wang, Zhangqi Wang +6 authors
A Multi-Agent framework with feedback mechanisms and Socratic guidance addresses multi-modal scientific problems and enhances reasoning and reflection, outperforming current models.
Rongyao Fang, Chengqi Duan, Kun Wang +9 authors
A new reasoning-driven paradigm, GoT, improves text-to-image generation and editing through language understanding and reasoning chains, integrating Qwen2.5-VL and an enhanced diffusion model with a Semantic-Spatial Guidance Module.
Quy-Anh Dang, Chris Ngo
Reinforcement learning enhances reasoning in small language models with limited resources, demonstrating significant performance improvements in mathematical reasoning tasks.
NVIDIA, Alisson Azzolini, Hannah Brandon +42 authors
Cosmos-Reason1 models, using hierarchical and two-dimensional ontologies for physical common sense and embodied reasoning, generate embodied decisions through multimodal large language models trained in vision and Physical AI stages.
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