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Alexander Groshev, Anastasiia Iashchenko, Pavel Paramonov +2 authors
GHOST 2.0, consisting of an Aligner and Blender module, achieves state-of-the-art results in head swapping by preserving identity information, handling extreme poses, and seamlessly integrating the reenacted head into the target background.
Hila Chefer, Uriel Singer, Amit Zohar +5 authors
VideoJAM enhances generative video models by integrating motion coherence with appearance fidelity, achieving top performance in motion coherence and visual quality.
Kanana LLM Team, Yunju Bak, Hojin Lee +26 authors
Kanana, a series of bilingual language models, achieves superior performance in Korean and competitive performance in English with lower computational costs through efficient pre-training and post-training techniques.
Xilin Wei, Xiaoran Liu, Yuhang Zang +9 authors
VideoRoPE, a 3D position embedding that preserves spatio-temporal relationships, improves performance on video tasks by addressing limitations in previous RoPE variants.
Dacheng Li, Shiyi Cao, Chengkun Cao +6 authors
A hybrid test-time scaling framework improves code generation coverage and accuracy across various models and domains.
Yixin Ye, Zhen Huang, Yang Xiao +3 authors
LIMO, a new model, achieves high mathematical reasoning performance using minimal training data, challenging the notion that extensive datasets are necessary for complex reasoning.
Jiazhen Pan, Che Liu, Junde Wu +6 authors
MedVLM-R1 enhances medical image analysis through explicit natural language reasoning generation, achieving superior accuracy with limited data and parameters via reinforcement learning rather than supervised fine-tuning.
Ganqu Cui, Lifan Yuan, Zefan Wang +20 authors
PRIME leverages implicit process rewards to improve the reinforcement learning of large language models, achieving better performance with less data compared to traditional methods.
Jintao Zhang, Chendong Xiang, Haofeng Huang +4 authors
SpargeAttn is a universal sparse and quantized attention method that accelerates diverse models by predicting the attention map and using an online softmax-aware filter to skip unnecessary matrix multiplications.
Lingfei Qian, Weipeng Zhou, Yan Wang +3 authors
A study evaluates 16 large language models on complex financial tasks, finding that domain-specific CoT fine-tuning and reinforcement learning improve performance and highlight the need for further research on long-context and multi-table reasoning.
Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov +1 authors
A new data-free cosine similarity method maps and steers features across layers of large language models, enhancing interpretability and targeted control in text generation.
Jianwei Yang, Reuben Tan, Qianhui Wu +10 authors
Magma is a multimodal foundation model with both verbal intelligence and spatial-temporal intelligence, trained on diverse datasets to perform agentic tasks like UI navigation and robotic manipulation, outperforming specialized models.
Lijie Liu, Tianxiang Ma, Bingchuan Li +4 authors
A unified video generation framework called Phantom integrates text and image inputs to produce subject-consistent videos, focusing on human generation and leveraging cross-modal alignment.
Guoqing Ma, Haoyang Huang, Kun Yan +112 authors
Step-Video-T2V is a state-of-the-art text-to-video model with 30B parameters, utilizing a deep compression Variational Autoencoder and a DiT with 3D full attention for high-quality video generation.
Chengqi Lyu, Songyang Gao, Yuzhe Gu +14 authors
A novel reinforcement learning framework, OREAL, using outcome rewards achieves high accuracy in mathematical reasoning tasks with smaller models compared to larger models trained using distillation.
Edward Yeo, Yuxuan Tong, Morry Niu +2 authors
Investigation into long chains-of-thought reasoning in large language models reveals the critical role of training compute, reward shaping, and verifiable reward signals in enabling and measuring this capability.
Juraj Gottweis, Wei-Hung Weng, Alexander Daryin +31 authors
A multi-agent AI system named AI co-scientist aids in scientific discovery by generating and validating novel hypotheses across biomedical areas, demonstrating potential improvements in drug repurposing, target discovery, and bacterial evolution understanding.
Jaehyeong Jo, Sung Ju Hwang
A continuous diffusion model for language modeling that leverages the geometry of discrete distributions outperforms existing discrete models and matches autoregressive models in performance.
Ziming Liu, Yifan Yang, Chengruidong Zhang +4 authors
RAS, a novel sampling strategy for diffusion transformers, dynamically adjusts sampling ratios based on regions of focus, achieving speedups in diffusion models with minimal quality loss.
Xu Huang, Wenhao Zhu, Hanxu Hu +4 authors
BenchMAX evaluates multilingual large language models across advanced capabilities like instruction following, reasoning, and code generation, revealing language-specific performance gaps.
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