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Zhen Li, Chuanhao Li, Xiaofeng Mao +17 authors
Sekai, a worldwide video dataset with comprehensive annotations, is introduced to support world exploration applications, enhancing video generation models.
Yifan Wang, Jianjun Zhou, Haoyi Zhu +7 authors
A permutation-equivariant neural network, $\pi^3$, reconstructs visual geometry without a fixed reference view, achieving state-of-the-art performance in camera pose estimation, depth estimation, and point map reconstruction.
Dawei Gao, Zitao Li, Yuexiang Xie +20 authors
AgentScope enhances agentic applications by providing flexible tool-based interactions, unified interfaces, and advanced infrastructure based on the ReAct paradigm, supporting efficient and safe development and deployment.
Zhenyi Liao, Qingsong Xie, Yanhao Zhang +4 authors
The study enhances visual-spatial reasoning in multi-modal large language models through GRPO training using the VSI-100k dataset, demonstrating significant performance improvements over base models.
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.
Zehong Ma, Longhui Wei, Shuai Wang +2 authors
The frequency-DeCoupled pixel diffusion framework improves image generation efficiency and quality by separating high-frequency details and low-frequency semantics, achieving superior performance compared to existing pixel diffusion models.
Kang Chen, Zhihao Liu, Tonghe Zhang +10 authors
The framework π<sub>RL</sub> uses reinforcement learning to train flow-based Vision-Language-Action models, addressing challenges with intractable action log-likelihoods and achieving significant performance improvements over supervised fine-tuning.
Yuqiao Tan, Minzheng Wang, Shizhu He +6 authors
The paper decomposes the policy of large language models into internal layer and modular policies, revealing distinct reasoning patterns across layers and proposing Bottom-up Policy Optimization to enhance performance on complex reasoning tasks.
Long Lian, Yifan Ding, Yunhao Ge +8 authors
The Describe Anything Model (DAM) leverages a focal prompt and localized vision backbone to achieve detailed localized captioning, outperforming existing models on various benchmarks through a semi-supervised data pipeline.
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.
Ethan Chern, Zhulin Hu, Bohao Tang +4 authors
Real-time multimodal video generation via diffusion is enabled by an improved distillation approach with multimodal conditioning and optimized scheduling, reducing inference latency while maintaining quality for interactive systems.
Hao Sun, Zile Qiao, Jiayan Guo +6 authors
ZeroSearch is a reinforcement learning framework that improves LLM search capabilities without using real search engines, using supervised fine-tuning and curriculum-based rollouts to handle document quality and API cost challenges.
Qinghe Wang, Xiaoyu Shi, Baolu Li +7 authors
MultiShotMaster extends a single-shot model with novel RoPE variants for flexible and controllable multi-shot video generation, addressing data scarcity with an automated annotation pipeline.
Alexander Amini, Anna Banaszak, Harold Benoit +30 authors
LFM2, a family of compact foundation models, achieves high efficiency and performance on-device through hardware-in-the-loop architecture search and advanced training techniques, supporting various tasks including multimodal applications.
Run-Ze Fan, Zengzhi Wang, Pengfei Liu
TextbookReasoning and MegaScience datasets, along with a comprehensive evaluation system, enhance scientific reasoning in AI by providing high-quality, verifiable data and outperforming existing models.
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.
Yunlong Lin, Zixu Lin, Kunjie Lin +8 authors
JarvisArt, an MLLM-driven agent, achieves superior photo retouching by understanding user intent and coordinating multiple retouching tools in Lightroom, outperforming GPT-4o on a novel benchmark.
Zichen Wen, Jiashu Qu, Dongrui Liu +11 authors
DIJA is a framework that exploits safety weaknesses in diffusion-based large language models by constructing adversarial prompts, demonstrating significant vulnerabilities in their alignment mechanisms.
Jiaqi Wang, Weijia Wu, Yi Zhan +6 authors
The Video Reality Test benchmark evaluates the realism and detection of AI-generated ASMR videos with audio, revealing that even the best models can deceive VLMs and humans, highlighting limitations in perceptual fidelity and audio-visual consistency.
Xinrun Xu, Pi Bu, Ye Wang +7 authors
DeepPHY evaluates Vision Language Models' physical reasoning and control through simulated environments with varying difficulty levels.
Peiyu Wang, Yi Peng, Yimeng Gan +12 authors
Skywork UniPic, a 1.5 billion-parameter autoregressive model, unifies image understanding, text-to-image generation, and image editing with state-of-the-art performance on commodity hardware.
Longwen Zhang, Qixuan Zhang, Haoran Jiang +4 authors
BANG is a generative approach using latent diffusion models and temporal attention to enable intuitive, part-level decomposition of 3D objects with precise control and multimodal interaction.
Guilong Lu, Xuntao Guo, Rongjunchen Zhang +2 authors
BizFinBench is a benchmark for evaluating large language models in financial applications, revealing distinct performance patterns across various tasks.
Jiacheng Chen, Ramin Mehran, Xuhui Jia +2 authors
A generative visual compositing framework using a diffusion model for scene editing and composition with source masking and simulated object jittering.
King Zhu, Hanhao Li, Siwei Wu +12 authors
Systematic exploration of test-time scaling methods in large language agents reveals that computational scaling improves performance, especially through parallel sampling, sequential revision, effective verification, and increased rollout diversity.
Doohyuk Jang, Yoonjeon Kim, Chanjae Park +2 authors
A diagnostic set examines and categorizes reasoning rigidity in large language models, identifying patterns where models ignore instructions and default to familiar reasoning.
Sixiang Chen, Jinbin Bai, Zhuoran Zhao +16 authors
An empirical study of GPT-4o's image generation capabilities across multiple tasks reveals its strengths and limitations compared to other models, highlighting the importance of architectural design and data scaling in unified generative frameworks.
Minghao Wu, Weixuan Wang, Sinuo Liu +7 authors
Research reveals significant disparities in multilingual benchmark evaluations, emphasizing the need for culturally and linguistically tailored benchmarks over translations to achieve equitable technological progress.
Jinsong Li, Xiaoyi Dong, Yuhang Zang +3 authors
DAEDAL, a novel training-free denoising strategy, enables dynamic length adaptation in Diffusion Large Language Models, improving performance and computational efficiency.
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