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Qwen, An Yang, Baosong Yang +39 authors
Qwen2.5, an enhanced series of large language models, demonstrates superior performance across various benchmarks and use cases through extensive pre-training and advanced post-training techniques.
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Benjamin Warner, Antoine Chaffin, Benjamin Clavié +11 authors
ModernBERT, an optimized encoder-only transformer model, demonstrates superior performance and efficiency in diverse classification and retrieval tasks compared to previous models.
Zhe Chen, Weiyun Wang, Yue Cao +37 authors
InternVL 2.5, an advanced multimodal large language model, showcases competitive performance across various benchmarks, including multimodal reasoning and understanding, and is the first open-source model to surpass 70% on the MMMU benchmark using Chain-of-Thought reasoning.
Orr Zohar, Xiaohan Wang, Yann Dubois +9 authors
A comprehensive study reveals effective design principles for video understanding in Large Multimodal Models, introducing Apollo, which achieves superior performance across different model sizes.
Andreas Steiner, André Susano Pinto, Michael Tschannen +15 authors
PaliGemma 2 integrates a SigLIP-So400m vision encoder with Gemma 2 models of varying sizes and resolutions, advancing transfer performance across diverse vision-language tasks, including OCR and captioning.
Marah Abdin, Jyoti Aneja, Harkirat Behl +24 authors
A 14-billion parameter language model surpasses its teacher model in STEM-focused QA through strategic use of synthetic data, improved data quality, and enhanced training techniques.
Senqiao Yang, Yukang Chen, Zhuotao Tian +4 authors
VisionZip reduces visual token redundancy and enhances efficiency in vision-language models, improving performance and inference speed across various tasks.
Viet Nguyen, Anh Aengus Nguyen, Trung Dao +4 authors
SNOOPI framework improves one-step text-to-image diffusion models with Proper Guidance-SwiftBrush and Negative-Away Steer Attention, enhancing both training stability and negative prompt support.
Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez +11 authors
A Byte Latent Transformer (BLT) matches tokenization-based LLM performance at scale with improved inference efficiency and robustness, using entropy-based byte patching.
Junying Chen, Zhenyang Cai, Ke Ji +5 authors
The proposed approach enhances medical reasoning in large language models using verifiable problems, guided search, and reinforcement learning to achieve superior performance in medical problem-solving.
Taiming Lu, Tianmin Shu, Junfei Xiao +8 authors
GenEx generates 3D environments from a single image, enabling AI agents to explore and interact with a consistent, expansive space through guided generative imagination.
Pan Zhang, Xiaoyi Dong, Yuhang Cao +26 authors
A framework combining disentangled perception, reasoning, and memory mechanisms enables real-time interaction for long-term multimodal AI systems.
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su +4 authors
A new reasoning paradigm, Coconut, utilizes continuous representations in latent space to enhance LLM performance on complex reasoning tasks, particularly those requiring backtracking.
Junnan Liu, Hongwei Liu, Linchen Xiao +6 authors
G-Pass@k and LiveMathBench assess Large Language Models' reasoning capabilities and consistency using complex mathematical problems.
Jianfeng Xiang, Zelong Lv, Sicheng Xu +6 authors
A 3D generation method using a unified SLAT representation and rectified flow transformers achieves high-quality results across different formats and conditions.
Chenglin Yang, Celong Liu, Xueqing Deng +4 authors
1.58-bit quantization of the FLUX.1-dev text-to-image model achieves comparable generation quality with reduced storage and inference overhead using self-supervised methods.
Junyu Luo, Xiao Luo, Kaize Ding +3 authors
A noise-robust supervised fine-tuning framework enhances large language models' performance in downstream tasks by detecting and relabeling noisy data.
Chujie Zheng, Zhenru Zhang, Beichen Zhang +6 authors
ProcessBench evaluates models' ability to identify errors in mathematical reasoning steps, showing that existing process reward models struggle with difficult problems and underperform compared to critic models and a fine-tuned PRM.
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li +8 authors
FastVLM, using FastViTHD, optimizes Vision Language Models by scaling image resolution to reduce latency and token count, improving time-to-first-token and maintaining performance.
Yang Shen, Xiu-Shen Wei, Yifan Sun +6 authors
An auto-regressive vision-language model trained with explanatory instructions achieves instruction-level zero-shot capabilities and strong zero-shot generalization in computer vision tasks.
Zongyu Lin, Wei Liu, Chen Chen +14 authors
STIV, a text-image-conditioned video generation method integrating Diffusion Transformer and classifier-free guidance, achieves state-of-the-art performance in text-to-video, text-image-to-video, and image-to-video tasks.
Guanting Dong, Chenghao Zhang, Mengjie Deng +3 authors
AR-MCTS enhances multimodal large language models' reasoning capabilities through active retrieval, Monte Carlo Tree Search, and a process reward model, improving performance across multimodal reasoning tasks.
Egor Cherepanov, Nikita Kachaev, Artem Zholus +2 authors
The paper provides a framework for understanding and evaluating different types of memory in reinforcement learning agents, using cognitive science-inspired definitions and a standardized experimental methodology.
Yiheng Xu, Zekun Wang, Junli Wang +6 authors
Aguvis is a unified, vision-based framework for autonomous GUI agents that integrates language grounding and explicit planning, enabling cross-platform generalization and outperforming existing methods.
Yiwen Hu, Huatong Song, Jia Deng +8 authors
YuLan-Mini, a 2.42B parameter base model, achieves top-tier performance with efficient pre-training techniques, including a data pipeline with cleaning and scheduling, robust optimization, and annealing with targeted data selection.
Zeyi Sun, Ziyang Chu, Pan Zhang +6 authors
X-Prompt, an auto-regressive large-vision language model, uses in-context learning to achieve competitive performance in seen and unseen image generation tasks.
Jiuhai Chen, Jianwei Yang, Haiping Wu +4 authors
Florence-VL enhances multimodal language models with improved visual features from Florence-2, achieving state-of-the-art results across various benchmarks through depth-breath fusion and a novel training recipe.
Zicheng Lin, Tian Liang, Jiahao Xu +6 authors
The proposed cDPO approach identifies and rewards critical tokens to improve the reasoning accuracy of LLMs by utilizing contrastive estimation between positive and negative models.
Zhijian Liu, Ligeng Zhu, Baifeng Shi +24 authors
NVILA, a family of VLMs, optimizes efficiency and accuracy through a scale-then-compress approach, enhancing performance across various benchmarks while reducing computational costs.
Liang Chen, Zekun Wang, Shuhuai Ren +24 authors
A comprehensive taxonomy unifies multimodal understanding and generation through Next Token Prediction, covering aspects like tokenization, model architectures, task representation, datasets, and open challenges.
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