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Manuel Faysse, Hugues Sibille, Tony Wu +3 authors
The ColPali retrieval model uses Vision Language Models to create embeddings from images of document pages, improving performance and speed in visually rich document retrieval tasks.
Mihir Prabhudesai, Russell Mendonca, Zheyang Qin +2 authors
Utilizing pre-trained reward models to adapt video diffusion models with gradient-based feedback enhances efficiency and performance compared to gradient-free methods.
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy +1 authors
TabReD is a new collection of industry-grade tabular datasets that address temporal dynamics and feature engineering, revealing that simpler models outperform more complex deep learning architectures in these settings.
Xun Liang, Shichao Song, Zifan Zheng +6 authors
A theoretical framework called Internal Consistency and a Self-Feedback framework with Self-Evaluation and Self-Update modules are presented to address reasoning and hallucination in large language models by assessing coherence across model layers.
Jinghuan Shang, Karl Schmeckpeper, Brandon B. May +4 authors
Theia, a vision foundation model for robot learning, outperforms existing models with less data and smaller sizes by distilling knowledge from multiple vision foundation models.
Wenqi Zhang, Zhenglin Cheng, Yuanyu He +8 authors
A multi-modal self-instruct benchmark using large language models and code capabilities enhances understanding and reasoning for abstract images and visual tasks, improving performance metrics like chart understanding and map navigation.
Zihan Wang, Deli Chen, Damai Dai +3 authors
Research on parameter-efficient fine-tuning for Large Language Models with Mixture-of-Experts architecture reveals that task-specific expert tuning can enhance performance and efficiency.
Xuchen Pan, Dawei Gao, Yuexiang Xie +5 authors
Enhancements to the AgentScope platform improve scalability, efficiency, and ease of use for large-scale multi-agent simulations through distributed mechanisms, flexible environments, and user-friendly tools.
Qichen Fu, Minsik Cho, Thomas Merth +3 authors
LazyLLM dynamically selects important prompt tokens to accelerate the prefilling stage in transformer-based language models without sacrificing accuracy.
Boyuan Chen, Diego Marti Monso, Yilun Du +3 authors
Diffusion Forcing trains diffusion models to denoise tokens with independent noise levels, combining next-token prediction and full-sequence diffusion strengths for sequence generation and decision-making tasks.
Kaibing Chen, Dong Shen, Hanwen Zhong +14 authors
A multi-modal language model using cross-attention, hierarchical ViT features, and Mixture of Experts mechanism achieves competitive performance in image and video captioning tasks with reduced computational costs.
Zehui Chen, Kuikun Liu, Qiuchen Wang +4 authors
MindSearch, an LLM-based multi-agent framework, improves web information seeking and integration through parallel processing and hierarchical retrieval, achieving better performance than existing solutions.
Runpeng Yu, Weihao Yu, Xinchao Wang
A comprehensive comparison of KAN and MLP models across diverse tasks reveals that MLP generally outperforms KAN except in symbolic formula representation where KAN's B-spline activation function provides advantage, and KAN exhibits more severe forgetting issues in class-incremental continual learning.
Ting Jiang, Minghui Song, Zihan Zhang +6 authors
E5-V, a novel framework, utilizes multimodal large language models (MLLMs) to achieve universal multimodal embeddings with strong performance and reduced costs through single modality training.
Mo Li, Songyang Zhang, Yunxin Liu +1 authors
NeedleBench and the Ancestral Trace Challenge evaluate the long-context retrieval and reasoning capabilities of large language models, highlighting their limitations in practical applications.
Lirui Zhao, Tianshuo Yang, Wenqi Shao +5 authors
Diffree, a Text-to-Image model using Stable Diffusion with a mask prediction module, achieves the addition of text-guided objects while maintaining background consistency and spatial appropriateness.
Feng Li, Renrui Zhang, Hao Zhang +5 authors
LLaVA-NeXT-Interleave addresses multi-image, multi-frame, multi-view, and multi-patch scenarios in LMMs using the M4-Instruct dataset and LLaVA-Interleave Bench to achieve leading results while maintaining single-image performance.
Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi
SHIC leverages foundation computer vision models to learn canonical surface maps without manual supervision, achieving superior results by simulating the annotation process using image-to-image correspondences and enhanced template views.
Yunhao Fang, Ligeng Zhu, Yao Lu +6 authors
A novel data augmentation approach iteratively improves visual language model data quality and performance using self-augmentation and specialist-augmentation, leading to state-of-the-art results on MMMU tasks.
Guoli Yin, Haoping Bai, Shuang Ma +21 authors
MMAU benchmark evaluates large language models' capabilities across multiple domains and essential skills using comprehensive offline tasks to enhance interpretability and reliability.
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