Weekly Papers

Imagine yourself: Tuning-Free Personalized Image Generation 想象你自己：无调优个性化图像生成 扩散模型在多种图像生成任务中表现出色。本研究中

InstantDrag: Improving Interactivity in Drag-based Image Editing InstantDrag: 提升基于拖拽的图像编辑中的交互性 基于拖拽的

How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data 你的代码大语言模型表现

SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding SciLitLLM: 如何适应大语言模型以理解科学文献 科学文

Building and better understanding vision-language models: insights and future directions 构建与深化理解视觉-语

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models xGen-MM (BLIP-3): 一个开放的大型多模态模型家族 本报告介绍了 x

VITA: Towards Open-Source Interactive Omni Multimodal LLM VITA：迈向开源交互式全模态大语言模型 GPT-4o 的卓越多模态能力和交互体验，

Medical SAM 2: Segment medical images as video via Segment Anything Model 2 Medical SAM 2: 利用 Segmen

每周AI论文速递系列已经创建GitHub仓库，欢迎star. SHIC: Shape-Image Correspondences with no Keypoint Supervision SHIC:

Internal Consistency and Self-Feedback in Large Language Models: A Survey 大语言模型中的内部一致性与自反馈：一项调查 大语言模

SpreadsheetLLM: Encoding Spreadsheets for Large Language Models SpreadsheetLLM：优化大语言模型处理电子表格的能力 电子表格

Unveiling Encoder-Free Vision-Language Models 无编码器视觉-语言模型的探索 当前的视觉-语言模型（VLMs）主要依赖视觉编码器提取视觉特征，并结合大语言模

Scaling Synthetic Data Creation with 1,000,000,000 Personas 利用 10 亿角色扩展合成数据创建 我们提出了一种新颖的以角色驱动的数据合成方法

LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs LongRAG: 使用长上下文大语言模型增强检索增强生

XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning XLand-100B: 一个大规模

Mixture-of-Agents Enhances Large Language Model Capabilities Mixture-of-Agents 增强大语言模型能力 最近在大语言模型 (L

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Du

Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models Meteor: 基于 Mamba 的大型

PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning PLLaVA：图像至视

AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation AutoCrawler: 一个为 Web 爬