提出注意力动态对齐与偏好微调方法，从内部注意力演化破解多模态幻觉难题
arXiv:2606.31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating conten…
最新论文提出通过logit空间对比对齐实现跨模态生物语言模型，提升序列设计与变异评分效果。
arXiv:2606.18703v1 Announce Type: new Abstract: Pretrained biological language models expose per-token probability distributions through masked-token …
多模态大模型统一检测并解释AI生成内容，跨模态鉴别新范式。
arXiv:2507.14632v4 Announce Type: replace Abstract: The rapid advancement of generative AI has substantially improved image and video synthesis, ampli…
统一多模态潜在扩散模型实现任意模态间的连贯生成，无需配对数据，跨模态生成能力惊艳
arXiv:2606.16408v1 Announce Type: new Abstract: We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that …
多模态先验注入表示空间去噪，RepFusion实现更鲁棒的跨模态表征融合。
arXiv:2606.14700v1 Announce Type: new Abstract: Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically li…
首次提出将酉算子映射到LLM可理解的形式，探索AI理解量子世界的新路径
arXiv:2606.13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators? Despite their remark…
医学影像AI幻觉问题首部跨模态系统化框架，在监管约束下分类、检测与缓解，临床可靠性关键突破。
arXiv:2606.13211v1 Announce Type: new Abstract: AI systems are being deployed across medical imaging faster than their failure modes are understood. A…
发现多数视觉指令样本可通过语言模式解决，提出无需训练的数据选择方法提升跨模态学习
arXiv:2603.09715v2 Announce Type: replace Abstract: Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, …
ViT也能开口说话？全新生成式语言-图像预训练框架，让视觉与语言深度融合！
arXiv:2605.00809v2 Announce Type: replace Abstract: In this paper, we present \textbf{Gen}erative \textbf{L}anguage-\textbf{I}mage \textbf{P}re-traini…
突破传统结果优化，动态跨模态协调强化学习提升视觉推理性能
arXiv:2606.08035v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing …
用Mamba替代Transformer，结合查询式跨模态投影器，大幅降低多模态大模型的计算复杂度
arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load o…
新型混合模态双人脸发型检索方法，同时利用文本和图像查询实现精准匹配，探索多模态检索的前沿技术。
arXiv:2606.03470v1 Announce Type: new Abstract: We introduce Dual Face-Hair Retrieval (DFHR), a new mixed-modality dual-reference task in image retrie…
首个多模态领域泛化基准，覆盖多种跨模态场景，助力模型稳健性评估。
arXiv:2606.00891v1 Announce Type: new Abstract: Multi-modal Domain Generalization (MMDG) seeks to leverage complementary modalities to enhance model r…
被ICML 2026接收的ObjEmbed方法，提出通用多模态对象嵌入，为跨模态检索与理解提供新范式。
arXiv:2602.01753v3 Announce Type: replace Abstract: Aligning objects with corresponding textual descriptions is a fundamental challenge and a realisti…
生物多模态大模型合并新方法，利用嵌入空间信号实现跨模态融合，推动科学发现。
arXiv:2603.14405v2 Announce Type: replace Abstract: Biological multimodal large language models (MLLMs) have emerged as powerful foundation models for…
最新研究发现，现代大语言模型与人类脑电图在情感效价轴上存在共享的饱和度规律，揭示AI与大脑情感表征的深层统一性。
arXiv:2606.00129v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features …
直达前沿AI医学研究：免费获取跨模态风险分析论文，洞见临床视觉语言模型安全
arXiv:2606.02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shar…
顶会ICML 2026提出多模态融合新方法，利用自一致性任务梯度场实现高效跨模态交互。
arXiv:2410.15475v2 Announce Type: replace Abstract: Multimodal learning aims to preserve as much task-related information as possible from different i…
从脑电信号直接回答视觉问题，这项研究打通了大脑与AI问答的桥梁，未来感十足
arXiv:2605.29588v1 Announce Type: cross Abstract: Decoding visual content from fMRI signals recorded while a person views images, and specifically ans…