Knowledge catalogue
arXiv:2508.00955v3 Announce Type: replace-cross Abstract: Adapting generative Multimodal Large Language Models (MLLMs) into universal embedding models typically demands resource-intensive contrastive
arXiv:2608.02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing singl
arXiv:2607.16898v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) have recently demonstrated powerful instruction-based image editing capabilities, while also raising serious
arXiv:2607.09842v2 Announce Type: replace-cross Abstract: We investigate whether identity-specifying system prompts produce statistically distinguishable geometric fingerprints in the hidden-state tra
arXiv:2608.00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependenc
arXiv:2607.29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.
arXiv:2607.29000v1 Announce Type: cross Abstract: Multimodal information can improve the accuracy of click-through rate (CTR) prediction and effectively alleviate item cold-start and long-tail problem
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively under
arXiv:2607.28341v1 Announce Type: new Abstract: While visual token pruning is essential for efficient Multimodal Large Language Models (MLLMs), existing training-free methods suffer from a critical li
arXiv:2607.27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clien
arXiv:2607.28565v1 Announce Type: new Abstract: Medical image fusion aims to integrate complementary information from diverse imaging modalities to support clinical diagnosis. Existing methods typical
arXiv:2607.28269v1 Announce Type: new Abstract: The deployment of Vision-Language Models (VLMs) in critical domains like disaster management requires high-quality multimodal datasets, especially for t
arXiv:2511.12268v3 Announce Type: replace-cross Abstract: Early detection of oral cancer and potentially malignant diseases is a major challenge in low-resource settings due to the scarcity of annotat
arXiv:2512.21831v2 Announce Type: replace Abstract: Multiview cooperative perception and multimodal fusion are essential for reliable 3-D spatiotemporal understanding in autonomous driving, especially
arXiv:2607.25322v1 Announce Type: new Abstract: Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and
arXiv:2607.23944v1 Announce Type: new Abstract: Human visual reasoning typically follows a coarse-to-fine attention process, starting from global scene understanding and gradually focusing on question
arXiv:2607.23245v1 Announce Type: cross Abstract: Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representatio
arXiv:2507.05515v4 Announce Type: replace Abstract: Vision-language models (VLMs) are facing the challenges of understanding and following multimodal assembly instructions, particularly when fine-grai
arXiv:2607.24407v1 Announce Type: new Abstract: Multimodal Large Language Models have made great progress in grounding tasks, yet existing methods still struggle to unify precise localization and comp
arXiv:2607.22586v1 Announce Type: new Abstract: Key-Value (KV) caching is essential for efficient inference in multimodal large language models (MLLMs), yet its memory footprint grows linearly with co
arXiv:2607.23870v1 Announce Type: cross Abstract: Smart-city airspace is transforming Uncrewed Aerial Vehicles (UAVs) from passive sensing platforms into cyber-physical decision makers that must follo
arXiv:2607.22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control. In this field, deep learning approaches have become
arXiv:2607.23581v1 Announce Type: new Abstract: Multimodal misinformation verification is challenging because misleading signals may come from different parts of a post and require different forms of
arXiv:2607.20357v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost,
arXiv:2607.20284v1 Announce Type: new Abstract: The rapid development of multimodal large language models (MLLMs) has introduced a flexible paradigm for remote sensing image scene understanding (RSISU
arXiv:2607.19910v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs
arXiv:2607.13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turb
arXiv:2607.13712v1 Announce Type: cross Abstract: Despite the rapid progress of Multimodal Large Language Models (MLLMs), they still suffer from untruthfulness issues, such as visual hallucinations, c
arXiv:2607.09142v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clini
arXiv:2607.13466v1 Announce Type: new Abstract: Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. W
arXiv:2601.08010v3 Announce Type: replace Abstract: Vision-language models achieve strong performance across a wide range of multimodal understanding and reasoning tasks, yet their multi-step reasonin
arXiv:2602.05513v3 Announce Type: replace-cross Abstract: Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real-world tasks. To address the challenges of effe
arXiv:2607.12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remai
arXiv:2603.24787v2 Announce Type: replace Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine lightweight models
arXiv:2510.07328v2 Announce Type: replace-cross Abstract: Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimod
arXiv:2607.08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that a
arXiv:2607.06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX. TimesX contains a wide selection of high-quality real-world t
arXiv:2607.05943v1 Announce Type: new Abstract: Training multimodal search agents to perform multi-hop reasoning remains challenging due to a fundamental structural disconnect: existing pipelines cons
arXiv:2607.04472v1 Announce Type: new Abstract: The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classificatio
arXiv:2607.01813v1 Announce Type: cross Abstract: Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to
arXiv:2606.31367v1 Announce Type: cross Abstract: The proliferation of multimedia content on social platforms has fueled multimodal misinformation, where images are used to reinforce false claims. Con
arXiv:2606.31966v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded enviro
arXiv:2606.31383v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) typically employ resampling-based projectors to transform dense visual features into a compact token sequence f
arXiv:2606.31599v1 Announce Type: cross Abstract: Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medica
arXiv:2606.30189v1 Announce Type: new Abstract: Current multimodal fusion approaches, particularly those based on static Mixture-of-Experts (MoE) architectures, often struggle to provide the adaptive
arXiv:2603.13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multim
arXiv:2606.28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical app
arXiv:2606.30168v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often fail in fine-grained visual reasoning, as question-relevant visual cues are diluted by dense and redundan
arXiv:2606.12716v2 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-review workflows introduces novel and significant r
arXiv:2606.26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchma
arXiv:2606.26984v1 Announce Type: new Abstract: Unified multimodal models capable of both understanding and generation have achieved remarkable strides. However, despite their unified designs, existin
arXiv:2603.01195v2 Announce Type: replace-cross Abstract: The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely req
arXiv:2606.26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their c
arXiv:2604.03314v2 Announce Type: replace-cross Abstract: Foundation models have revolutionized AI, but adapting them efficiently for multimodal tasks, particularly in dual-stream architectures compos
arXiv:2606.25325v1 Announce Type: new Abstract: We find that current emotion-oriented Omni-MLLMs still lack reliable omni-modal perception: they (i) underutilize multimodal cues in their reasoning tra
arXiv:2606.24112v1 Announce Type: new Abstract: Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed proven
arXiv:2606.20738v1 Announce Type: new Abstract: This article presents a complementary approach for integrating multimodal medical data in cancer classification, based on state space models represented
arXiv:2606.23063v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instru
arXiv:2606.22889v1 Announce Type: new Abstract: Multimodal large language models (LLMs) are increasingly adopted to interpret 12-lead ECG images, though the interpretations often lack validation. Howe
arXiv:2606.22873v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed in consumer, medical, financial, and enterprise applications. This broad deployment expands the