Search: “multimodal”

arXiv:2609.01316v1 Announce Type: cross Abstract: Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations th

arXiv:2609.01004v1 Announce Type: cross Abstract: Despite their strong multimodal understanding ability, multimodal large language models (MLLMs) incur substantial computational overhead when processi

arXiv:2609.00879v1 Announce Type: new Abstract: Reliable disaster damage assessment requires models that provide both accurate predictions and transparent explanations. However, existing multimodal ap

arXiv:2505.16990v3 Announce Type: replace Abstract: In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM). We observe that training with a purely discre

arXiv:2608.29802v1 Announce Type: new Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pres

arXiv:2608.29098v1 Announce Type: new Abstract: Multimodal safety moderation requires distinguishing risks arising from visual content, user intent, and assistant behavior. Existing safeguards, howeve

arXiv:2608.30705v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) struggle with fine-grained Visual Search, the task of locating small or rare objects in high-resolution images.

arXiv:2601.07576v2 Announce Type: replace-cross Abstract: Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance acr

arXiv:2608.26879v1 Announce Type: new Abstract: Fusing multiple modalities is expected to improve model performance. However, on the MultiHuSE dataset, early, late, and symmetric attention fusion ofte

arXiv:2608.27286v1 Announce Type: new Abstract: Inferring molecular structures from multimodal spectroscopic measurements requires integrating complementary yet highly heterogeneous signals. However,

arXiv:2608.26191v1 Announce Type: new Abstract: Incident risk prediction from longitudinal electronic health records (EHRs) is challenging because relevant signals are multimodal, weak in isolation, a

arXiv:2608.26095v1 Announce Type: new Abstract: In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from s

arXiv:2608.25412v1 Announce Type: new Abstract: Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embedd

arXiv:2511.19972v4 Announce Type: replace Abstract: Recently, Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach to incentivizing reasoning capability in Large

arXiv:2608.24947v1 Announce Type: new Abstract: End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade lear

arXiv:2602.19001v2 Announce Type: replace Abstract: As large language models increasingly power personal assistants, users expect them to reason over multimodal life histories, from recognizing people

arXiv:2608.24160v1 Announce Type: new Abstract: Multimodal understanding models that can jointly judge text-to-image (T2I), text-to-video (T2V) and text-to-speech (TTS) generation are increasingly use

What's better than an open-weight multimodal model release? Well, the technical report. I just love how these labs like Qwen and DeepSeek continue to drop gem after gem. Qwen3.8-Flash is the latest in

Z.ai: Z.ai releases GLM-5.3-Flash, the first natively multimodal GLM-5 series model, with 320B parameters, saying it outperforms GLM-5.2 at “one-tenth the price” — We introduce GLM-5.3-Flash, the firs

arXiv:2608.20743v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by allowing a lightweight drafter to propose future tokens while a target model verifies them

arXiv:2608.21290v1 Announce Type: cross Abstract: We propose VT-MUSE, a Multimodal Unified SEquential representation learning framework for visuotactilemanipulation. Existing approaches often encode v

arXiv:2608.19207v1 Announce Type: new Abstract: Production deployments of Multimodal Large Language Models (MLLMs) increasingly rely on system messages to govern model behavior. Yet existing benchmark

arXiv:2608.20019v1 Announce Type: new Abstract: Incomplete multimodal sentiment analysis has garnered significant attention in recent years. Existing approaches typically assume that data is missing a

arXiv:2602.12407v3 Announce Type: replace-cross Abstract: Background: Robot-assisted minimally invasive surgery (RMIS) research increasingly relies on multimodal data, yet access to proprietary robot

arXiv:2608.12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural setti

arXiv:2608.12398v1 Announce Type: cross Abstract: We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that

arXiv:2608.11343v1 Announce Type: new Abstract: Multimodal retrieval and classification across different types of media, spanning text, images,video and audio, has traditionally relied on dual-encoder

arXiv:2603.29418v2 Announce Type: replace-cross Abstract: Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior l

arXiv:2608.08915v1 Announce Type: new Abstract: Situated language use is multimodal and embodied. For example, gestures can carry information that is absent or underspecified in the speech signal, yet

arXiv:2608.08184v1 Announce Type: new Abstract: Large multimodal agents (LMAs) are increasingly proposed for intelligent transportation systems (ITS), but existing studies often conflate multimodality

arXiv:2507.19634v4 Announce Type: replace-cross Abstract: Recent advances in large language models have laid the foundation for multimodal LLMs (MLLMs), which unify text, speech, and vision within a s

arXiv:2608.06699v1 Announce Type: new Abstract: Agentic multimodal large language models (MLLMs) extend multimodal perception and reasoning with planning, tool use, and interaction in dynamic environm

👀 Seeing is just the beginning. With Qwen-MM-Plugins, turn your favorite agent harness multimodal-native — read images, videos & documents, edit videos, work with 3D/CAD, and more. From multimodal mod

arXiv:2608.05249v1 Announce Type: cross Abstract: Real-world multimodal instructions often bundle multiple requirements with unequal importance, yet most multimodal training data still reduce instruct

arXiv:2608.02711v1 Announce Type: new Abstract: Recent advances in image generation have demonstrated the potential of unified multimodal models that integrate understanding, generation, and editing.

arXiv:2608.01827v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced visual understanding and reasoning, yet their static parametric knowledge limits their ability to

arXiv:2608.01456v1 Announce Type: cross Abstract: Agents are increasingly expected to act not only as task executors, but also as decision-makers on behalf of human users. This shift requires agents t

arXiv:2606.15782v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response

arXiv:2608.02583v1 Announce Type: cross Abstract: Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retri

arXiv:2608.00986v1 Announce Type: new Abstract: Current advancements in Multimodal Anomaly Detection (MAD) are largely driven by enhancing multimodal fusion, particularly through the integration of RG

arXiv:2607.29545v1 Announce Type: new Abstract: Multimodal video generation aims to generate and edit videos conditioned on arbitrary combinations of text, images, and videos within a single model, al

arXiv:2508.16129v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm. Although se

arXiv:2607.26947v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cro

arXiv:2607.25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psy

arXiv:2607.23514v1 Announce Type: cross Abstract: Multimodal automated fact-checking (MAFC) verifies claims by retrieving and reasoning over external evidence. However, most existing static benchmarks

arXiv:2607.18080v2 Announce Type: replace-cross Abstract: Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associ