Search: “multimodal”

arXiv:2608.27456v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the a

arXiv:2608.26382v1 Announce Type: new Abstract: Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human

arXiv:2510.20803v2 Announce Type: replace Abstract: We propose a novel AutoRegressive Generation-based paradigm for image Segmentation (ARGenSeg), achieving multimodal understanding and pixel-level pe

arXiv:2608.25023v1 Announce Type: cross Abstract: Multimodal product models can complete missing e-commerce attributes, yet current methods still optimize attribute-answer accuracy without verifying v

arXiv:2608.24931v1 Announce Type: cross Abstract: Multimodal neuroimaging combining structural MRI and positron emission tomography (PET) captures complementary structure-function relationships across

arXiv:2608.26094v1 Announce Type: new Abstract: Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics s

arXiv:2608.25467v1 Announce Type: new Abstract: Multimodal regression suffers from the mean-collapse pathology: under squared loss, an unconstrained regressor converges to the conditional mean, which

arXiv:2608.25251v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can appear reliable in-domain while failing when acquisition domain, paired supervision, or evaluation protocol ch

arXiv:2608.23874v1 Announce Type: cross Abstract: Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental m

arXiv:2608.23758v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely cons

GLM-5.3-Flash is live in Hermes Agent via Nous Portal https://portal.nousresearch.com Introducing GLM-5.3-Flash - Leading capabilities at a highly competitive price - Natively multimodal with a 1M-tok

Megathread for discussing the release of GLM-5.3-Flash. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comparisons We'll try

arXiv:2608.24574v1 Announce Type: new Abstract: Video multimodal large language models support language guided video segmentation, but they often show spatio temporal inconsistencies, e.g., jitter, dr

arXiv:2608.23978v1 Announce Type: new Abstract: Visual grounding is typically evaluated as a one-shot mapping from an informative referring expression to a visual target. This formulation misses a cen

arXiv:2608.23478v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cl

arXiv:2608.22975v1 Announce Type: new Abstract: Embodied multimodal agents must answer from growing observation streams under a fixed per-decision token budget. We formalize this constraint through fo

arXiv:2608.22963v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed as multi-step agents, where explicit reasoning supports task decomposition and tool c

arXiv:2608.22485v1 Announce Type: new Abstract: Current visual tokenizers in Multimodal Large Language Models (MLLMs) predominantly rely on patch-based partitioning, which causes severe semantic mixtu

arXiv:2608.23234v1 Announce Type: new Abstract: In this technical report, we present a training-free framework for audio-guided video object segmentation, which integrates Multimodal Large Language Mo

arXiv:2608.20769v1 Announce Type: cross Abstract: Streaming emotion understanding uses historical state while continuously interpreting current audio, often feeding the model's previous prediction bac

arXiv:2608.20868v1 Announce Type: cross Abstract: Document processing pipelines traditionally cascade optical character recognition (OCR) engines with downstream models for structured information extr

arXiv:2608.20402v1 Announce Type: cross Abstract: Biomedical knowledge graphs (KGs) are pivotal for knowledge organization, yet traditional binary relations often struggle to represent the conditional

arXiv:2602.07104v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have advanced the capabilities to interpret and act on visual input in 3D environments, empowering di

arXiv:2608.19726v1 Announce Type: new Abstract: The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between th

arXiv:2608.18410v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models process long multimodal token sequences, making inference expensive in both memory and computation. Existing efficie

arXiv:2608.18671v1 Announce Type: new Abstract: Egocentric video captures activities from the wearer's perspective, providing a direct view of human attention, hand--object interaction, and goal-direc

arXiv:2608.15869v1 Announce Type: cross Abstract: Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments. B

arXiv:2608.16536v1 Announce Type: cross Abstract: Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embedd

arXiv:2512.22983v2 Announce Type: replace Abstract: Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic mani

arXiv:2608.15520v1 Announce Type: new Abstract: A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost. With adaptive acquisition, the policy determ

arXiv:2608.16622v1 Announce Type: cross Abstract: Multimodal harmful meme detection is typically formulated as image--text harmfulness classification. A model may correctly predict harmfulness while m

arXiv:2608.15437v1 Announce Type: cross Abstract: Multimodal bird's-eye-view (BEV) perception combines LiDAR depth accuracy with dense camera semantics, but its high computational cost and imperfect s

arXiv:2509.24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensivenes

arXiv:2608.14924v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images wi

arXiv:2508.09105v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and conte

arXiv:2608.15736v1 Announce Type: new Abstract: Foundation models (FMs) increasingly support multimodal and geospatial reasoning, yet it remains unclear whether cartographic principles designed for hu

arXiv:2608.14718v1 Announce Type: cross Abstract: Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading model

arXiv:2608.15085v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) exhibit substantial performance degradation in non-English visual reasoning, despite the strong multilingual co

arXiv:2608.14309v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can generate pathological descriptions from histological images, but gigapixel Whole Slide Images (WSIs) exceed

arXiv:2601.08151v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visua

You can literally run Opus 4.6 Max-level intelligence on a MacBook Pro. Locally. We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With

arXiv:2608.12746v1 Announce Type: cross Abstract: Object hallucination in multimodal large language models arises when language priors and corpus co-occurrence bias outweigh the visual evidence, with

arXiv:2608.13441v1 Announce Type: new Abstract: Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientif

arXiv:2608.12611v1 Announce Type: new Abstract: Existing screenshot-to-code systems face a trade-off between flexibility and controllability. Direct multimodal generation can hallucinate visible detai

arXiv:2606.20077v2 Announce Type: replace-cross Abstract: Visual tokens enter Large Language Models (LLMs) as raw, foreign signals. How they are transformed into meaningful representations and interac

arXiv:2608.10567v1 Announce Type: new Abstract: Analytic dashboards combine coordinated views and interactions for data exploration and decision-making. Recent models can generate them from data and n