Search: “multimodal”

PRISM-VLM: A Multi-Axis Discriminative Benchmark for Compact Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2609.27395v1 Announce Type: new Abstract: Compact vision-language models (VLMs) now power a growing share of multimodal applications. The benchmarks used to compare them, however, inherit a fron

24 Sept 2026

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Research

RelCheck: Dual-Evidence Spatial Grounding for VLM Hallucination Correction

DGX agent

arXiv:2609.27890v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) fre- quently generate text that is inconsistent with the input image. While object- and attribute-level hallu

researcharxiv-cs-ai

24 Sept 2026

SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference

arXiv:2609.28064v1 Announce Type: new Abstract: Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conf

safetyarxiv-cs-ai

24 Sept 2026

CableVLA: Simulation-Privileged Global-Local Representation Learning for Cable Routing

model-releasesarxiv-cs-ro

arXiv:2609.25606v1 Announce Type: new Abstract: Cable routing requires coordinated control of global cable topology and changing local contacts. We present CableVLA, an end-to-end multimodal vision-la

FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation

arXiv:2609.25085v1 Announce Type: cross Abstract: Data assimilation aims to infer the state of complex dynamical systems based on observational data. However, accurate inference of the multimodal post

researcharxiv-cs-lg

MedVLA: A Hierarchical Vision-Language-Action Framework for Closed-Loop Precision Medical Robot Manipulation

arXiv:2609.25756v1 Announce Type: new Abstract: Precision medical robotics demands adaptive decision-making under strict safety, interpretability, and execution constraints. Although recent Vision-Lan

safetyarxiv-cs-ro

OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials

arXiv:2603.17043v2 Announce Type: replace Abstract: Physics-aware multimodal large language models (MLLMs) can localize and characterize two-dimensional (2D) material flakes from optical microscopy im

Pretraining of Medical Visual Encoders Toward Multi-modal Large Language Models

arXiv:2609.23860v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) commonly reuse visual encoders pretrained with CLIP, although the features of these ViTs are ultimately consume

Bridge3D: Enabling Vision-Language-Action Models to See and Act in 3D

model-releasesarxiv-cs-ro

arXiv:2609.24525v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization in robotic manipulation via large-scale multimodal pretraining. However,

INTCORT: Training-Free Spatial Reasoning Enhancement for Vision-Language Models via Input Transformations and Confidence Routing

arXiv:2609.24813v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in multimodal tasks, yet they still exhibit poor ability in spatial reasoning. E

researcharxiv-cs-cv

Look Where It Counts: A Free, Label-Free Visual Evidence Signal for Fine-Grained Vision-Language Reasoning

arXiv:2609.24244v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) fail at fine-grained visual questions less because they cannot reason than because they never see the evidence:

Low resource cross-modal alignment using HGNN to enhance speech representation

arXiv:2609.23191v1 Announce Type: new Abstract: Speech-text space alignment is a multimodal representation learning method consisting to map different speech and text into a shared representation spac

safetyarxiv-cs-cl

ME-VLM:A Unified VLM for Embodied Cognition and Agent Coordination

arXiv:2609.24526v1 Announce Type: new Abstract: Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and

MinCU: A Fine-Grained Benchmark for Grounded Minimal-Change Understanding in Image Pairs

arXiv:2609.23336v1 Announce Type: new Abstract: Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large languag

Seeing Through Conflicts: Improving Instruction Hierarchy Alignment in Vision-Language Models

arXiv:2609.22234v1 Announce Type: new Abstract: Instruction hierarchy (IH) alignment teaches language models to prioritize higher-level instructions when inputs conflict. While studied primarily in te

safetyarxiv-cs-cl

Agents

Spatial Action Review: A Visual Analytics Dashboard for Auditing Language-to-Action Hand-offs in Electron Microscopy

arXiv:2609.24470v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly explored as interfaces for scientific image analysis, where a visual question-answering (VQA)

agentsarxiv-cs-cv

SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models

arXiv:2609.24875v1 Announce Type: new Abstract: Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation mod

A Sim-to-Real Integration Pipeline for Training and Deployment of Chunk-Based VLA Manipulation Policies

arXiv:2609.21817v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations

safetyarxiv-cs-ro

Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models

arXiv:2609.21363v1 Announce Type: new Abstract: Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces

safetyarxiv-cs-cv

Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies

arXiv:2609.21220v1 Announce Type: new Abstract: Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision

safetyarxiv-cs-ro

Samsone: A Family of Open Small Audio Language Models for On-Device Inference

arXiv:2609.21666v1 Announce Type: cross Abstract: The success of Large Audio Language Models has driven the development of massive multimodal networks exceeding billions of parameters. However, the de

Agents

SkillIR: Evolving Scene-Aware Skills for Agentic Image Restoration

arXiv:2609.21468v1 Announce Type: new Abstract: This paper studies agentic image restoration, in which multimodal agents coordinate specialized restoration tools to recover images affected by complex

agentsarxiv-cs-cv

XCalib Depth-Guided Geometric Optimization for Dense Thermal-Visible Video Registration

arXiv:2609.21770v1 Announce Type: new Abstract: Image registration is a vital preprocessing step in multimodal perception tasks, including image fusion, object detection, and semantic segmentation. In

safetyarxiv-cs-cv

A Multi-Modal Generative Model for Tomato Disease Leaves Understanding

arXiv:2609.19555v1 Announce Type: cross Abstract: Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting v

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

model-releasesarxiv-cs-cl

arXiv:2609.19969v1 Announce Type: new Abstract: The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost

FOCAL: Fine-Grained Optimal-Transport-Driven Contrastive Alignment of Language and ECGs with Waveform Enhancement

arXiv:2505.11939v3 Announce Type: replace-cross Abstract: Electrocardiograms (ECGs) are essential non-invasive tools for diagnosing cardiovascular diseases. While recent multimodal ECG-Report contrast

QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning

arXiv:2609.19990v1 Announce Type: new Abstract: The high visual-token load in multimodal large language models (MLLMs) motivates training-free pruning to reduce later-layer computation, but under a fi

Understanding and Exploiting Diagonal Attention Sparsity in Autoregressive Image Generation

arXiv:2609.19702v1 Announce Type: new Abstract: Autoregressive image generation has emerged as a paradigm for multimodal AI systems due to its compatibility with transformer-based LLM serving infrastr

Hardware

CapMap-MS-TTA: 3rd Place Solution for the MUMU Track of the 8th LSVOS Challenge at ECCV 2026

arXiv:2609.18206v1 Announce Type: cross Abstract: The MUMU track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge requires a single unified multimodal model to jointly solve image ta

hardwarearxiv-cs-ai

Lumen: Parameter-Efficient Alignment of Pretrained Vision and Language Encoders for Zero-Shot Computational Pathology

arXiv:2609.17868v1 Announce Type: new Abstract: Pathology vision-language models are commonly built by pretraining or fine-tuning large encoders on paired image-caption data. We asked whether a pathol

MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education

arXiv:2609.19088v1 Announce Type: new Abstract: Large vision-language models have achieved remarkable progress in multi-modal understanding, yet their capabilities in educational settings remain insuf

Rethinking Domain Specialization for Open-Ended Scientific Reasoning in Astronomy Language Models

arXiv:2609.17644v1 Announce Type: cross Abstract: Domain-specialized language models are widely used for scientific question answering, but stronger general-purpose systems raise a sharper question: w

Audio-Visual Turn-taking Prediction in Cocktail Party Scenarios

arXiv:2609.17056v1 Announce Type: cross Abstract: Current predictive turn-taking models (PTTMs) achieve strong performance on benchmarks with controlled acoustic conditions and clean audio signals. Th

researcharxiv-cs-cl

CTAN: Cycle-Temporal Attention Network for Embodied Audio-Visual Navigation

arXiv:2609.17420v1 Announce Type: cross Abstract: Audio-visual embodied navigation equips robots with the capability to infer the locations of sound sources by integrating visual inputs and acoustic i

From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contracts

arXiv:2609.17326v1 Announce Type: new Abstract: Scientific poster generation distills a multimodal paper into a single-page visual artifact, forcing strict trade-offs between informational coverage an

Applications

Multi-Label Proportion Learning for Sea-Ice Type Prediction

arXiv:2609.16347v1 Announce Type: new Abstract: Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data fo

applicationsarxiv-cs-lg

StackTok: Accelerating VLMs Inference with Budget-Adaptive Visual Token Selection

arXiv:2609.16841v1 Announce Type: cross Abstract: Increasing image resolution produces ever-longer visual-token sequences in vision-language models (VLMs), substantially raising their inference cost.

SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS

arXiv:2512.05126v2 Announce Type: replace-cross Abstract: Automatic video dubbing aims to generate high-fidelity speech that is temporally aligned with visual content. However, existing methods still

Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?

arXiv:2609.17248v1 Announce Type: new Abstract: Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked

Agents

ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence

arXiv:2609.14824v1 Announce Type: new Abstract: The emergence of large language models (LLMs) and large multimodal models (LMMs) has enabled a new class of agentic systems capable of integrating natur

agentsarxiv-cs-ai

BLInD: Learning Driver Intent as a Distribution over Future Ego Trajectories

arXiv:2609.13941v1 Announce Type: new Abstract: We present BLInD (Blind Learned Intent Distribution), a compact network that maps recent vehicle-state history (e.g. speed, curvature, indicator, and ve

safetyarxiv-cs-cv

Circuit-MLLM: Topological Logic-Guided Latent-Space Visual Reasoning for Circuit Schematic Understanding

arXiv:2609.15668v1 Announce Type: cross Abstract: Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tas

Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation

arXiv:2609.15334v1 Announce Type: cross Abstract: Medical imaging modalities such as ultrasound and X-ray are widely used in clinical practice, where diagnosis follows a structured, evidence-driven wo

Evaluation of MLLM-Agnostic Plug-and-Play Keyframe Selection Methods for Long Video Understanding

arXiv:2609.13250v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) cannot process every frame of a long video because of limitations in visual-token and computational budgets.

Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation

arXiv:2609.15094v1 Announce Type: cross Abstract: In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user popu

safetyarxiv-cs-ai

LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents

arXiv:2609.13287v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) achieve high decoding efficiency through block-parallel, arbitrary-order generation, making them attractive fo

MarKey: Marginal Utility Guided Greedy Keyframe Selection for Long Video Understanding

arXiv:2609.15408v1 Announce Type: cross Abstract: Long-video understanding remains challenging for multimodal large language models (MLLMs) because densely encoding long frame sequences is computation

researcharxiv-cs-cl

Physically Typed and Geometry-Aware Representations for Earth Foundation Models

arXiv:2609.13868v1 Announce Type: new Abstract: Earth-observation (EO) foundation models have become exceptionally effective at learning se mantic, high-dimensional geospatial embeddings, while modern