Search: “multimodal”

RUTA: Principled Visual Token Allocation via Rate-Utility Optimization

arXiv:2608.04132v1 Announce Type: new Abstract: High-resolution images and long videos provide vision-language models with rich context for multimodal reasoning and fine-grained perception, but the re

6 Aug 2026

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SEAR: Simple and Efficient Adaptation of Visual Geometric Transformers for Unpaired RGB+Thermal 3D Reconstruction

DGX agent

arXiv:2603.18774v2 Announce Type: replace Abstract: Foundational feed-forward visual geometry models enable accurate and efficient camera pose estimation and scene reconstruction by learning strong sc

model-releasesarxiv-cs-cv

6 Aug 2026

The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering

model-releasesarxiv-cs-ai

arXiv:2608.04589v1 Announce Type: cross Abstract: EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize

6 Aug 2026

Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding

arXiv:2607.11844v2 Announce Type: replace Abstract: Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos inv

ChartAnno: Evaluating MLLMs for Chart Annotation Generation

model-releasesarxiv-cs-ai

arXiv:2608.03464v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate e

Local Ai

CIGTSurv: Clinical Information Guided Tri-modal Survival Prediction with Local Prototype Association and Global Feature Alignment

arXiv:2608.03247v1 Announce Type: cross Abstract: Multimodal learning has significantly advanced survival prediction by integrating pathology images with genomic data. However, clinical information, d

local-aiarxiv-cs-cl

DocTrace: Towards Traceable Long Document VQA via Hierarchical Evidence Graph Reasoning

arXiv:2608.03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneo

safetyarxiv-cs-ai

HERO: Hierarchical Evidential Reasoning Optimization for Radiology Report Generation via Reason-then-Summarize

arXiv:2601.03321v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have substantially advanced Radiology Report Generation (RRG), yet aligning them through reinforcemen

safetyarxiv-cs-ai

Learning Molecular Representations from Cellular Phenotypes with Structure Preservation

arXiv:2608.02688v1 Announce Type: cross Abstract: Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing mul

safetyarxiv-cs-ai

Qwen-3D: A Generalist 3D Vision-Language Model for Spatial Understanding

arXiv:2608.02980v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable success on images and short videos, yet scaling them to long videos remains challenging due to f

UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space

arXiv:2608.03817v1 Announce Type: cross Abstract: Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions

researcharxiv-cs-ai

A Benchmark Dataset for MLLM-Generated Image Detection: GPT Image2 & Nano Banana2

arXiv:2608.01258v1 Announce Type: new Abstract: The realism of images generated by multimodal large language models (MLLMs), such as GPT Image2 and Nano Banana2, has improved rapidly in recent years.

ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality

arXiv:2603.25538v3 Announce Type: replace Abstract: Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimizat

safetyarxiv-cs-lg

Attention-Steered Vision-Language Models for Sign Language Translation

arXiv:2608.00235v1 Announce Type: new Abstract: Vision-language models (VLMs) have emerged as a powerful framework for multimodal video understanding. However, they remain limited in the sign language

Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference

arXiv:2608.00077v1 Announce Type: new Abstract: Visual-token pruning is usually judged by answer quality at a fixed retention budget. For text-rich multimodal large language models (MLLMs), this proto

CAPEval: A Decoupled Caption Evaluation across Understanding and Generation

arXiv:2608.02589v1 Announce Type: new Abstract: Captions serve as a primary supervision signal for both multimodal understanding and text-to-image generation. However, previous evaluations treat the c

Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception

arXiv:2608.01055v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-obj

CultureVidBench: Benchmarking Cultural Understanding in Text-to-Video Generation

model-releasesarxiv-cs-cl

arXiv:2608.01942v1 Announce Type: cross Abstract: Text-to-video (T2V) generation models have advanced rapidly, yet their ability to represent diverse cultural contexts remains underexplored. Existing

DiffuseAgent-MI: Distributionally-Grounded,Tool-Integrated Self-Evolving Agents for Faithful Visual Reasoning

arXiv:2608.00540v1 Announce Type: new Abstract: Tool-integrated vision-language agents have made remarkable progress on compositional and multi-step visual reasoning. Yet their outputs frequently exhi

DrawAI: Agentic Benchmark and Workflow for Making Raster Images Editable

arXiv:2608.00548v1 Announce Type: new Abstract: Recent image-generation models and multimodal agents can produce high-quality visuals for increasingly complex visual communication tasks. Yet their ras

Local Ai

Dynamic Resolution Routing for Efficient Egocentric Grounding

arXiv:2608.01638v1 Announce Type: new Abstract: Egocentric visual grounding requires high-resolution inputs to localize small objects. However, scaling Multimodal Large Language Models to this domain

local-aiarxiv-cs-cv

Tutorials

Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency

arXiv:2608.01730v1 Announce Type: new Abstract: No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one

tutorialsarxiv-cs-cv

LUT: Latent Utility Training for Visual Reasoning

arXiv:2608.00743v1 Announce Type: new Abstract: Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reason

Mitigating Visual Degradation in MLLMs via Spatial-Spectral Visual Anchor Learning

arXiv:2608.01635v1 Announce Type: new Abstract: Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual instructi

Quick on the Uptake: Eliciting Implicit Intents from Human Demonstrations for Personalized Mobile-Use Agents

arXiv:2508.08645v3 Announce Type: replace Abstract: As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use a

safetyarxiv-cs-cl

Applications

ReMiX-MAE: Learning Missing-Channel Cross-Modal Representations from RGB-Only Clinical Facial Videos for Sympathetic-Mediated Pain Assessment

arXiv:2608.02561v1 Announce Type: new Abstract: Automated pain assessment in real clinics is limited by scarce clinically grounded facial video data with weak labels (often sequence-level self-report)

applicationsarxiv-cs-cv

Slot2Text: Object-Centric Visual Tokenization for Efficient and Spatially Traceable Surgical MLLMs

arXiv:2608.01473v1 Announce Type: cross Abstract: Multimodal large language models (MLLM) for surgical scene understanding typically inject hundreds of dense visual tokens into a language model, leadi

safetyarxiv-cs-cl

Two Sides of the Same Coin: Co-Evolving Search for Cross-Task Attacks on Vision-Language Models

arXiv:2608.02137v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit strong generalization across multimodal tasks but remain vulnerable to adversarial perturbations. Existing attacks

UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction

arXiv:2608.01298v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks.

Tutorials

Unleashing the Power of Text: Text-Guided Flow Matching for Image Fusion under Complex Degradations

arXiv:2608.00530v1 Announce Type: new Abstract: Infrared-visible image fusion under realistic degradation scenarios is a challenging task, as degradations not only cause a loss of reliable modality-sp

tutorialsarxiv-cs-cv

Agents

VC-Tooler: Learning Compositional and Adaptive Visual Tool Use

arXiv:2608.02217v1 Announce Type: new Abstract: Agentic multimodal reasoning extends passive image understanding by allowing VLMs to actively acquire and refine visual evidence through visual tool int

agentsarxiv-cs-cv

What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs

model-releasesarxiv-cs-cl

arXiv:2608.00013v1 Announce Type: new Abstract: Choosing the right large language model (LLM) backbone is the most consequential decision when building a vision-language model (VLM), yet it remains fu

Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering

model-releasesarxiv-cs-lg

arXiv:2607.29675v1 Announce Type: cross Abstract: Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy cons

Evidence-Grounded Constraint Checking in Construction Documents

arXiv:2607.29058v1 Announce Type: new Abstract: Professional-document review is a constraint-checking problem in which decisions depend on relations among text, geometry, pages, and document revisions

researcharxiv-cs-ai

Applications

FibVLA: An Efficient Temporal Vision-Language-Action Model with Fibonacci Sampling

arXiv:2607.29596v1 Announce Type: cross Abstract: Vision-language-action models (VLAs), which leverage the cognition of multimodal information to infer physical-world actions, provide a generalized so

applicationsarxiv-cs-cv

Is It Time for the Renaissance of Salient Object Detection in the Era of MLLMs?

arXiv:2607.29222v1 Announce Type: new Abstract: The zero-shot capabilities of multimodal large language models (MLLMs) are pushing salient object detection (SOD) beyond task-specific supervision. To d

SciFigPlag-Bench: A Benchmark for Provenance-Aware Scientific Figure Plagiarism Detection

arXiv:2607.29124v1 Announce Type: new Abstract: Scientific figures often encode the visual evidence behind scientific findings, yet figure plagiarism remains underexplored as a benchmarked multimodal

SeekBrain: An Autonomous Multi-Agent System for Accelerating Neuroscience Discovery

model-releasesarxiv-cs-ai

arXiv:2607.29347v1 Announce Type: cross Abstract: Modern neuroscience relies on integrating multi-scale, multimodal datasets to uncover the neural principles underlying intelligence. However, analytic

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

arXiv:2607.28687v1 Announce Type: cross Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routi

researcharxiv-cs-ai

Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers

arXiv:2607.28611v1 Announce Type: new Abstract: Visual generation increasingly requires high-resolution images, long videos, and multimodal context, making the quadratic cost of full attention prohibi

Divergence Decoding: Training-Free Capability Fusion

model-releasesarxiv-cs-lg

arXiv:2607.27248v1 Announce Type: cross Abstract: While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains. Conversely, domain models~(

LoMeVQA: A Comprehensive Benchmark for Longitudinal Medical VQA

arXiv:2607.27806v1 Announce Type: new Abstract: In clinical practice, patients often undergo multiple imaging examinations over successive visits, yielding longitudinal data. Modeling such temporal in

PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

arXiv:2607.27378v1 Announce Type: new Abstract: Accurate evaluation of multimodal large language models (MLLMs) in dental panoramic radiography (orthopantomogram, OPG) is limited by the lack of fine-g

Scaling Vision-Language Models Is Not Enough to Mitigate Bias

arXiv:2607.28211v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly unde

SCALPEL: Semantic Cross-modal Alignment via LLM-Powered Encoder Learning for Medical Vision-Language Representation

arXiv:2607.26885v1 Announce Type: new Abstract: Vision-language pre-training (VLP) serves as a cornerstone for medical multimodal representation learning. However, existing medical VLP frameworks are

SMSP: A Plug-and-Play Strategy of Multi-Scale Perception for MLLMs to Perceive Visual Illusions

arXiv:2603.23118v2 Announce Type: replace Abstract: Recent works have shown that multimodal large language models (MLLMs) are highly vulnerable to hidden-pattern visual illusions, where the hidden con

SpatialQ: Understanding 3D Gaussian Splatting Scene Quality via Visual-based MLLM

arXiv:2607.26595v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as an effective representation for novel view synthesis and 3D scene reconstruction, creating an increasing dem

Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation

model-releasesarxiv-cs-cl

arXiv:2607.26555v1 Announce Type: new Abstract: Multimodal fake news detectors often generalize poorly across domains because they learn to trust unreliable evidence: domain-specific shortcuts amplifi