Knowledge catalogue
arXiv:2607.00125v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot im
arXiv:2606.31903v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation. Existing acce
arXiv:2601.21173v2 Announce Type: replace-cross Abstract: With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in com
arXiv:2603.19127v2 Announce Type: replace Abstract: As Spoken Language Models (SLMs) integrate speech and text modalities, they inherit the safety vulnerabilities of their LLM backbone while introduci
arXiv:2606.31504v1 Announce Type: new Abstract: We present SimpleSearch-VL, an efficient, reliable, and practical framework for multimodal agentic search. Its core idea is to improve the agent's own s
arXiv:2606.31451v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing res
arXiv:2606.28406v1 Announce Type: cross Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design sch
arXiv:2606.29805v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are prone to hallucination as their generation preferences are insufficiently calibrated to visual evidence, ca
arXiv:2606.28514v1 Announce Type: new Abstract: Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents. Existing benchmarks show that these m
arXiv:2606.29788v1 Announce Type: new Abstract: When a multimodal AI agent is asked to forget a fact, current memory systems usually delete the text entry and report success. We find that the fact can
arXiv:2403.06728v2 Announce Type: replace Abstract: Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. The performance o
arXiv:2606.29573v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly expected to generate fine-grained descriptions of visual content. However, we observe and theo
arXiv:2510.12784v2 Announce Type: replace-cross Abstract: Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding
arXiv:2606.14752v2 Announce Type: replace-cross Abstract: Modern Vision-Language-Action (VLA) models must bridge pretrained vision-language reasoning and precise continuous robot control. Existing act
arXiv:2606.28049v1 Announce Type: new Abstract: In recent years, multimodal large language models (MLLMs) have shown strong potential for embodied intelligence, yet their ability to maintain geometric
arXiv:2606.16494v2 Announce Type: replace-cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by con
arXiv:2606.27652v1 Announce Type: new Abstract: We find that explicit reasoning does not necessarily translate into better multimodal emotion recognition (MER) accuracy, even though it makes predictio
arXiv:2602.01588v3 Announce Type: replace-cross Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals
arXiv:2511.20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods
arXiv:2606.27376v1 Announce Type: new Abstract: Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision,
arXiv:2606.27373v1 Announce Type: new Abstract: Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However,
arXiv:2509.07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture. However, conventional training relies
arXiv:2606.26654v1 Announce Type: new Abstract: Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogu
arXiv:2606.18974v2 Announce Type: replace Abstract: Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an
We stress tested many frontier AI models for multimodal medical reasoning (including GPT-5, Claude 3.5, Gemini 2.5 Pro). They’re not ready. Faulty reasoning, use of inappropriate shortcuts, hallucinat
arXiv:2606.26473v1 Announce Type: new Abstract: Many multimodal systems estimate the reliability of each modality and weight their contributions to the final prediction. However, it remains unclear wh
arXiv:2606.25375v1 Announce Type: new Abstract: With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prio
arXiv:2606.26079v1 Announce Type: new Abstract: Standard benchmarks for multimodal large language models (MLLMs) score each item on one canonical ordering and miss whether order-irrelevant shuffling c
arXiv:2606.24233v1 Announce Type: new Abstract: The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relie
arXiv:2606.24187v1 Announce Type: new Abstract: Long video understanding remains a daunting challenge for Multimodal Large Language Models (MLLMs) due to the excessive computation and memory footprint
arXiv:2606.22699v1 Announce Type: new Abstract: Frontier multimodal models can guess whether a person is lying from a testimony video. To do so, they stream that raw face and voice to a third-party mo
arXiv:2606.23206v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal reasoning. However, prevailing reinforcement learning (RL)
arXiv:2603.14837v2 Announce Type: replace Abstract: Analyzing street-view imagery with computer vision models offers a promising approach for rapid, hyperlocal disaster damage assessment, but existing
arXiv:2606.21337v1 Announce Type: new Abstract: Massive unstructured multimodal streams suffer from high 'data entropy,' impeding both efficient human knowledge acquisition and high-quality AI post-tr
arXiv:2606.23593v1 Announce Type: cross Abstract: Robot-assisted minimally invasive surgery improves surgical precision but introduces complexity, making technical error detection essential for ensuri
arXiv:2606.12303v1 Announce Type: new Abstract: Multimodal image fusion aims to integrate complementary information from different modalities into a fused image that preserves rich local details while
arXiv:2606.10488v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods provide a streamlined and efficient tool for adapting large models to domain-specific multimodal downstre
arXiv:2509.05913v3 Announce Type: replace Abstract: Musculoskeletal disorders pose significant risks to athletes, and early risk assessment is essential for prevention. However, most existing methods
arXiv:2606.10147v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer?
arXiv:2606.10194v1 Announce Type: cross Abstract: Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate
arXiv:2606.11107v1 Announce Type: cross Abstract: Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT s
arXiv:2606.10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for s
arXiv:2606.07801v1 Announce Type: new Abstract: Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the c
arXiv:2606.09169v1 Announce Type: new Abstract: In recent years, unified multimodal models (UMMs) have emerged to support both understanding and generation within a single framework. Mastering dynamic
arXiv:2606.08511v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computational cost of self-attention over long vis
arXiv:2606.07585v1 Announce Type: cross Abstract: This thesis addresses group emotion recognition (GER) in-the-wild with a focus on privacy preservation. Unlike traditional emotion recognition methods
arXiv:2606.07541v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong performance on objective tasks such as video understanding and reasoning. However, it remai
arXiv:2606.07689v1 Announce Type: new Abstract: Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with r
arXiv:2606.08464v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has proven effective for enhancing problem-solving in large language models. However, when applied to multimodal LLMs (
arXiv:2606.07872v1 Announce Type: new Abstract: When a multimodal large language model answers a visual reasoning question correctly, is the prediction actually supported by the task-critical visual e
arXiv:2601.22574v2 Announce Type: replace-cross Abstract: Although Video Large Multimodal Models have achieved strong performance in video understanding, they still suffer from hallucination. Existing
arXiv:2604.05360v2 Announce Type: replace-cross Abstract: Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must
arXiv:2606.06875v1 Announce Type: new Abstract: Diffusion transformers (DiTs) equipped with multimodal attention (MM-Attn) have become a dominant paradigm for image generation. However, preventing the
arXiv:2508.15851v2 Announce Type: replace Abstract: Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information
arXiv:2606.05874v1 Announce Type: new Abstract: Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-n
arXiv:2606.05833v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to m
arXiv:2606.05997v1 Announce Type: new Abstract: We present the AILS-NTUA submission to the EXIST 2026 Lab at CLEF, addressing multimodal sexism identification and characterization in memes (Task 2) an
arXiv:2606.05931v1 Announce Type: new Abstract: When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curate
Nemotron 3.5 Content Safety is NVIDIA's multimodal safety solution designed for enterprise AI applications, offering customizable safeguards for both text and image inputs across different global cont
Carl Franzen / VentureBeat: Alibaba releases Qwen3.7-Plus, a multimodal proprietary model with a 1M-token context window, costing $2 per 1M tokens, 60% less than text-only Qwen3.7-Max — However, like