Knowledge catalogue
arXiv:2607.13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems
arXiv:2607.14021v1 Announce Type: new Abstract: Dexterous manipulation remains a critical bottleneck in industrial automation; tasks such as cable routing, connector insertion, and precision assembly
arXiv:2601.12349v3 Announce Type: replace-cross Abstract: Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted to perceive screen content a
arXiv:2607.12091v1 Announce Type: new Abstract: Affective Behaviour Analysis aims to enable machines to infer human affective states from behavioural signals, particularly facial expressions, in real-
arXiv:2607.12732v1 Announce Type: new Abstract: Robot motion planning is inherently multimodal, yet classical planners typically return only a single solution. Probabilistic formulations address this
arXiv:2607.12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability. Recent approaches ba
Introduction RxBrain (Hy-Embodied-RxBrain-1.0) is a unified multimodal foundation model for embodied cognition — a single model that couples language reasoning with visual imagination to deliver three
arXiv:2607.12678v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) floor plan drawings contain both graphical primitives and textual annotations, which provide complementary geometric and s
The best base model for training isn't about someone else's benchmarks. Excited to offer @thinkymachines' first open-weights model: Inkling. 975B MoE, multimodal, with Apache 2.0. A great foundation f
arXiv:2607.12079v1 Announce Type: new Abstract: Decoding continuous language from fMRI signals remains a core challenge in non-invasive brain-computer interface research. We present two complementary
Grok 4.5 is an AI language model developed by xAI, featuring improvements in reasoning, coding, and multimodal capabilities compared to earlier versions. The model demonstrates enhanced performance ac
arXiv:2607.08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as sc
arXiv:2606.12886v2 Announce Type: replace-cross Abstract: Interleaved thinking, where a unified multimodal model alternates between textual reasoning and visual generation, has shown promise on spatia
arXiv:2607.08182v1 Announce Type: cross Abstract: Vision-language-action (VLA) models aim to map multimodal inputs to robot actions. However, most existing approaches struggle to cover complex dynamic
arXiv:2607.08072v1 Announce Type: new Abstract: End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in au
arXiv:2607.08029v1 Announce Type: new Abstract: The emergence of vision language models with fewer than 3 billion parameters has accelerated the implementation of on-device multimodal intelligence. Ho
arXiv:2510.04547v5 Announce Type: replace-cross Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-lang
arXiv:2607.06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and
arXiv:2607.07179v1 Announce Type: new Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information
arXiv:2603.15685v2 Announce Type: replace-cross Abstract: Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make in
arXiv:2602.23615v3 Announce Type: replace Abstract: Current Large Multimodal Models (LMMs) struggle with high-resolution visual inputs during the reasoning process, as the number of image tokens incre
arXiv:2607.07401v1 Announce Type: cross Abstract: While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acq
Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning dep
arXiv:2607.06649v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on cross-modal tasks by jointly training on large-scale textual and
SensorFM is a foundation model that analyzes multimodal wearable sensor signals from devices like smartwatches and fitness trackers to generate insights into health and activities. The model was train
arXiv:2601.06521v2 Announce Type: replace-cross Abstract: While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic
arXiv:2607.05614v1 Announce Type: cross Abstract: Document comprehension is a challenging yet impactful task for Multimodal Large Language Models, especially as these systems see growing adoption in r
arXiv:2607.05625v1 Announce Type: new Abstract: Wound monitoring is a critical yet underserved clinical challenge, where timely identification of severe adverse events (SAEs) such as infection, tissue
arXiv:2607.05765v1 Announce Type: new Abstract: Embodied navigation aims to build agents that interpret multimodal goals, reason in 3D space, and reach target destinations reliably in the real world.
arXiv:2602.00846v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) struggle with alignment due to the limitations of existing reward models (RMs), which are predominantly vis
arXiv:2607.06262v1 Announce Type: new Abstract: Diffusion and flow policies have recently demonstrated remarkable performance in robotic applications by accurately capturing multimodal robot trajector
arXiv:2607.05978v1 Announce Type: cross Abstract: Multimodal large language models can emit localized predictions, bounding boxes for objects and temporal windows for video and audio events, but they
arXiv:2406.07089v4 Announce Type: replace Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have shown promise for remote sensing tasks such as visual question answering and scene
arXiv:2607.05798v1 Announce Type: cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have evolved from static perception to interleaved visual-language reasoning, often re
arXiv:2607.04149v1 Announce Type: new Abstract: In safety-critical traffic scenarios, answering complex questions relies on minute, localized visual cues. However, standard Multimodal Large Language M
arXiv:2607.02551v1 Announce Type: cross Abstract: Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal p
arXiv:2607.04779v1 Announce Type: new Abstract: Reasoning segmentation aims to predict pixel-wise masks for targets given complex language queries. Existing approaches leverage Multimodal Large Langua
arXiv:2607.04665v1 Announce Type: new Abstract: Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image from a multimodal query consisting of a reference image and an edit text des
arXiv:2607.04334v1 Announce Type: new Abstract: Multimodal GUI agents read an interface through two redundant channels: the rendered pixels of a screenshot and a serialized structure such as a DOM or
arXiv:2607.04112v1 Announce Type: cross Abstract: Multimodal LLMs struggle to systematically model the temporal evolution of visual scenes in videos or multi-image sequences. Such inputs require model
arXiv:2607.03283v1 Announce Type: new Abstract: Embodied intelligence systems require not only end-to-end policy models, but also reusable functional modules that transform multimodal observations, ro
arXiv:2607.05310v1 Announce Type: new Abstract: Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining. Howe
arXiv:2607.03180v1 Announce Type: new Abstract: Multimodal diffusion transformers (MM-DiTs) have emerged as the prevalent backbone for modern text-to-image generation systems. However, they exhibit cr
arXiv:2607.02991v1 Announce Type: new Abstract: While multimodal Large Language Models (MLLMs) excel at offline video understanding, an interesting question of how far they are from serving as a real-
arXiv:2607.04625v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) streamlines long-document understanding by leveraging retrieval mechanisms to restrict input images to a highly c
arXiv:2607.02588v1 Announce Type: cross Abstract: Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally
arXiv:2607.02607v1 Announce Type: cross Abstract: Video reasoning requires Large Multimodal Models (LMMs) to remain grounded in dense evidence, yet existing systems largely adopt 'read-once, generate-
arXiv:2607.03530v1 Announce Type: new Abstract: We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for 'mental' visualizati
arXiv:2607.03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind
arXiv:2607.02961v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) exhibit remarkable vision-language capabilities and are increasingly deployed in real-world applications such as pe
arXiv:2607.02897v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize private information from web data, raising privacy conc
arXiv:2607.02907v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress but still struggle with complex visual reasoning tasks requiring multi-step
arXiv:2607.04609v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies that execute fixed-length action chunks can exhibit multimodal bifurcation: a cross-chunk inconsistency in which a
arXiv:2511.07403v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but continue to struggle with spatial rea
arXiv:2607.04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform int
arXiv:2510.11098v5 Announce Type: replace-cross Abstract: Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks r
What if a routine breast cancer H&E slide could help answer a hard question: how likely is this cancer to come back? Our @NatureComms paper introduces a multimodal AI test for recurrence risk across i
arXiv:2607.01400v1 Announce Type: cross Abstract: Deep multimodal brain-encoding models now predict fMRI responses to naturalistic video with high accuracy. Whether their predicted neural signals also
arXiv:2601.01095v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand tempo
arXiv:2607.01876v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal understanding, yet their enormous parameter scale and cross-modal