arXiv AI
Jun 11
Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.
Multimodal models
Vision-language models, speech and cross-modal systems that read, look and listen in the same forward pass.
arXiv AI
Jun 11
CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.
arXiv AI
Jun 11
LASA: A Weak Supervision Method for Open-Vocabulary Scene Sketch Semantic Segmentation
arXiv:2606. 11837v1 Announce Type: cross Abstract: Open-vocabulary scene sketch semantic segmentation aims to assign dense semantic labels to sparse line drawings based on flexible category vocabularies specified at inference time, without relying on pixel-level annotations during training.
arXiv AI
Jun 11
Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends
arXiv:2606. 12207v1 Announce Type: cross Abstract: Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control.
arXiv AI
Jun 11
Frozen Multimodal Embeddings for Personality and Cognitive Ability Assessment in Asynchronous Video Interviews
arXiv:2606. 11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.
arXiv AI
Jun 11
Pretrained self-supervised speech models can recognize unseen consonants
arXiv:2606. 11542v1 Announce Type: cross Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized representations.
Hugging Face Trending Papers
Jun 10
Q-Fold: Query-Aware Focus-Context Spatio-Temporal Folding for Long Video Understanding
Long-video understanding remains challenging for multimodal large language models, because temporally extended videos often contain thousands of frames and are therefore expensive to process exhaustively. Existing methods usually construct compact visual inputs from long videos under a limited visual budget.
Hugging Face Trending Papers
Jun 10
MSUE: Multi-Modal Soccer Understanding Expert
This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Language Model (VLM), which systematically restructures raw domain data into diverse VQA samples, including concise answers and long-form responses.
Hugging Face Trending Papers
Jun 10
DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is misaligned with physical interaction, where a high-frequency modality changes at hundreds of hertz, vision evolves more slowly, and language stays constant across an episode.
Hugging Face Trending Papers
Jun 10
Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework
Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data. Existing methods, often reliant on static rule templates, struggle to traverse multi-hop reasoning chains or resolve latent spatial dependencies across multiple building entities.
Hugging Face Trending Papers
Jun 10
Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding
In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language. We propose a three-stage pipeline that decomposes the accident understanding into when, what, and where.
Hugging Face Trending Papers
Jun 10
MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning
We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intelligence reasoning. A key feature of our approach is that both the training and inference phases are augmented via knowledge distillation.
arXiv Machine Learning
Jun 10
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
arXiv:2606. 10592v1 Announce Type: new Abstract: Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal.
arXiv AI
Jun 10
Exploration of Foundation Model-Based Robots in Patient and Elderly Care
arXiv:2606. 10208v1 Announce Type: cross Abstract: Demand for older-adult and patient care is growing rapidly as populations age worldwide.
arXiv AI
Jun 10
Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters
arXiv:2606. 09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computation: the dense network cannot be retained on-chip, and every parameter must be loaded for every input.
arXiv AI
Jun 10
From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
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 AI
Jun 10
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.
arXiv AI
Jun 10
Whisfusion: Parallel ASR Decoding with Masked Diffusion
arXiv:2508. 07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency scale with transcript length.
arXiv AI
Jun 10
Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech
arXiv:2606. 09962v1 Announce Type: cross Abstract: Continuous diffusion for categorical data is a framework belonging to the diffusion family and aiming at generating discrete data.
arXiv AI
Jun 10
GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
arXiv:2604. 12306v3 Announce Type: replace-cross Abstract: Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools.