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Gaze Estimation for Human-Robot Interaction: Analysis Using the NICO Platform

arXiv:2509.24001v3 Announce Type: replace Abstract: This paper evaluates the current gaze estimation methods within a human-robot interaction (HRI) context of a shared workspace scenario. We introduce

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Hankel Subspace Self-Supervised Learning for Parallel MRI Reconstruction

DGX agent

arXiv:2610.11560v1 Announce Type: new Abstract: Parallel magnetic resonance imaging reconstruction is an ill-posed inverse problem under undersampling. Multi-coil acquisition and Hankel lifting expose

tutorialsarxiv-cs-cv

9 Oct 2026

ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics

arXiv:2603.13833v2 Announce Type: replace Abstract: Enabling robots to navigate open-world environments via natural language is critical for general-purpose autonomy. Yet, Vision-Language Navigation h

Implementation Guidelines for Data Quality Metrics

arXiv:2610.10919v1 Announce Type: cross Abstract: Despite decades of data quality (DQ) research, a gap remains between DQ dimensions, such as accuracy or completeness, which the literature defines in

Lapras: Latent Reasoning for Time Series Language Models

arXiv:2610.11111v1 Announce Type: new Abstract: Time Series Language Models (TSLMs) offer a promising path toward time series understanding by reasoning over temporal signals and producing natural lan

Learning from Hetero Density for Cryo-EM Protein Reconstruction

arXiv:2610.11403v1 Announce Type: new Abstract: Reconstructing protein structures from cryo-electron microscopy (cryo-EM) maps is essential for understanding macromolecular assemblies. Although learni

Machine learning and information theory concepts towards an AI Mathematician

arXiv:2403.04571v2 Announce Type: replace Abstract: The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathe

Mid-Training Language Models on Raw Video

arXiv:2610.11019v1 Announce Type: cross Abstract: Multimodal large language models learn mostly from paired image-text data or annotated video, and raw web video is rarely used to further train an exi

Neural Decoding as Cognitive Inference

arXiv:2610.11923v1 Announce Type: cross Abstract: The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observ

Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models

arXiv:2610.10990v1 Announce Type: new Abstract: Unified multimodal diffusion large language models (dLLMs) offer a single architecture for both image generation and multimodal understanding, but their

OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphs

arXiv:2610.12461v1 Announce Type: new Abstract: Recent 3D world models generate photorealistic, explorable scenes that remain frozen in time. OuroWorld is a mask-free framework that turns any static 3

Parametric Trajectory Distillation for Few-Step Video Generation

arXiv:2610.11498v1 Announce Type: new Abstract: Video diffusion and flow models require many sequential evaluations, making generation computationally expensive. Few-step distillation reduces this cos

Prosody-to-Text: Predicting text from low-pass filtered speech

arXiv:2610.11544v1 Announce Type: new Abstract: While predicting prosody from text is an established task in the field, the opposite direction, predicting text that fits a given prosodic pattern, rema

RISR: Residual-Informed Scientific Equation Discovery with Large Language Models

arXiv:2610.11387v1 Announce Type: cross Abstract: Symbolic regression combines structural search with numerical fitting, but aggregate fit scores do not describe how the remaining error varies across

RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation

arXiv:2602.14032v2 Announce Type: replace Abstract: Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on l

Score-Based Learning of Cluster DAGs from Interventions

arXiv:2610.11947v1 Announce Type: cross Abstract: Graphical approaches to causal abstraction transform a low-level causal directed acyclic graph (DAG) over many measured variables into a smaller, high

Self-sufficient Independent Component Analysis for Demixing Flows

arXiv:2512.00665v2 Announce Type: replace-cross Abstract: We study the problem of learning disentangled signals from data using non-linear Independent Component Analysis (ICA). Motivated by advances i

Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance

arXiv:2610.10867v1 Announce Type: new Abstract: Complex Network (CN) classification requires high-level structural characterizations that are both scale-invariant and computationally efficient. Method

Skill-V: Verifiable Self-Evolving Skill Library for Interactive Agents

arXiv:2610.11781v1 Announce Type: new Abstract: Interactive agents can turn experience into reusable skills, yet existing self-evolving skill libraries primarily improve by accumulating new knowledge.

Softmax Attention on Gaussian Mixtures: Linear When It Can, Selective When It Must

arXiv:2610.11798v1 Announce Type: cross Abstract: Softmax attention, at the heart of Transformers, has demonstrated remarkable capabilities. Yet its underlying mechanisms remain only partially underst

SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction

arXiv:2610.12349v1 Announce Type: new Abstract: Understanding a dynamical world calls for more than a latent state that summarizes its observations: the state should also be organized into the factors

Structure Tax: How Structured Output affects LLMs Performance

arXiv:2610.12056v1 Announce Type: new Abstract: Deploying large language models in production often requires constraining outputs to structured formats such as JSON or XML, and prior work treats the r

Thinking Inertia: LLMs Keep Thinking When Told Not To

arXiv:2610.11765v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly ship with explicit 'thinking modes', yet their counterpart, 'no-thinking', has received far less attention. We

USDCraft: Geometrically Grounded Programmatic Modeling of Articulated 3D Assets for Simulation

arXiv:2610.11322v1 Announce Type: new Abstract: Geometrically faithful and functional articulated 3D assets are essential for real-to-sim robot manipulation, where policies trained in simulation must

We’re putting too much faith in AI’s ability to say no

Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is fu

tutorialsmit-tech-review

What can linear attention learn from nonlinear teachers in-context?

arXiv:2610.10761v1 Announce Type: cross Abstract: Linear attention is a tractable model for understanding the mechanisms governing in-context learning in transformers. For linear regression tasks, rec

Whose Ground Truth? Embracing Ambiguity in Human-Centered AI

arXiv:2610.10805v1 Announce Type: new Abstract: As AI systems increasingly interact with people and make decisions about them, understanding human interpretations becomes an important part of developi

A Bayesian Mirror Architecture for Emergent Consciousness: Circular Hierarchies, Self-Manifolds, and Hybrid Event-Self Binding

arXiv:2610.08792v1 Announce Type: new Abstract: We present a foundational formulation of the Bayesian Mirror Architecture (BMA), a self-referential generative framework in which sensory abstractions,

Building tonight? Join us at Codex Build Night where our AI Creative Technologist Guy Bar Hava will be demoing alongside @comfyui and @OpenA…

Building tonight? Join us at Codex Build Night where our AI Creative Technologist Guy Bar Hava will be demoing alongside @comfyui and @OpenAI to show how to build with LTX-2.5 open weights. Sign up he

tutorialscomfyui--x

CERO: Where and When to Allocate Rollouts for RL Post-Training

arXiv:2610.09679v1 Announce Type: new Abstract: Adaptive rollout methods for group-relative reinforcement learning typically allocate a fixed per-update budget across prompts. We instead study how to

Consistent Distribution Matching for Data-Free Diffusion Distillation

arXiv:2610.09221v1 Announce Type: cross Abstract: Flow and diffusion models suffer from slow inference due to computationally expensive numerical integration. Distillation provides a promising way for

Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas

arXiv:2609.39167v1 Announce Type: cross Abstract: Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain de

Depth-to-RGB: Repurposing a Frozen Depth Estimator for Geometry-Guided Compositing

arXiv:2610.09125v1 Announce Type: new Abstract: Reference-based object compositing inserts or replaces an object using a background image, a reference image, and a 2D compositing mask. These inputs gu

DisParQ: Self-Supervised Part Concepts for Interpretable Vision Foundation Models

arXiv:2610.09802v1 Announce Type: new Abstract: Concept-based vision models represent images through an intermediate layer of human-inspectable concepts, so what a model relies on can be traced to tho

EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching

arXiv:2610.09715v1 Announce Type: new Abstract: We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on

Efficient Provably Private Classification with a Tabular Foundation Model

arXiv:2610.10068v1 Announce Type: new Abstract: Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level informat

Evaluating the Transfer of Co-Evolved Communication from 2D to 3D Simulation

arXiv:2610.09280v1 Announce Type: new Abstract: This work examines the transfer of a co-evolved communication mechanism between two robotic agents from a discrete two-dimensional (2D) simulator to a t

Event-Aligned Visual Action Reasoning for World Action Models

arXiv:2610.09427v1 Announce Type: new Abstract: World-Action Models (WAMs) utilize future visual prediction as an intermediate reasoning process to guide action generation. However, existing WAMs typi

Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines

arXiv:2610.09196v1 Announce Type: new Abstract: Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that pr

Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative

arXiv:2610.09984v1 Announce Type: cross Abstract: While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier

Fully Interpretable Minimal Transformers: From Geometry to Algorithm

arXiv:2610.09838v1 Announce Type: new Abstract: We present a framework for building and interpreting minimal transformer models. By constraining a transformer's embedding dimension and head size to 2,

Geometry-Centered 3D Latent World Models for Growing Surfaces

arXiv:2506.03173v3 Announce Type: replace Abstract: Many physical systems do not merely move or deform; they grow, adding material and changing the geometry that a world model must represent. Existing

GeoPrior-Mamba: Structured Process Priors with Mamba for Fine-Resolution XCO2 Reconstruction

arXiv:2610.09456v1 Announce Type: new Abstract: Reconstructing fine-resolution column-averaged dry-air CO2 (XCO2) fields from sparse satellite observations requires models to infer spatial structure t

GRACE: Generation-aware latent compression for efficient video generation

arXiv:2610.10524v1 Announce Type: new Abstract: Highly compressed video autoencoders offer an effective way to accelerate video diffusion models, as the Diffusion Transformer (DiT) operates on far few

HCPN-GCN: Scaling Hierarchical Prototype Networks with Cone Geometry for Continual Graph Learning

arXiv:2610.08823v1 Announce Type: cross Abstract: Continual Graph Learning (CGL) aims to incrementally learn from graph-structured data while preserving knowledge acquired from previous tasks. A major

HealthcareNLP: where are we and what is next?

arXiv:2512.08617v2 Announce Type: replace Abstract: This tutorial focused on Healthcare Domain Applications of NLP, what we have achieved around HealthcareNLP, and the challenges that lie ahead for th

How Do Transformers Learn to Represent Symmetries?

arXiv:2610.10305v1 Announce Type: new Abstract: Training Transformer-based architectures with finite data augmentation has become an increasingly popular approach in geometric machine learning. Despit

Leaner Transformers Can Easily Learn to Cluster

arXiv:2610.09760v1 Announce Type: new Abstract: Transformers have in-context learning capabilities, where some known learning algorithms can be executed in the forward pass through the model. Recent w