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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,

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Tutorials

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

DGX agent

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

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8 Oct 2026

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

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

Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals

arXiv:2610.10037v1 Announce Type: new Abstract: Physical reservoir computing exploits the nonlinear dynamics of physical systems to process time-dependent data with greater energy efficiency than conv

Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position

arXiv:2610.10114v1 Announce Type: new Abstract: The architectural design of Large Language Models (LLMs) is shifting from traditional full-attention-only models to hybrid models, which combine differe

tutorialsarxiv-cs-cl

MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection

arXiv:2610.09473v1 Announce Type: new Abstract: Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift an

MORCA: Offline-to-Online Reinforcement Learning for Adaptive Cache Reuse in Video Diffusion Acceleration

arXiv:2610.10457v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) achieve remarkable performance in video synthesis, but their iterative denoising process suffers from high inference laten

Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding

arXiv:2610.09355v1 Announce Type: cross Abstract: Learning EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalogra

OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments

arXiv:2610.10210v1 Announce Type: new Abstract: Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under diff

PARC-Loc: Text-to-Point-Cloud Localization with Partial Assignment and Relational Consistency

arXiv:2610.09761v1 Announce Type: cross Abstract: Text-to-point-cloud localization estimates a position in a city-scale 3D map from descriptions of surrounding objects. Existing coarse-to-fine methods

tutorialsarxiv-cs-cl

Physics-Informed Neural Plasticity: PDE Solvers That Reshape Themselves

arXiv:2610.09510v1 Announce Type: new Abstract: Physics-informed neural PDE solvers adapt their parameters to satisfy governing equations, yet their representational structure typically remains fixed

Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation

arXiv:2509.18597v3 Announce Type: replace Abstract: Large language models (LLMs) can translate natural-language instructions for robotic manipulation into executable code, but ambiguity, noisy generat

S2Tok: Streaming 3D Gaussian Reconstruction with Persistent Spatial Tokens

arXiv:2610.08978v1 Announce Type: new Abstract: Streaming 3D reconstruction requires more than a sequence of geometric predictions: it requires a persistent scene state that can incorporate new eviden

Spatial Latent Reasoning for Embodied Reference Understanding

arXiv:2610.09418v1 Announce Type: new Abstract: Pointing-gesture visual grounding requires connecting hand geometry with the visual identity and extent of a referred object. A central challenge for co

The Dichotomy Between Pattern Recognition and Step-by-Step Reasoning

arXiv:2610.09186v1 Announce Type: new Abstract: We argue that pattern recognition and step-by-step reasoning are two ends of a spectrum. A large language model (LLM) learns to reason step-by-step when

Trajectory Planning without Trajectory Data: A Manifold-Guided Approach

arXiv:2610.08863v1 Announce Type: new Abstract: A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model

Video-Conditioned Generative Joint 2D-3D Hand Motion Recovery

arXiv:2610.10512v1 Announce Type: new Abstract: Recovering faithful 3D hand motion from video remains challenging due to frequent occlusions and incomplete visual observations, which make frame-wise p

VM-ARRAYDPS: Virtual Microphone Augmented Diffusion Posterior Sampling for Unsupervised Blind Speech Separation

arXiv:2610.09334v1 Announce Type: cross Abstract: Blind Source Separation(BSS) is a fundamental problem in signal processing, aiming to separate multiple source signals from their mixtures without pri

When does a network's training history predict its future learning better than its current state? Evidence from a response probe and a forecasting screen

arXiv:2610.09621v1 Announce Type: new Abstract: Networks that behave alike now can still learn differently when training continues. Work on loss of plasticity and critical periods shows that the path

Anchor and Adapt: Asymmetric Prompt Adaptation for Few-Shot Industrial Anomaly Detection

arXiv:2610.07016v1 Announce Type: cross Abstract: In few-shot industrial anomaly detection, the few normal target images provide no direct defect supervision, making anomaly prompts difficult to learn

AssemState: Manual and Physical-State-Guided Reasoning for Zero-shot Furniture Assembly

arXiv:2610.08446v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physic

CETUS: How Far Do Representations Trained on Earth Transfer to Cassini SAR of Titan?

arXiv:2610.07576v1 Announce Type: new Abstract: Cassini synthetic aperture radar (SAR) images reveal the dunes, plains, and lake basins of Titan, providing an instance of representations learned from

Detecting a Shift Is Not Enough: Exact Minimax Limits of Linear Representation Repair

arXiv:2610.08069v1 Announce Type: new Abstract: A mean shift between two data sources can be easy to detect but hard to remove without substantially changing their representations. We cast its removal

Do Neural PDE Solvers Learn the Right Dynamics?

arXiv:2610.06952v1 Announce Type: new Abstract: Neural PDE solvers can achieve low prediction errors, but do they reproduce the dynamics of the systems they model? Prediction scores alone offer an inc

Educating future engineers about LLMs: A scalable workshop

arXiv:2610.07027v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly integrated into engineering workflows, students require hands-on experience to learn how to collabora

ElasticFit: Fit-Aware 3D Object Insertion via VLM Reasoning and Generative Adaptation

arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while pr

Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation

arXiv:2610.07708v1 Announce Type: new Abstract: Recommender systems learn from observed user-item interactions, but explicit negative feedback is often unavailable. Since deep learning models require

ExoBridge: Learning a Bare Hand to Hand-Worn Exoskeleton Mapping through Human Limb Coupling

arXiv:2610.07692v1 Announce Type: new Abstract: Human video offers a scalable source of experience for dexterous robot learning, but obtaining motion and tactile supervision while preserving bare hand

Explore ideas through interactive visuals that respond as you learn, try, and discover.

On October 7, 2026 OpenAI released a session titled “Explore ideas through interactive visuals that respond as you learn, try, and discover.” The webcast showcased real‑time visual tools that adapt to

tutorialsopenai--x

Generalizable Robustness Testing of DNN-Based Robotic Navigation Systems via XAI-Guided Search

arXiv:2610.06862v1 Announce Type: new Abstract: **Context:** Deep Neural Networks (DNNs) increasingly control Cyber-Physical Systems (CPSs), yet small input perturbations can cause unsafe system-level

How Bregman Divergences Shape Shampoo

arXiv:2610.08534v1 Announce Type: new Abstract: Understanding the principles behind Shampoo has recently guided the development of more effective neural network optimizers. These methods learn a preco