tutorials

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?

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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

tutorialsarxiv-cs-lg

8 Oct 2026

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

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

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

How Learning Governs Unlearning across the Memorization-Generalization Spectrum

arXiv:2610.08577v1 Announce Type: cross Abstract: While unlearning seeks to negate undesired capabilities acquired through learning, little research has examined how the way models learn shapes their

IEEE 802.11bx - WLAN Intelligent Networking (WIN): Toward an AI-Ready Wi-Fi 9

arXiv:2610.07900v1 Announce Type: cross Abstract: Wi-Fi 9 is expected to go beyond mere communication and provide new services such as sensing or computation. At this juncture, Artificial Intelligence

Is sqrt{d} Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?

arXiv:2610.07551v1 Announce Type: cross Abstract: Learning Gaussian mixture models (GMMs) using the Expectation-Maximization (EM) algorithm and its gradient-based variants is a fundamental problem in

Learn more about identifying AI-generated content: https://blog.google/innovation-and-ai/models-and-research/google-deepmind/synth-id-ai-con…

Google AI has published a new blog post titled “Learn more about identifying AI‑generated content,” announcing tools that make it easier to spot machine‑created text on a global scale. The article – r

tutorialsgoogle-ai--x

Learning Disentangled Representations with Quantum Variational Autoencoders

arXiv:2610.07196v1 Announce Type: cross Abstract: Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery

Learning Explainable Representations of Complex Game-playing Strategies

arXiv:2610.07638v1 Announce Type: new Abstract: As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules

Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis

arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E

Learning from Hallucinating Critical Points for Navigation in Dynamic Environments

arXiv:2509.26513v2 Announce Type: replace Abstract: Generating large and diverse obstacle datasets to learn motion planning in environments with dynamic obstacles is challenging due to the vast space

Learning Scientific Exploration from Human Research Decision Trajectories

arXiv:2610.07184v1 Announce Type: cross Abstract: A key challenge in building AI systems for scientific research is enabling extit{scientific exploration}: the systematic process of investigating unkn

Looks like you copied Chandra OCR 2 into your repo, renamed it, changed the license, and made the output worse (your olmocr score is 83.8 vs…

tutorialsjeremy-howard--x

Looks like you copied Chandra OCR 2 into your repo, renamed it, changed the license, and made the output worse (your olmocr score is 83.8 vs our 85.8). Introducing interfaze-1-lite, the first general

Mask-Guided KV Cache Eviction in Block Diffusion Language Models

arXiv:2610.06996v1 Announce Type: cross Abstract: Block diffusion language models keep a large key-value (KV) cache throughout generation and attend to it at every denoising step, limiting both memory

Mathematical Proof Assistants for Teaching Logic: The LogiKEy Methodology

arXiv:2610.08214v1 Announce Type: new Abstract: We report on an approach to teaching logic to mixed groups of computer science, mathematics, and philosophy students, based on the logico-pluralistic Lo

MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata

arXiv:2610.08479v1 Announce Type: cross Abstract: Few-shot meta-learning traditionally formulates task adaptation either as analytical gradient descent through unrolled computational graphs or as metr

Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors

arXiv:2610.08131v1 Announce Type: cross Abstract: Achieving compositional concept generalization (CoCoGen), the ability to understand novel situations by recombining learned primitives, remains a fund

Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment

arXiv:2610.07800v1 Announce Type: cross Abstract: Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is availabl

Optimal and Efficient Online Inverse Optimization

arXiv:2610.08735v1 Announce Type: new Abstract: In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear ob

OxiGen: Oxidation-State-Aware Crystal Generation

arXiv:2610.08296v1 Announce Type: new Abstract: Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable cr