tutorials

LLMs learn by predicting tokens. World models (JEPA, data2vec) learn by predicting their own abstractions. Which needs more data? For data w…

LLMs learn by predicting tokens. World models (JEPA, data2vec) learn by predicting their own abstractions. Which needs more data? For data with hidden hierarchy, we prove the gap is exponential. https

tutorialsyann-lecun--x

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Tutorials

LPTR-AFLNet: Lightweight Integrated Chinese License Plate Rectification and Recognition Network

DGX agent

arXiv:2507.16362v3 Announce Type: replace Abstract: Chinese License Plate Recognition (CLPR) faces numerous challenges in unconstrained and complex environments, particularly due to perspective distor

tutorialsarxiv-cs-cv

1 Jun 2026

Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models

arXiv:2605.31603v1 Announce Type: cross Abstract: Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelit

Personalize Your Large Vision-language Models With In-context Prompt Tuning

arXiv:2605.31513v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated strong general multimodal capability and are increasingly deployed in downstream systems. This tr

Practical Cross-Band Channel Prediction for AI-RAN via Physics-Guided Deep Unfolding

arXiv:2605.31279v1 Announce Type: cross Abstract: To make cross-band channel prediction practical for AI-native RAN, algorithms must generalize across diverse environments and support real-time infere

Procedural Generation of First Person Shooter Maps using Map-Elites

arXiv:2605.30570v1 Announce Type: new Abstract: We investigate the application of MAP-Elites (a well-known quality diversity algorithm) to design levels for First-Person Shooter (FPS) games. We consid

Reliable Self-Improvement Training by Verifying Reasoning, Not Just Answers

arXiv:2603.21558v2 Announce Type: replace Abstract: Self-improvement training, where models learn from self-generated solutions, promises sustained capability gains but suffers from a pervasive failur

Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams

arXiv:2605.31108v1 Announce Type: new Abstract: In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to ot

Representation Forcing for Bottleneck-Free Unified Multimodal Models

arXiv:2605.31604v1 Announce Type: new Abstract: Unified multimodal models (UMMs) aim to handle perception and generation in a single model. Yet existing UMMs still rely on a frozen, separately pretrai

Reward Learning from Best-of-N Preference Data: Targets, Tradeoffs, and Design Principles

arXiv:2605.30619v1 Announce Type: cross Abstract: Best-of-N sampling is widely used to construct pairwise preference data: N candidates are drawn from a base distribution, and the best is paired with

Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks

arXiv:2605.31106v1 Announce Type: new Abstract: Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. Ho

Robust Dreamer: Deviation-Aware Latent Gaussian Memory for Action-Controlled AR Video Generation

arXiv:2605.30855v1 Announce Type: new Abstract: Frame-wise action-controlled image-to-video generation is a promising paradigm for interactive world simulation, where each control signal should elicit

Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?

arXiv:2605.30557v1 Announce Type: cross Abstract: Spatial reasoning is a fundamental capability for vision-language models (VLMs) deployed in real-world environments. However, visual observations are

Sequential Group Composition: A Window into the Mechanics of Deep Learning

arXiv:2602.03655v2 Announce Type: replace Abstract: How do neural networks trained over sequences acquire the ability to perform structured operations, such as arithmetic, geometric, and algorithmic c

Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning

arXiv:2509.22335v3 Announce Type: replace-cross Abstract: We investigate why deep neural networks suffer from loss of plasticity in continual learning, and thus fail to learn new tasks without reiniti

Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail

arXiv:2605.31244v1 Announce Type: new Abstract: Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the

SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World

arXiv:2605.30770v1 Announce Type: new Abstract: Extending humanoid traversal to the open world is key to practical deployment in human environments, but remains challenging. The robot must use vision

tutorialsarxiv-cs-ro

STEP: Learning STructured Embeddings for Progressive Time Series

arXiv:2605.31061v1 Announce Type: cross Abstract: We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transition

SWIM: Single-Instance Whole-Body Imitation for swiMming

arXiv:2605.31120v1 Announce Type: cross Abstract: We propose a new method for synthesizing physically-based swimming motions. Physically-based character animation aims to generate physically valid, co

Text-guided Feature Disentanglement for Cross-modal Gait Recognition

arXiv:2605.30784v1 Announce Type: new Abstract: Gait recognition is a biometric technique that identifies individuals based on their walking patterns, offering advantages in long-range, non-intrusive

The relative strength of hierarchical structure and statistics differs across the measures in naturalistic reading

arXiv:2509.23195v2 Announce Type: replace Abstract: The hierarchical syntactic structure and non-hierarchical, statistical, or sequential factors have long been framed as rival theories in accounting

tutorialsarxiv-cs-cl

The Terminal Representation in Reinforcement Learning

arXiv:2605.31289v1 Announce Type: cross Abstract: Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are thr

Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing

arXiv:2605.31367v1 Announce Type: cross Abstract: Token mixing layers play a key role in how language models can learn and generate long-range dependencies. Their efficiency relies on the necessary tr

tutorialsarxiv-cs-cl

Transforming and Encoding FTS for SAT Solving: What Helps, What Hurts (Extended Version)

arXiv:2605.30563v1 Announce Type: new Abstract: Factored tasks are a classical planning representation that extends SAS+ with limited forms of disjunctive preconditions, conditional effects, and angel

Transforming rare cancer research with Amazon Quick: Integrating biomedical databases for breakthrough discoveries

In this post, we walk through how to use Amazon Quick Research to integrate biomedical data sources for rare cancer research. The walkthrough uses pediatric sarcoma as the research domain and draws on

tutorialsaws-ml-blog

Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

arXiv:2411.19463v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, pract

You can work 5 days a week and succeed as a startup. Mercury has done that from day 0 and we are valued @ $5.2bn 7 years after launch. I hav…

tutorialsjeremy-howard--x

You can work 5 days a week and succeed as a startup. Mercury has done that from day 0 and we are valued @ $5.2bn 7 years after launch. I have been an entrepreneur for 20 years and raised 3 kids while

you gotta be confident enough to act and open enough to learn

David Holz emphasizes the dual importance of confidence and openness in effective action and growth. The post suggests that success requires both the conviction to act decisively and the intellectual

tutorialsdavid-holz--x

actually wild

actually wild 'You cannot govern a technology you have only been briefed on.' Dr. @VivianBala's challenge to his fellow legislators has become a global rallying cry. Last week, @takahiroanno, leader o

tutorialsswyx--x

OpenAI Robotics is hiring, looking for exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture ro…

OpenAI Robotics is hiring, looking for exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society. AI should be able to help

tutorialssam-altman--x

They call it stupid hot for a reason: Heat muddles animal brains

Research shows that animals get their minds muddled during heat waves, with birds struggling to learn, dogs biting more often, and chamois picking fights when temperatures rise. When animals can't sta

tutorialsars-technica

Try Tesla self-driving

Try Tesla self-driving As little as five years ago, I publicly stated that self driving cars were not in the imminent future. I was dead wrong about that. I am now a happy owner of a cyber truck, and

tutorialselon-musk--x

Help Needed: How to create this type of art in Stable Diffusion? (Models, LoRA & settings)

tutorialsr-stablediffusion

This Reddit post from r/StableDiffusion seeks community guidance on replicating a specific art style using Stable Diffusion, covering recommended models, LoRA (Low-Rank Adaptation) fine-tuning techniq

30 May 2026

I did a talk on LLM Wikis and HTML artifacts recently, if you are curious to learn more on the topic: https://academy.dair.ai/events/cmovobp…

I did a talk on LLM Wikis and HTML artifacts recently, if you are curious to learn more on the topic: https://academy.dair.ai/events/cmovobp97000904l5h0n9a2yz Doing a second session and a few releases

tutorialsdair-ai--x

30 May 2026

On acknowledging AI (don’t take it too seriously)

tutorialsethan-mollick--x

On acknowledging AI (don’t take it too seriously) My modest proposal for how to acknowledge AI work is to use the Latin phase 'Fieri Iussit,' which means 'commanded to be made,' a common phrase on Rom

30 May 2026

A shared playbook for trustworthy third party evaluations

This document outlines OpenAI's framework and recommendations for conducting independent third-party evaluations of AI systems to ensure trustworthiness and accountability. It establishes shared stand

tutorialsopenai

Aleph 2.0

tutorialscristobal-valenzuela--x

Aleph 2.0 Introducing Runway Aleph 2.0. Edit videos using AI, keeping camera and motion all consistent. Select specific frames in your video to reprompt with GPT Images 2.0 or Nano Banana 2, then appl

BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models

arXiv:2512.00283v3 Announce Type: replace-cross Abstract: Foundation models have revolutionized various fields such as natural language processing (NLP) and computer vision (CV). While efforts have be

CamC2V: Context-aware Controllable Video Generation

arXiv:2504.06022v3 Announce Type: replace Abstract: Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditi

Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions

arXiv:2605.29008v1 Announce Type: new Abstract: Driving a system from one state to another through targeted interventions is a fundamental challenge in science, yet most predictive models offer limite

Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties

arXiv:2505.02743v2 Announce Type: replace Abstract: Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data g

Deep Psychovisual Image Representations

arXiv:2605.29260v1 Announce Type: new Abstract: Psychovisual models suggest human vision decouples low-level feature extraction from higher cognition by first forming intermediate abstractions. In con

Detecting Unknown Objects via Energy-based Separation for Open World Object Detection

arXiv:2603.29954v2 Announce Type: replace Abstract: In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to

DiScoFormer: Plug-In Density and Score Estimation with Transformers

arXiv:2511.05924v3 Announce Type: replace Abstract: Estimating probability density and its score from samples remains a core problem in generative modeling, Bayesian inference, and kinetic theory. Exi

Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning

arXiv:2605.30039v1 Announce Type: new Abstract: Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains throu

Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach

arXiv:2406.10238v2 Announce Type: replace Abstract: An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at

tutorialsarxiv-cs-cl

ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models

arXiv:2605.29438v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are a powerful paradigm for generalist robotic control. However, their high computational cost and limited control f

tutorialsarxiv-cs-ro

Explaining Concept Shift with Interpretable Feature Attribution

arXiv:2505.20634v2 Announce Type: replace Abstract: Concept shift occurs when the distribution of labels conditioned on the features changes between domains, which can make even a well-tuned ML model