Search: “tutorials”

dMoE: dLLMs with Learnable Block Experts

arXiv:2605.30876v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance whil

tutorialsarxiv-cs-cl

Eigenvectors of Experts are Training-free Non-collapsing Routers

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DGX agent

arXiv:2605.30992v1 Announce Type: new Abstract: Sparse Mixture of Experts (SMoE) architectures improve the training efficiency of Large Language Models (LLMs) by routing input tokens to a selected sub

tutorialsarxiv-cs-lg

1 Jun 2026

Function2Scene: 3D Indoor Scene Layout from Functional Specifications

arXiv:2605.30819v1 Announce Type: new Abstract: Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how th

Functorial Neural Architectures from Higher Inductive Types

arXiv:2603.16123v2 Announce Type: replace-cross Abstract: Neural networks often learn the parts of a task but fail on novel combinations of those parts. We argue that this failure is architectural: a

Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective

arXiv:2603.09936v2 Announce Type: replace Abstract: Generative Modeling via Drifting~itep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drif

Generative Quantum Data Embeddings for Supervised Learning

arXiv:2605.30866v1 Announce Type: cross Abstract: Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are

great overview of deepagents! what it is, how we made it great at complex tasks, how to take it to production

tutorialsharrison-chase--x

great overview of deepagents! what it is, how we made it great at complex tasks, how to take it to production here's a quick overview of a) what is deepagents b) what makes deepagents good at complex

Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models

arXiv:2605.31162v1 Announce Type: new Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We

Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

arXiv:2605.31352v1 Announce Type: new Abstract: Robotics manipulation usually assumes that the shape and pose of the object are known to the robot prior to motion planning. However, precise geometric

tutorialsarxiv-cs-ro

here's a quick overview of a) what is deepagents b) what makes deepagents good at complex tasks c) how to easily take one to production! htt…

tutorialsharrison-chase--x

DeepAgents are AI systems designed to handle complex tasks through advanced reasoning and planning capabilities. The post outlines the key features that enable DeepAgents to excel at complicated probl

How can embedding models bind concepts?

arXiv:2605.31503v1 Announce Type: new Abstract: Humans easily determine which color belongs to which shape in multi-object scenes, an ability known as concept binding. Vision-language embedding models

I find debates over whether companies find AI useful to be odd at this point I talk to leadership teams at lots of big firms, and it is pret…

tutorialsethan-mollick--x

I find debates over whether companies find AI useful to be odd at this point I talk to leadership teams at lots of big firms, and it is pretty universal that they are getting obvious and real value. T

Improving Relative Representations with Learned Anchors and Whitened Inner Products

arXiv:2605.30596v1 Announce Type: new Abstract: Independently trained neural models typically converge to incompatible latent representations, creating a fundamental barrier to highly modular AI syste

Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization

arXiv:2605.31508v1 Announce Type: new Abstract: Video Object-Centric Learning (OCL) aims to represent objects as extit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive

Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance

arXiv:2605.31304v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely used, but interpreting what they actually learn remains difficult. A major obstacle is that individual neurons

Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing

arXiv:2605.30622v1 Announce Type: cross Abstract: Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed

Just saving this here to document a story and as a self reflection on whether AI is really making me more productive Yesterday morning I fou…

tutorialsjeremy-howard--x

Just saving this here to document a story and as a self reflection on whether AI is really making me more productive Yesterday morning I found a way to complete the new HVM approach, that is much fast

'Just use AI' can be useful (I call it The Crowd in my informal framework I gave in my post below), but you also need a Lab where people can…

tutorialsethan-mollick--x

'Just use AI' can be useful (I call it The Crowd in my informal framework I gave in my post below), but you also need a Lab where people can go with their ideas and Leadership to make decisions about

KnowledgeGain: Evaluating and Optimizing Science News Generation for Reader Learning

arXiv:2605.31099v1 Announce Type: cross Abstract: Science news is an important medium to communicate discoveries between the research communities and the public. Yet, most metrics for generated or sum

Latest latentspace pod is excellent. @EthanHe_42 really gets it and lays out a lot of the thinking that led us to Flipbook and how to think …

Latest latentspace pod is excellent. @EthanHe_42 really gets it and lays out a lot of the thinking that led us to Flipbook and how to think about the future of generative UI agents 🤝 video gen 🤝 users

tutorialsswyx--x

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs

arXiv:2510.00419v2 Announce Type: replace Abstract: Zeroth-order optimizers have recently emerged as an attractive approach for fine-tuning large language models (LLMs), as they avoid backpropagation

Learning Coupled Subspaces for Multi-Condition Spike Data

arXiv:2410.19153v2 Announce Type: replace Abstract: In neuroscience, numerous studies conduct sensory or behavioral experiments under multiple conditions to acquire neural responses in the form of hig

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy

arXiv:2605.30432v1 Announce Type: cross Abstract: Social systems consist of networks of individuals who influence one another through social interactions. Studying how processes evolve on these networ

Learning Parametric Nitrogen Fertilizer Response Curves Using Neuro Symbolic Regression

arXiv:2605.31276v1 Announce Type: new Abstract: Accurately modeling crop response to Nitrogen (N) fertilization is a fundamental challenge in precision agriculture, as it impacts both economic returns

Learning Permutation-invariant Macroscopic Dynamics

arXiv:2605.30812v1 Announce Type: new Abstract: Accurately modeling the macroscopic dynamics of high-dimensional microscopic systems is of broad interest across the sciences. Many data-driven approach

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

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

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

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