tutorials

GitHub Copilot app for Beginners: Run several agents at once

Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful. The post GitHub Copilot app for Beginners: Run several agents

tutorialsgithub-ai-blog

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Tutorials

Induction and Inquiry via Probabilistic Reasoning over Language and Code

DGX agent

arXiv:2609.01815v1 Announce Type: new Abstract: How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. An

tutorialsarxiv-cs-ai

3 Sept 2026

Kirin: Animal Motion Generation from In-the-Wild Video

arXiv:2609.01823v1 Announce Type: new Abstract: Understanding animal motion is fundamental to modeling animal behavior and biomechanics, yet progress in this area lags far behind human motion research

Latent unified smooth Hamiltonians for excited state chemistry

arXiv:2609.01871v1 Announce Type: cross Abstract: We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems

Learning and extrapolating scale-invariant processes

arXiv:2601.14810v3 Announce Type: replace-cross Abstract: Machine Learning (ML) has deeply changed some fields recently, like Language and Vision and we may expect it to be relevant also to the analys

No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels

arXiv:2508.11180v2 Announce Type: replace-cross Abstract: Multi-view learning is widely applied to real-life datasets, but it often suffers from both missing views and missing labels. Prior probabilis

Oracle, will I ever learn? A study of prediction convergence and complementarity across link prediction models

arXiv:2609.02638v1 Announce Type: new Abstract: Knowledge graphs have become an important source of structured knowledge for Web applications, including search, question answering, and recommender sys

PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion

arXiv:2609.02216v1 Announce Type: new Abstract: Inductive knowledge graph completion (IKGC) aims to predict missing links involving entities unseen during training, requiring models to learn transfera

Persistent Sparse Autoencoders: Learning Feature-Specific Timescales in Language Model Representations

arXiv:2607.17117v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) decompose language model activations into sparse features, yet these models traditionally encode each token indepen

Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising

arXiv:2609.02654v1 Announce Type: new Abstract: Low-dose computed tomography (LDCT) measurements contain mixed Poisson-Gaussian noise. However, most self-supervised methods rely on generic image stati

Prompting the Unknown: Understanding Response Uncertainty in Large Language Models

arXiv:2407.14845v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses

Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery

arXiv:2609.02209v1 Announce Type: cross Abstract: Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast

SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging

arXiv:2509.16509v3 Announce Type: replace Abstract: Humans learn in two complementary ways: a slow, cumulative process that builds broad, general knowledge, and a fast, on-the-fly process that capture

SMart: A Multi-source Multi-phase Time Series Representation Transfer Framework

arXiv:2609.02203v1 Announce Type: cross Abstract: Time series representation learning (TSRL) has attracted growing research interests in recent years. Two recent explorations in TSRL are: i) exploitin

A Network Science Perspective on Evaluating Deep Graph Generative Models

arXiv:2609.01015v1 Announce Type: cross Abstract: Traditional network models from network science, such as the Erdos-Renyi and configuration models, generate random networks that reproduce few selecte

ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection

arXiv:2609.00853v1 Announce Type: cross Abstract: InfRared Small Target Detection (IRSTD) is a challenging task. Relying solely on pixel-level information, vision-only methods struggle to distinguish

BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Management

arXiv:2609.00615v1 Announce Type: cross Abstract: The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making a

Beyond Landmark Extraction: A Framework for Robust Geometric Feature Construction in Structured Image Classification

arXiv:2609.00634v1 Announce Type: new Abstract: Much of the literature on structured image recognition has disproportionately focused on the comparison of classification algorithms. Rather than invest

Building Expressive and Tractable Probabilistic Generative Models: A Review

arXiv:2402.00759v4 Announce Type: replace-cross Abstract: We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily fo

Dense Process Supervision for Search Agents via Fact Utility Estimation

arXiv:2609.00833v1 Announce Type: new Abstract: Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due t

Detailed explanation of how to create a text-to-image model from scratch.

tutorialsr-stablediffusion

Posting this here even if it's not a model you can use directly. It's about building a text-to-image model from scratch. The cookbook includes all the research material that you may or may be not inte

Exact Global MCMC with Denoising Diffusion

arXiv:2609.00279v1 Announce Type: cross Abstract: This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC proposals for complex high-dimensional ta

FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge

arXiv:2402.16934v2 Announce Type: replace-cross Abstract: Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effec

Fizgig 5.2 - combining two Minimax training methods beats either alone

tutorialsr-stablediffusion

Two of the ways that exist (im sure there are more) to train a LoRA on H3 well are on two different trainers. Fizgig's is Optimised Likeness Learning: I've found the stable core of H3's identity lives

Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain

arXiv:2609.00788v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information,

How an AWS team detects dashboard content failures at scale using Amazon Bedrock

Business intelligence dashboards can fail silently, showing blank, stale, or wrong data even when every infrastructure monitor reports healthy. Learn how an AWS team built an automated, AI-powered con

tutorialsaws-ml-blog

How Temporal Correlations Shape Memory in Linear Recurrent Neural Networks

arXiv:2609.00420v1 Announce Type: new Abstract: The linear recurrent neural network (LRNN) is a simple model for studying how much memory a network builds up as it trains. For uncorrelated inputs, ear

Ideal Observer for Segmentation of Dead Leaves Images

arXiv:2512.05539v3 Announce Type: replace Abstract: The visible parts of a scene are determined by occlusion among overlapping surfaces. Here we consider 'dead leaves' models, which replicate this by

Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing

arXiv:2609.00552v1 Announce Type: new Abstract: Existing coded-computing designs do not explicitly exploit the intrinsic structure of the input data. In communication systems, statistical structure an

Modernizing and scaling support operations with generative AI on AWS

Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and u

tutorialsaws-ml-blog

Poisson-Gamma Dynamical Systems with Time-varying Transition Dynamics

arXiv:2609.00896v1 Announce Type: new Abstract: Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to

Pre-carved Niches: The Formation Dynamics of Modular Task Partitions in Early LLM Training

arXiv:2609.01170v1 Announce Type: new Abstract: Large language models exhibit a modular internal organization that mirrors well-studied functional networks of the human brain, but how this organizatio

PredErase: Training-Free Object-and-Effect Removal with Predictive Latent Guidance

arXiv:2609.00956v1 Announce Type: new Abstract: Removing an object is not the same as filling its mask. Cast shadows and contact shading usually lie outside the user-provided instance mask M_obj, so a

Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey

arXiv:2609.01212v1 Announce Type: new Abstract: With the rapid and continuous growth in the incorporation of machine learning models based on the Transformer architecture, capable deployment is in hig

SCAFFOLD: A Large-Scale Structured Dataset of Computer Science Research Figures with Diagram QA and Chain-of-Thought Reasoning Traces

arXiv:2609.00018v1 Announce Type: new Abstract: Computer science papers rely heavily on diagrams: architecture drawings, system flowcharts, and pipeline schematics that often carry more information th

SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

arXiv:2603.17380v3 Announce Type: replace-cross Abstract: Virtual-cell models aim to predict how cell populations respond to perturbations, but control and treated cells are measured as unpaired popul

Semi-Supervised Virtual Staining via Morphology Preservation and Histopathological Realism Constraints

arXiv:2609.00984v1 Announce Type: cross Abstract: Virtual staining aims to computationally generate target-stained histopathological images while reducing the cost and time associated with conventiona

Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data

arXiv:2609.01262v1 Announce Type: new Abstract: Tabular deep learning (TDL) leverages neural networks (NN) to extract patterns from tabular data. Traditional TDL methods follow a supervised learning p

SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding

arXiv:2609.01353v1 Announce Type: new Abstract: Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineerin

Taming Modality Entanglement in Continual Audio-Visual Segmentation

arXiv:2510.17234v3 Announce Type: replace-cross Abstract: Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings

The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally

arXiv:2609.01587v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is widely used to reduce the cost of serving large language models (LLMs), but its accuracy cost is uneven and is oft

Training-Free Refinement of Flow Matching with Divergence-based Sampling

arXiv:2604.04646v2 Announce Type: replace-cross Abstract: Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample-wise velocities connecti

Unmasking Face Embeddings: Reading, Rendering and Naming with Foundation Models

arXiv:2609.00411v1 Announce Type: new Abstract: Modern face recognition (FR) owes much of its success to deep neural networks that learn to extract compact identity embeddings from face images. These

(V)LMs generalize beyond surface co-occurrence: Evidence from cross-modal number agreement

arXiv:2609.00443v1 Announce Type: cross Abstract: Language models learn about grammatical number primarily from co-occurrence, and show frequency effects as a result---sometimes taken to indicate that

What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models

arXiv:2609.01551v1 Announce Type: new Abstract: Self-supervised video foundation models learn rich spatiotemporal representations, yet it remains unclear what visual concepts these representations enc

Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation

arXiv:2608.29121v1 Announce Type: new Abstract: Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of cate

1 Sept 2026

ARMOR: Manifold-Oriented Training for Adversarially Robust Aerial Object Detection under Data Scarcity

arXiv:2608.29510v1 Announce Type: new Abstract: Aerial object detection is increasingly deployed in real-world applications, but models remain vulnerable to physical, universal adversarial patches tha

1 Sept 2026

As we prepare to release Astra, we’re focused on making increasingly capable AI safe and broadly accessible. Astra represents a significant …