Search: “tutorials”

Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts

arXiv:2608.20061v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost. However, optimizing

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Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners

DGX agent

arXiv:2608.19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on e

tutorialsarxiv-cs-ai

21 Aug 2026

MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use

arXiv:2608.20202v1 Announce Type: new Abstract: Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. However, existing

Orthogonal JEPA: Factorized Predictive States for Latent World Models

arXiv:2608.20065v1 Announce Type: new Abstract: World models construct latent states that support prediction, planning, and reasoning about an underlying system. Joint-embedding predictive architectur

RIPE++: Reinforced Keypoint Learning from Positive Pairs Only

arXiv:2608.19693v1 Announce Type: new Abstract: Sparse keypoint extraction and matching underpin core tasks in geometric computer vision, including structure-from-motion, visual SLAM, augmented realit

skchange: Fast and Flexible Algorithms for Changepoint Detection

arXiv:2608.19767v1 Announce Type: cross Abstract: Skchange is an open-source Python library for detecting structural changes in time series. It implements modern change detection algorithms within a u

Specification-delta-driven data governance: an empirical study of the {guillemotleft}spec-delta{guillemotright} as the unit of change in lakehouse data platforms

arXiv:2608.19838v1 Announce Type: new Abstract: Spec Driven Development SDD has consolidated the idea that the specification rather than the code should be the primary artefact governing AI assisted w

Structured Affinity for Unsupervised Visual Class-Incremental Memory in Deep Artificial Immune Networks

arXiv:2608.20104v1 Announce Type: cross Abstract: Artificial immune networks (AINs) are naturally memory-forming systems, but conventional visual AINs often rely on flattened vector affinity that igno

The Thousand Brains Theory 2.0: An Extension for the Long-Range Connections of the Neocortical Heterarchy

arXiv:2507.05888v2 Announce Type: replace-cross Abstract: Vernon Mountcastle hypothesized that the basis for intelligence in mammals is the replication of a general computational unit, the cortical co

Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion

arXiv:2406.09770v2 Announce Type: replace-cross Abstract: Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape

Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents

arXiv:2608.19794v1 Announce Type: new Abstract: The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward gen

UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM

arXiv:2601.11665v3 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management

What You Can't See Is What You Learn: Restricted Evidence Visibility Favors Compositional Generalization in Shared-Genome Language-Model Societies

arXiv:2608.20054v1 Announce Type: new Abstract: Multi-module systems often expose every module to the full input. We test whether restricting evidence visibility changes which solutions gradient-based

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation

arXiv:2509.04222v2 Announce Type: replace Abstract: Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure. How

A single design choice determines whether machine learning models of materials make physically impossible predictions

arXiv:2608.18714v1 Announce Type: cross Abstract: Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built in

About the impact of grouping classes in multiclass classification [D]

tutorialsr-machinelearning

A premise: I hope this question is 'worth' of this subreddit, I did a decent amount of research before posting, I thought it was potentially interesting enough for it, but possibly not basic enough fo

AI Can Learn Scientific Taste

arXiv:2603.14473v3 Announce Type: replace Abstract: Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with

Build intelligent security for healthcare APIs with Amazon Bedrock

Learn how to add context-aware security monitoring to FHIR APIs using Amazon Bedrock. This post shows how to detect anomalous access patterns, classify data sensitivity automatically, and generate com

tutorialsaws-ml-blog

CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation

arXiv:2608.18614v1 Announce Type: new Abstract: Industrial visual inspection must both decide whether a product is defective and localize the defect, yet pixel-level masks are costly to collect at sca

Composed Historical Image Retrieval by Modeling Temporal Representations

arXiv:2608.18694v1 Announce Type: cross Abstract: While time evolves linearly, the geometry of neural embedding spaces is inherently multi-dimensional, often chaotic, and difficult to interpret. In pr

Epistemic Subordination: Generative AI and the Infrastructure of Knowledge

arXiv:2608.18758v1 Announce Type: cross Abstract: Generative AI does not merely produce biased outputs. It encodes the majority's way of knowing as the default infrastructure of knowledge itself. We c

GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs

arXiv:2608.18292v1 Announce Type: cross Abstract: Consider a robot guide dog escorting a blind user to an available seat while a second assistive robot dog concurrently retrieves a cup of coffee and d

Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets

arXiv:2608.18907v1 Announce Type: cross Abstract: Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative

Learning Topological Features of widehat Z-invariants

arXiv:2608.18570v1 Announce Type: cross Abstract: Machine learning and data analysis techniques have recently emerged as powerful tools for identifying patterns and formulating conjectures in mathemat

MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction

arXiv:2608.18546v1 Announce Type: new Abstract: Multimodal urban data has expanded the applications of urban region representation learning, such as functional zone identification and real estate appr

PSA: Proper prompt structure REALLY matters in H3

tutorialsr-stablediffusion

I had mistakenly been using a base for H3 prompting from some random tip / example by someone. It worked ok, I thought. But I was getting a bit frustrated because almost every time I was making a long

Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses

arXiv:2608.18659v1 Announce Type: cross Abstract: Tsetlin Machines (TMs) learn interpretable Boolean clauses using finite-state automata. We introduce the Quantum-Logic Tsetlin Machine (QL-TM), which

Role-Conditioned Sub-Token Routing for Efficient Vision-Language-Action Policies

arXiv:2608.18410v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models process long multimodal token sequences, making inference expensive in both memory and computation. Existing efficie

Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance

arXiv:2608.18602v1 Announce Type: new Abstract: Computer vision is increasingly used to automate recognition tasks in large ecological datasets, but more complex tasks such as multi-object tracking co

What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems

arXiv:2608.18186v1 Announce Type: cross Abstract: In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surround

X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA

arXiv:2608.18986v1 Announce Type: new Abstract: Digital subtraction angiography (DSA) is the reference standard for leptomeningeal collateral (LMC) assessment, providing critical prognostic insights t

ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows

arXiv:2509.15236v2 Announce Type: replace-cross Abstract: Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the traini

Children, but not language models, show accelerating returns in word learning

arXiv:2608.17120v1 Announce Type: new Abstract: Children learn hundreds of words over the first years of their lives, in a process that begins slowly but quickly picks up speed. Prior models describe

Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning

arXiv:2608.16907v1 Announce Type: cross Abstract: Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on Ge

Environment-Invariant Subspace Learning for Generalizable Deepfake Detection

arXiv:2608.17700v1 Announce Type: new Abstract: Cross-distribution generalization remains a critical bottleneck in deepfake detection. While recent efforts leverage the semantic priors of large-scale

From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation

arXiv:2608.18076v1 Announce Type: cross Abstract: Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically o

General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting

arXiv:2608.17440v1 Announce Type: new Abstract: Although Graph Neural Networks (GNNs) have made significant advances in spatio-temporal traffic forecasting, their performance is limited when they rely

How Do Large Language Models Learn Concepts During Continual Pre-Training?

arXiv:2601.03570v2 Announce Type: replace Abstract: Human beings primarily understand the world through concepts (e.g., dog), abstract mental representations that structure perception, reasoning, and

inspired by the anthropic post, i made a protein font! how does it work? when you design by differentiating backward through a folding model…

inspired by the anthropic post, i made a protein font! how does it work? when you design by differentiating backward through a folding model like esm-fold2 you can ask that the distogram of the folded

tutorialsyann-lecun--x

Intent-Driven Dynamic Chunking: Segmenting Documents to Reflect Predicted Information Needs

arXiv:2602.14784v1 Announce Type: cross Abstract: Breaking long documents into smaller segments is a fundamental challenge in information retrieval. Whether for search engines, question-answering syst

J-Miner: Recovering Executable Decision Knowledge from Language-Model Classifiers

arXiv:2608.17063v1 Announce Type: cross Abstract: Large language models can be fine-tuned into specialized classifiers that perform well across diverse text tasks and make complex judgments, but they

KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn

arXiv:2608.17150v1 Announce Type: new Abstract: To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content

Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models

arXiv:2608.17306v1 Announce Type: cross Abstract: While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generaliz

Live Interactive Training for Video Segmentation

arXiv:2603.26929v2 Announce Type: replace Abstract: Interactive video segmentation often requires many user interventions for robust performance in challenging scenarios (e.g., occlusions, object sepa

LLMs increase the complexity of codebases. They duplicate methods, write overdefensive code against impossible edge cases, & overoptimize to…

tutorialsjeremy-howard--x

LLMs increase the complexity of codebases. They duplicate methods, write overdefensive code against impossible edge cases, & overoptimize too early. Can further training fix this? Naur's “Programming

Looking Beyond the Scale: Do Surgical Skill Models Learn Transferable Representations Across Assessment Rubrics?

arXiv:2608.17519v1 Announce Type: new Abstract: Vision-based surgical skill assessment has shown strong in-domain results, yet a fundamental question remains unasked: do these models learn transferabl

MCTS-KBQA: Monte Carlo Tree Search with Information Gain Rewards for Knowledge Base Question Answering

arXiv:2502.13428v2 Announce Type: replace-cross Abstract: This work investigates how to improve large language model (LLM)-based reasoning for knowledge base question answering (KBQA) via Monte Carlo

MemCatalyst: Amplifying Data Auditing on Vision-Language Models via Data Poisoning

arXiv:2608.17722v1 Announce Type: cross Abstract: Vision-Language models (VLMs) achieve outstanding performance largely due to the amount of training data available on the internet. At the same time,