tutorials

scKDGM: KAN-guided Dynamic Graph Masked Learning for Single-Cell RNA-seq Clustering

arXiv:2606.28459v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) clustering is essential for identifying cell types, but high dimensionality, sparsity, dropout, and technical noi

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See, Think, Learn: A Self-Taught Multimodal Reasoner

DGX agent

arXiv:2512.02456v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have achieved remarkable progress in integrating visual perception with language understanding. However, effecti

tutorialsarxiv-cs-cl

30 Jun 2026

SemDynReg: Semantics-Guided Deformation Regularization for Dynamic 3D Gaussian Splatting

arXiv:2606.28656v1 Announce Type: cross Abstract: Deformable 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for rendering dynamic scenes in a wide range of 3D applications. However,

Showing how to display dynamic subagents was tricky - this is what we aligned on

tutorialsharrison-chase--x

This post likely discusses technical decisions and design patterns for dynamically displaying subagents within an AI system, documenting the alignment the team reached after encountering implementatio

Simplify multi-account access to Amazon Bedrock models with managed entitlements

In this post, we show you how to use managed entitlements for Amazon Bedrock to subscribe once from a central account and distribute model access across your organization. This approach removes the ne

tutorialsaws-ml-blog

Simplifying Flow Matching Transformations with Low-Rank Mixture Models

arXiv:2606.29724v1 Announce Type: new Abstract: Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, ty

SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents

arXiv:2606.28434v1 Announce Type: cross Abstract: Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets. Existing memory m

T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation

arXiv:2606.30147v1 Announce Type: new Abstract: Recent progress in Text-to-Image generation benefits from large-scale Text-Image pairs. However, the scarcity of Text-LiDAR pairs often causes over-smoo

Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model

arXiv:2606.30104v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models aim to learn generalizable representations from large-scale brain recordings. However, the role of tempor

The Download: AI “coworkers” and stratospheric internet

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI agents are not your “coworkers” Imagine coming in to work t

tutorialsmit-tech-review

The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles

arXiv:2606.29720v1 Announce Type: cross Abstract: Resampling methods such as SMOTE and random under/over-sampling are standard tools for class-imbalanced classification, almost always evaluated by min

Traffic-CBM: A Structurally Interpretable Multimodal Framework for Encrypted Traffic Classification

arXiv:2606.29909v1 Announce Type: new Abstract: Encrypted traffic classification has achieved strong performance, but its decision process remains difficult to interpret. Existing methods usually comb

What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs

arXiv:2606.28615v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rati

When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning

arXiv:2601.07965v2 Announce Type: replace Abstract: When a model knows when it does not know, many possibilities emerge. The first question is how to enable a model to recognize that it does not know.

Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction

arXiv:2606.28544v1 Announce Type: cross Abstract: Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clus

A big reason that token costs become an issue in organizations is leadership is not making decisions about how to use AI, the processes that…

tutorialsethan-mollick--x

A big reason that token costs become an issue in organizations is leadership is not making decisions about how to use AI, the processes that need to change as a result, & how to think about multiplaye

A very rare deep dive into Runway's culture, technology and mission, or how to build new worlds.

tutorialscristobal-valenzuela--x

A very rare deep dive into Runway's culture, technology and mission, or how to build new worlds. Got to hang with the @runwayml folks during their film festival week, and pull back the curtain in toda

AI agents are not your “coworkers”

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Imagine coming in to work to learn that a new underling will r

tutorialsmit-tech-review

Apologies to those looking forward to @mattpocockuk's keynote, but he had to bow out for personal stuff... ... but he didn't leave us high a…

Apologies to those looking forward to @mattpocockuk's keynote, but he had to bow out for personal stuff... ... but he didn't leave us high and dry!!!!!!!!!! happy to announce our final and tenth onlin

tutorialsswyx--x

CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence

arXiv:2606.27683v1 Announce Type: cross Abstract: Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may

Deep Residual Networks Learn the Geodesic Curve in the Wasserstein Space

arXiv:2102.09235v3 Announce Type: replace Abstract: Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNN

DeLux: Cross-Modal Local Artifact Restoration in Video Using Neuromorphic Data

arXiv:2606.27576v1 Announce Type: new Abstract: Conventional RGB cameras suffer from lighting artifacts such as flare, glare, flicker, and overexposure, leading to irrecoverable information loss that

Developmental approach reveals the statistical learning of Neural Language Models: Transformers generalize from the most abstract statistical patterns

arXiv:2606.27460v1 Announce Type: new Abstract: In this study, we use a developmental approach to investigate the statistical learning and mental representation of neural language models (NLM). A seri

DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning

arXiv:2606.27410v1 Announce Type: cross Abstract: The primary goal of Remote Sensing Image Change Captioning (RSICC) is to automatically generate descriptions of changes between remote sensing images

Distributed Air-Gap Flux and Rotor-Current Fusion for Operating-Regime Identification in a 10-MW Kaplan Hydrogenerator

arXiv:2606.27800v1 Announce Type: cross Abstract: Reliable monitoring of hydroelectric generators requires descriptors that capture both electrical loading and electromagnetic field behavior. This wor

Explainable AI for Biodiversity Monitoring and Ecological Image Analysis

arXiv:2606.27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, dron

Flexformer: Flexible Linear Transformer with Learnable Attention Kernel

arXiv:2606.27748v1 Announce Type: cross Abstract: Transformer models rely on attention mechanism to capture long-range dependencies but suffer from quadratic complexity, limiting their scalability to

FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models

arXiv:2606.27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern larg

HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speech

arXiv:2606.28249v1 Announce Type: cross Abstract: Recently, Large Language Model (LLM)-based Text-to-Speech (TTS) models have achieved remarkable naturalness. However, the standard Supervised Fine-Tun

https://longcat.chat/blog/longcat-2.0/ People are missing out on how big a deal Longcat 2.0 by Meituan (aka 'Chinese Doordash') is. Near fro…

tutorialsjeremy-howard--x

https://longcat.chat/blog/longcat-2.0/ People are missing out on how big a deal Longcat 2.0 by Meituan (aka 'Chinese Doordash') is. Near frontier performance, trained on 50k Chinese domestic accelerat

Improving Adversarial Robustness via Activation Amplification and Attenuation

arXiv:2606.27784v1 Announce Type: cross Abstract: The existence of adversarial attacks is often attributed to the presence of non-robust features in neural networks. While prior defenses reduce their

Latent Visual Diffusion Reasoning with Monte Carlo Tree Search

arXiv:2606.27988v1 Announce Type: new Abstract: Analyzing fine-grained skill activities (e.g., sports, surgery) requires not only recognizing visual patterns but also performing step-by-step visual re

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation

arXiv:2606.28268v1 Announce Type: cross Abstract: Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for

LLM-as-a-Judge explained in ~10 mins. Knowing how to build AI verifiers and judges is one of the most important emerging AI skills today. He…

LLM-as-a-Judge explained in ~10 mins. Knowing how to build AI verifiers and judges is one of the most important emerging AI skills today. Here is a quick intro on the topic and where to learn how to a

tutorialsdair-ai--x

MASS: Motion-Aligned Selective Scan for Refinement in Flow-Based Video Frame Interpolation

arXiv:2606.27718v1 Announce Type: new Abstract: Video frame interpolation (VFI) remains a challenging task, particularly when dealing with large, non-linear motions and complex occlusions. While flow-

Masterclass deep-dive into programmatic spawning of subagents - highly encourage taking a look! @colifran_ dropping elite ball knowledge per…

tutorialsharrison-chase--x

Masterclass deep-dive into programmatic spawning of subagents - highly encourage taking a look! @colifran_ dropping elite ball knowledge per usual this blog covers 6 common workflow patterns we see wi

MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis

arXiv:2602.17901v3 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimens

On the Effect of Uncertainty on Layer-wise Inference Dynamics

arXiv:2507.06722v2 Announce Type: replace Abstract: Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing

PairSAE: Mechanistic Interpretability from Pair Representations in Protein Co-Folding

arXiv:2606.27440v1 Announce Type: new Abstract: Foundation models for structural biology have achieved remarkable performance in predicting biomolecular structure and show promise for the design of pr

PhysChoreo: Physics-Controllable Video Generation with Part-Aware Semantic Grounding

arXiv:2511.20562v2 Announce Type: replace Abstract: While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability

Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte

arXiv:2606.28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery

RAE-NWM: Navigation World Model in Dense Visual Representation Space

arXiv:2603.09241v2 Announce Type: replace Abstract: Visual navigation requires agents to reach goals in complex environments through perception and planning. World models address this task by simulati

Relating Reinforcement Learning to Dynamic Programming-Based Planning

arXiv:2603.07844v2 Announce Type: replace Abstract: This paper bridges some of the gap between optimal planning and reinforcement learning (RL), both of which share roots in dynamic programming applie

tutorialsarxiv-cs-ro

Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction

arXiv:2502.00168v5 Announce Type: replace-cross Abstract: Supervised dimensionality reduction maps labeled data into a low-dimensional feature space while preserving class separation. A common strateg

Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation

arXiv:2606.27681v1 Announce Type: cross Abstract: World models in partially observed environments rely on latent representations that summarize interaction history, but in many modern LLM-based archit

Think tank games out how to respond to disaster scenarios in space warfare

A think tank conducted research examining how the Space Force should respond to space warfare scenarios, with experts envisioning various ways satellites and spacecraft might be used in gray-zone or w

tutorialsars-technica

this blog covers 6 common workflow patterns we see with dynamic subagents (fanout and synthesize, adversarial verification, etc) if you'd pr…

tutorialsharrison-chase--x

this blog covers 6 common workflow patterns we see with dynamic subagents (fanout and synthesize, adversarial verification, etc) if you'd prefer to learn w/ a video, check out this excellent guide fro

Token spend is not just about productivity, it also organizational R&D, an investment in figuring how to rebuild around AI that will involve…

tutorialsethan-mollick--x

Token spending should be understood as organizational research and development rather than purely a productivity cost, representing an investment in discovering how to restructure business processes a