tutorials

From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting

arXiv:2605.07203v1 Announce Type: new Abstract: Scene change detection methods built on Gaussian splatting universally follow a render-then-compare paradigm: the pre-change scene is rendered into 2D a

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How to Train Your Latent Diffusion Language Model Jointly With the Latent Space

DGX agent

arXiv:2605.07933v1 Announce Type: new Abstract: Latent diffusion models offer an attractive alternative to discrete diffusion for non-autoregressive text generation by operating on continuous text rep

tutorialsarxiv-cs-cl

11 May 2026

Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language models

arXiv:2508.05803v2 Announce Type: replace Abstract: Human memory is fleeting. As words are processed, the exact wordforms that make up incoming sentences are rapidly lost. Cognitive scientists have lo

HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs

arXiv:2506.12362v3 Announce Type: replace-cross Abstract: Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., n

I'm delighted that @coursera and @udemy have come together as one company to serve learners. Both Coursera and Udemy were founded with the b…

I'm delighted that @coursera and @udemy have come together as one company to serve learners. Both Coursera and Udemy were founded with the belief that access to high-quality education changes lives. O

tutorialsandrew-ng--x

Implicit Multi-Camera System Calibration Using Gaussian Processes

arXiv:2605.07491v1 Announce Type: new Abstract: This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit cal

Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention

arXiv:2605.07010v1 Announce Type: new Abstract: Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure

Learning Large-Scale Modular Addition with an Auxiliary Modulus

arXiv:2605.07648v1 Announce Type: new Abstract: Learning parity functions, more general modular addition, is a challenging machine learning task due to its input sensitivity. A recent study substantia

Learning quantum Hamiltonians at any temperature in polynomial time

arXiv:2310.02243v2 Announce Type: replace-cross Abstract: We study the problem of learning a local quantum Hamiltonian H given copies of its Gibbs state rho = e^{-eta H}/extrm{tr}(e^{-eta H}) at a kno

LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

arXiv:2605.06676v1 Announce Type: cross Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compress

// LLMs Improving LLMs // Interesting progress the past of couple of weeks around self-improving AI agents. If autoresearch was interesting,…

// LLMs Improving LLMs // Interesting progress the past of couple of weeks around self-improving AI agents. If autoresearch was interesting, you will like this read. (bookmark it) We've been hand-tuni

tutorialsdair-ai--x

LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute

arXiv:2605.06809v1 Announce Type: new Abstract: Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are

Manifold Random Features

arXiv:2602.03797v2 Announce Type: replace Abstract: We present a new paradigm for creating random features to approximate bi-variate functions (in particular, kernels) defined on general manifolds. Th

NavOne: One-Step Global Planning for Vision-Language Navigation on Top-Down Maps

arXiv:2605.06317v2 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulatio

Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition

arXiv:2605.07140v1 Announce Type: cross Abstract: Skeleton-based human activity recognition has achieved strong empirical performance, yet most existing models remain black boxes and difficult to inte

Not All Tokens Learn Alike: Attention Entropy Reveals Heterogeneous Signals in RL Reasoning

arXiv:2605.07660v1 Announce Type: new Abstract: Reinforcement-learning-based post-training has become a key approach for improving the reasoning ability of large language models, but its token-level l

One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators

arXiv:2605.06873v1 Announce Type: cross Abstract: Probabilistic conditioning is concerned with the identification of a distribution of a random variable X given a random variable Y. It is a cornerston

Open-Ended Task Discovery via Bayesian Optimization

arXiv:2605.07572v1 Announce Type: new Abstract: When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what t

PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks

arXiv:2605.06833v1 Announce Type: cross Abstract: Misbehavior detection in Vehicle-to-Everything (V2X) networks is a second line of defense against insider falsification attacks that cryptographic mec

Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network

arXiv:2602.03490v2 Announce Type: replace Abstract: Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such w

PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting

arXiv:2605.08035v1 Announce Type: cross Abstract: Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches

Prototype Guided Post-pretraining for Single-Cell Representation Learning

arXiv:2605.07938v1 Announce Type: new Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular function.

Rethinking State Tracking in Recurrent Models Through Error Control Dynamics

arXiv:2605.07755v1 Announce Type: cross Abstract: The theory of state tracking in recurrent architectures has predominantly focused on expressive capacity: whether a fixed architecture can theoretical

Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability

arXiv:2605.07212v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained a

Searching for Birds with Pinecone Full-Text Search

This tutorial demonstrates how to implement full-text search functionality using Pinecone's vector database, using bird-related data as a practical example. It likely covers setting up Pinecone, index

tutorialspinecone

SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning

arXiv:2602.03201v3 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of

Statistical inference with belief functions: A survey

arXiv:2605.07908v1 Announce Type: cross Abstract: Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack

Structure learning of Hamiltonians from real-time evolution

arXiv:2405.00082v4 Announce Type: replace-cross Abstract: We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply e^{-i Ht} for an unknown local Ham

Structured Coupling for Flow Matching

arXiv:2605.07676v1 Announce Type: new Abstract: Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent struc

Tables Guide Vision: Learning to See the Heart through Tabular Data

arXiv:2503.14998v3 Announce Type: replace Abstract: Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align

Task Relevance Is Not Local Replaceability: A Two-Axis View of Channel Information

arXiv:2605.07086v1 Announce Type: new Abstract: Channel importance in vision networks is usually summarized by a single score. That summary hides two different questions: how much a channel is related

// The Memory Curse in LLM Agents // (bookmark it) Long histories apparently degrades agents as they become increasingly history-following a…

// The Memory Curse in LLM Agents // (bookmark it) Long histories apparently degrades agents as they become increasingly history-following and risk-minimizing. Across 7 LLMs and 4 social dilemma games

tutorialsdair-ai--x

This is a tutorial on diffusion and flow matching, based on my previous postings here. I’ve made available the PDF, a python notebook for pe…

tutorialsemad-mostaque--x

This is a tutorial on diffusion and flow matching, based on my previous postings here. I’ve made available the PDF, a python notebook for people to play with it, and the TEX source so hopefully one of

Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics

arXiv:2605.07471v1 Announce Type: new Abstract: Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fa

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders

arXiv:2605.07922v1 Announce Type: new Abstract: Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues l

TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses

arXiv:2509.22813v2 Announce Type: replace Abstract: State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architectur

TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story. So we ran what I believe is the fir…

TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story. So we ran what I believe is the first comprehensive study of TurboQuant: where it helps, where

Uncovering Hidden Systematics in Neural Network Models for High Energy Physics

arXiv:2605.07470v1 Announce Type: new Abstract: Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flex

UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection

arXiv:2605.07146v1 Announce Type: new Abstract: Underwater salient object detection (USOD) plays a vital role in marine vision tasks but remains fundamentally challenging due to severe visual degradat

VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation

arXiv:2508.06819v3 Announce Type: replace Abstract: The task of parsing subcutaneous vessels in clinical images is often hindered by the high cost and limited availability of ground truth data, as wel

When Symbol Names Should Not Matter: A Logistic Theory of Fresh-Symbol Classification

arXiv:2605.07120v1 Announce Type: new Abstract: Template tasks have emerged as a clean testbed for asking whether transformers reason with abstract symbols rather than concrete token names. We study t

Why DDIM Hallucinates More than DDPM: A Theoretical Analysis of Reverse Dynamics

arXiv:2605.06831v1 Announce Type: cross Abstract: We theoretically study the hallucination phenomena in two canonical diffusion samplers: the stochastic Denoising Diffusion Probabilistic Model (DDPM)

With the model's simultaneous speech capability, Horace has gotten a lot easier to work with recently.

The post discusses improvements in working with Horace (likely a tool or system) due to recent implementation of simultaneous speech capability in its underlying model. This advancement has made the i

Your AI Use Is Breaking My Brain

Your AI Use Is Breaking My Brain Excellent, angry piece by Jason Koebler on how AI writing online is becoming impossible to avoid, filtering it is mentally exhausting and it's even starting to distort

tutorialssimon-willison

As always, no insights and in personal capacity: The DeepMind unionization effort has very worthy goals it seems Maybe Google will finally g…

As always, no insights and in personal capacity: The DeepMind unionization effort has very worthy goals it seems Maybe Google will finally grant GDM that /independent/ ethics oversight board that was

10 May 2026

big 'Look what they need to mimic a fraction of our power' energy. the original DOOM impl is ~40k lines of C and a bit of assembly and is al…

big 'Look what they need to mimic a fraction of our power' energy. the original DOOM impl is ~40k lines of C and a bit of assembly and is also a full software renderer. My Codex /goal that has been ru

10 May 2026

If the AI models are so smart, why do I feel like I’m losing a few neurons every time I read a longer form content written by AI? We’ve come…

If the AI models are so smart, why do I feel like I’m losing a few neurons every time I read a longer form content written by AI? We’ve come a long way but we still have long way to go. In terms of cl

10 May 2026

Anthropic, OpenAI, and other AI firms met with Hindu, Sikh, and Greek Orthodox leaders to draft principles on how to infuse models with ethics and morality (Krysta Fauria/Associated Press)