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GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models

arXiv:2605.01829v1 Announce Type: new Abstract: Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Stand

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Tutorials

GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models

DGX agent

arXiv:2605.01256v1 Announce Type: new Abstract: A promising paradigm for adapting instruction-tuned language models is to learn task-specific updates on a pretrained base model and subsequently merge

tutorialsarxiv-cs-cl

5 May 2026

Inducing Permutation Invariant Priors in Bayesian Optimization for Carbon Capture and Storage Applications

arXiv:2605.02409v1 Announce Type: new Abstract: Bayesian Optimization is an iterative method, tailored to optimizing expensive black box objective functions. Surrogate models like Gaussian Processes,

Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography

arXiv:2605.00882v1 Announce Type: new Abstract: Remote Photoplethysmography (rPPG) enables convenient non-contact physiological measurement. Existing Self-Supervised Learning (SSL) methods commonly fa

Isotropic Fourier Neural Operators

arXiv:2605.02597v1 Announce Type: new Abstract: Fourier Neural Operators are deep learning models that learn mappings between function spaces and can be used to learn and solve partial differential eq

Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM

arXiv:2605.01973v1 Announce Type: new Abstract: Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, appli

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation

arXiv:2509.10468v2 Announce Type: replace-cross Abstract: Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenize

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

arXiv:2605.02699v1 Announce Type: cross Abstract: Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is

Learning Koopman operators for coupled systems via information on governing equations of subsystems

arXiv:2605.01835v1 Announce Type: new Abstract: Nonlinear coupled systems are ubiquitous in science and engineering. The analysis and modeling of such systems is challenging due to their high dimensio

Learning to Race in Minutes: Infoprop Dyna on the Mini Wheelbot

arXiv:2605.01096v1 Announce Type: new Abstract: Reinforcement Learning (RL) has the potential to enable robots with fast, nonlinear, and unstable dynamics to reach the limits of their performance. How

Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusion

arXiv:2510.06516v2 Announce Type: replace Abstract: Limited-angle electron tomography aims to reconstruct 3D shapes from 2D projections of Transmission Electron Microscopy (TEM) within a restricted ra

Measuring AI Reasoning: A Guide for Researchers

arXiv:2605.02442v1 Announce Type: cross Abstract: In this paper, we offer a guide for researchers on evaluating reasoning in language models, building the case that reasoning should be assessed throug

MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation

arXiv:2605.01369v1 Announce Type: cross Abstract: Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a pr

Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation

arXiv:2605.02471v1 Announce Type: new Abstract: Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveragin

On the Characterization and Limits of 4D Radar for Aided Inertial Navigation

arXiv:2605.01773v1 Announce Type: new Abstract: Frequency Modulated Continuous Wave (FMCW) radar is a promising sensor for aided inertial navigation, due to its robustness in environments that challen

tutorialsarxiv-cs-ro

Online Generalised Predictive Coding

arXiv:2605.02675v1 Announce Type: cross Abstract: This paper introduces an extension of generalised filtering for online applications. Generalised filtering refers to data assimilation schemes that jo

PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning

arXiv:2603.26816v2 Announce Type: replace Abstract: High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcemen

Provably Learning Attention with Queries

arXiv:2601.16873v2 Announce Type: replace Abstract: We study the problem of learning Transformer-based sequence models with black-box access to their outputs. In this setting, a learner may adaptively

RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions

arXiv:2605.01104v1 Announce Type: cross Abstract: Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing

Rethinking Electro-Optical Vision Foundation Models for Remote Sensing Retrieval: A Controlled Comparison with Generalist VFM

arXiv:2605.02283v1 Announce Type: new Abstract: Vision foundation models have attracted significant attention for their ability to leverage large-scale unlabeled visual data. This advantage is particu

Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model

arXiv:2605.02604v1 Announce Type: new Abstract: Source-Free Domain Adaptation (SFDA) adapts source models to target domains without accessing source data, addressing privacy and transmission issues. H

SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models

arXiv:2603.22097v2 Announce Type: replace-cross Abstract: Foundation models are now increasingly being developed for Earth observation (EO), yet they often rely on stochastic masking that do not expli

Stable GFlowNets with Probabilistic Guarantees

arXiv:2605.01729v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward. Despite their theoretical promise, practical trainin

TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification

arXiv:2605.02558v1 Announce Type: new Abstract: Accurate badminton stroke prediction is crucial for fine-grained sports analysis and tactical decision support. However, existing methods struggle to mo

Time-series forecasting through the lens of dynamics

arXiv:2507.15774v2 Announce Type: replace Abstract: While deep learning is facing an homogenization across modalities led by Transformers, they are still challenged by shallow linear models in the tim

TrajRAG: Retrieving Geometric-Semantic Experience for Zero-Shot Object Navigation

arXiv:2605.01700v1 Announce Type: new Abstract: Existing zero-shot Object Goal Navigation (ObjectNav) methods often exploit commonsense knowledge from large language or vision-language models to guide

U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning

arXiv:2605.02765v1 Announce Type: cross Abstract: LLMs are increasingly used for end-user task planning, yet their black-box nature limits users' ability to ensure reliability and control. While recen

Video Generation with Predictive Latents

arXiv:2605.02134v1 Announce Type: new Abstract: Video Variational Autoencoder (VAE) enables latent video generative modeling by mapping the visual world into compact spatiotemporal latent spaces, impr

XekRung Technical Report

arXiv:2605.00072v1 Announce Type: cross Abstract: We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we de

tutorialsarxiv-cs-ai

Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement

arXiv:2507.18064v2 Announce Type: replace Abstract: Most existing low-light image enhancement (LLIE) methods rely on pre-trained model priors, low-light inputs, or both, while neglecting the semantic

Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay

arXiv:2605.00374v1 Announce Type: new Abstract: Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overloo

Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization

arXiv:2605.00641v1 Announce Type: new Abstract: Both Dimensionality Reduction (DR) and Graph Drawing (GD) aim to visualize abstract, non-linear structures, yet rely on different optimization paradigms

DeGenTWeb: A First Look at LLM-dominant Websites

arXiv:2605.00087v1 Announce Type: cross Abstract: Many recent news reports have claimed that content generated by large language models (LLMs) is taking over the web. However, these claims are typical

Doubly robust identification of treatment effects from multiple environments

arXiv:2503.14459v2 Announce Type: replace-cross Abstract: Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social scienc

Efficient Mutation Testing of Quantum Machine Learning Models

arXiv:2605.00107v1 Announce Type: cross Abstract: Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer par

Latent Generative Modeling of Random Fields from Limited Training Data

arXiv:2505.13007v2 Announce Type: replace Abstract: The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying q

Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization

arXiv:2605.00130v1 Announce Type: new Abstract: Learning meaningful representations from medical time series (MedTS) such as ECG or EEG signals is a critical challenge. These signals are often high-di

MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video

arXiv:2605.00242v1 Announce Type: new Abstract: Millimetre-wave (mmWave) radar offers a more privacy-preserving alternative to RGB-based human pose estimation. However, existing methods typically rely

Predictive Spatio-Temporal Scene Graphs for Semi-Static Scenes

arXiv:2605.00121v1 Announce Type: new Abstract: We have seen tremendous recent progress in our ability to build 'spatio-semantic' representations that enable robots to perform complex reasoning across

tutorialsarxiv-cs-ro

Structured In-context Environment Scaling for Large Language Model Reasoning

arXiv:2509.23330v3 Announce Type: replace Abstract: Large language models (LLMs) have achieved significant advancements in reasoning capabilities through reinforcement learning (RL) via environmental

What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics

arXiv:2605.00294v1 Announce Type: new Abstract: This study presents a systematic approach to identifying and characterizing student misconceptions in online learning environments through a novel combi

When Do Diffusion Models learn to Generate Multiple Objects?

arXiv:2605.00273v1 Announce Type: new Abstract: Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical ev

A generalised pre-training strategy for deep learning networks in semantic segmentation of remotely sensed images

arXiv:2604.27704v1 Announce Type: new Abstract: In the segmentation of remotely sensed images, deep learning models are typically pre-trained using large image databases like ImageNet before fine-tune

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space

arXiv:2604.27443v1 Announce Type: cross Abstract: Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., first a

tutorialsarxiv-cs-ai

Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements

arXiv:2604.28173v1 Announce Type: new Abstract: Effective human behavior modeling requires a representation of the human body movement that capitalizes on its compositionality. We propose a hierarchic

Constraint-Aware Flow Matching via Randomized Exploration

arXiv:2508.13316v2 Announce Type: replace Abstract: We consider the problem of designing constraint-aware flow matching (FM) models that address the issue of constraint violations commonly observed in

Coverage-Aware Web Crawling for Domain-Specific Supplier Discovery via a Web--Knowledge--Web Pipeline

arXiv:2602.24262v4 Announce Type: replace Abstract: Identifying the full landscape of small and medium-sized enterprises (SMEs) in specialized industry sectors is critical for supply-chain resilience,

Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining

arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec

tutorialsarxiv-cs-ai