Search: “tutorials”

Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations

arXiv:2602.01456v2 Announce Type: replace-cross Abstract: Joint-Embedding Predictive Architectures (JEPA) learn view-invariant representations and admit projection-based distribution matching for coll

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Tutorials

Robust Cross-Domain Generalization Using Unlabeled Target Data with Source-Domain Supervision

DGX agent

arXiv:2605.29122v1 Announce Type: new Abstract: It is often desirable to generalize medical imaging AI models trained with dense annotations to data acquired from different ultrasound scanners or clin

tutorialsarxiv-cs-cv

29 May 2026

Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation

arXiv:2602.21565v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific d

Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals

arXiv:2605.29098v1 Announce Type: new Abstract: Reconstructing object geometry from radio frequency (RF) signals is fundamentally challenging due to the lensless imaging nature of RF sensing, which le

SRUG: Shadow-Guided Relightable Urban Scene with Generation Model

arXiv:2605.24700v2 Announce Type: replace Abstract: Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend

Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI

arXiv:2605.29240v1 Announce Type: new Abstract: AI-augmented classrooms generate rich teacher and student feedback before graded outcomes become available, yet these signals can be difficult to transl

Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement Planning

arXiv:2605.28842v1 Announce Type: cross Abstract: The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in

Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection

arXiv:2605.29455v1 Announce Type: new Abstract: Multi-modal industrial anomaly detection typically relies on separate models for each product category, fundamentally limiting practical scalability. Wh

What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies

arXiv:2603.02082v2 Announce Type: replace Abstract: Children's acquisition of filler-gap dependencies has been argued by some to depend on innate grammatical knowledge, while others suggest that the d

When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models

arXiv:2603.23085v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing

Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention

arXiv:2605.29548v1 Announce Type: new Abstract: Larger models learn tasks smaller models do not. What drives this phenomenon? We develop a simple phenomenological argument that power-law scaling alrea

Zero-shot CT Super-Resolution using Diffusion-based 2D Projection Priors and Signed 3D Gaussians

arXiv:2508.15151v3 Announce Type: replace-cross Abstract: Computed tomography (CT) is important in clinical diagnosis, but acquiring high-resolution (HR) CT is constrained by radiation exposure risks.

Adaptive Sampling and Clipping for Private Worst-Case Group Optimization

arXiv:2602.10820v2 Announce Type: replace Abstract: A central requirement for the acceptance of machine learning methods for human-centric tasks is that they should be fair, in the sense that they sho

Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting

arXiv:2605.27668v1 Announce Type: cross Abstract: Probabilistic forecasting estimates the likelihood of uncertain future events. To improve LLM forecasting, existing methods typically learn from binar

Automatic Pruning Discovery for Large Language Models

arXiv:2511.15390v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance on a wide range of tasks, hindering real-world deployment due to their massive siz

C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning

arXiv:2605.27860v1 Announce Type: new Abstract: Retrieval-augmented generation combined with reinforcement learning has shown promise for grounding large language models in trustworthy medical evidenc

Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval

arXiv:2605.27449v1 Announce Type: cross Abstract: In the field of multimodal fact checking, the accuracy of retrieving evidence from different modalities has a significant impact on the downstream cla

CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenes

arXiv:2605.28125v1 Announce Type: new Abstract: Many real-world 3D reconstruction applications demand photorealism and metric accuracy across unbounded, complex scenes with challenging lighting and im

Continuous Diffusion Models Can Obey Formal Syntax

arXiv:2602.12468v2 Announce Type: replace Abstract: Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their conti

D^2Turb: Depth-Aware Simulation and Decoupled Learning for Single-Frame Atmospheric Turbulence Mitigation

arXiv:2605.27460v1 Announce Type: new Abstract: Single-frame atmospheric turbulence mitigation is inherently ill-posed due to spatially varying blur coupled with non-rigid geometric distortion. Existi

DiagramRAG: A Lightweight Framework to Retrieve Scientific Diagram for Figure Generation

arXiv:2605.27931v1 Announce Type: new Abstract: Scientific diagrams are essential for communicating complex methodologies in academic papers. A natural way for researchers to specify such diagrams is

Evaluation of AI Ethics Tools in Language Models: A Developers' Perspective Case Study

arXiv:2512.15791v2 Announce Type: replace-cross Abstract: In Artificial Intelligence (AI), language models have gained significant importance due to the widespread adoption of systems capable of simul

Formula-One Prompting: A Composable Equation-First Prefix for Applied Mathematics

arXiv:2601.19302v3 Announce Type: replace Abstract: This paper introduces Formula Prompting (FP) and Formula-One Prompting (F-1), two single-call methods that elicit governing equations before solving

From Talking to Singing: A New Challenge for Audio-Visual Deepfake Detection

arXiv:2605.27944v1 Announce Type: new Abstract: With rapid advances in audio-visual generative models, reliable forgery detection becomes increasingly critical. Existing methods for audio-visual deepf

Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition

arXiv:2504.10079v4 Announce Type: replace Abstract: Few-shot action recognition (FSAR) aims to recognize novel action categories with few exemplars. Existing methods typically learn frame-level repres

Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization

arXiv:2605.28802v1 Announce Type: new Abstract: Free-text explanations extend human label variation (HLV) beyond label disagreement by revealing the reasoning and preferences behind annotators' decisi

Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions

arXiv:2605.27523v1 Announce Type: cross Abstract: Deep generative models offer powerful tools for multivariate data analysis, but their black-box architectures are often unidentified and difficult to

Inversely Learning Transferable Rewards via Abstracted States

arXiv:2501.01669v4 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous

Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies

arXiv:2605.27546v1 Announce Type: new Abstract: Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth di

Learn from your own latents and not from tokens: A sample-complexity theory

arXiv:2605.27734v1 Announce Type: new Abstract: Generative models, from diffusion models to large language models, achieve remarkable performance but at a cost in training data orders of magnitude lar

Learning a Kinodynamic Trajectory Manifold for Impact-Aware Compliant Catching of Fast-Moving Objects

arXiv:2605.28462v1 Announce Type: new Abstract: Fast catching of free-flying objects is difficult because of short reaction time, impact uncertainty, and kinodynamic constraints. We use reinforcement

tutorialsarxiv-cs-ro

Learning Correlated Reward Models: Statistical Barriers and Opportunities

arXiv:2510.15839v2 Announce Type: replace Abstract: Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learni

Learning the Error Patterns of Language Models

arXiv:2605.28328v1 Announce Type: cross Abstract: When generating outputs for domains with specific validity constraints (e.g., a program should compile), LLMs often fail in a small number of focused

Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization

arXiv:2605.28309v1 Announce Type: new Abstract: In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but

Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging

arXiv:2602.03855v2 Announce Type: replace-cross Abstract: Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based a

Measure-to-measure Regression with Transformers

arXiv:2605.28075v1 Announce Type: new Abstract: Many learning problems require predicting how populations evolve under an unknown transformation. A natural representation for such populations is a pro

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

arXiv:2605.28693v1 Announce Type: cross Abstract: Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains h

Neural Image Space Tessellation efect

arXiv:2602.23754v2 Announce Type: replace-cross Abstract: We present Neural Image Space Tessellation effect (NIST), a lightweight screen-space post-processing approach for reducing the faceted silhoue

PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation

arXiv:2605.28634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising paradigm for generalist robotic policies, yet their adaptation is hindered by data inefficiency an

tutorialsarxiv-cs-ro

Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields

arXiv:2605.28625v1 Announce Type: new Abstract: Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussi

Revealing Algorithmic Deductive Circuits for Logical Reasoning

arXiv:2605.27824v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models (LLMs) can achieve strong reasoning performance by incorporating functional symbolic representation

SAFEVPR: Patch-Based Conformal Verification for Safe Cross-Condition Sequence Visual Place Recognition

arXiv:2605.28048v1 Announce Type: new Abstract: Sequence-based visual place recognition (VPR) for SLAM and robot relocalization must decide whether the retrieved top-1 candidate is safe to accept. Con

tutorialsarxiv-cs-ro

Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization

arXiv:2605.28394v1 Announce Type: new Abstract: Animation of 2D hand-drawn sketches provides an effective medium for visual communication. However, these sketches pose challenges, particularly in hand

The Principles of Diffusion Models

arXiv:2510.21890v2 Announce Type: replace-cross Abstract: This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse for

Transformers Provably Learn to Internalize Chain-of-Thought

arXiv:2605.28600v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning fro

Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models

arXiv:2605.28132v1 Announce Type: new Abstract: Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this,

Why Gaussian Diffusion Models Fail on Discrete Data and How to Prevent It?

arXiv:2604.02028v2 Announce Type: replace Abstract: Diffusion models have become a standard approach for generative modeling in continuous domains, yet their application to discrete data remains chall

Adversarial Water-Filling: Theory, Algorithms and Foundation Model

arXiv:2605.26163v1 Announce Type: cross Abstract: Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case inter