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Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

arXiv:2606.20477v2 Announce Type: replace Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a lar

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Tutorials

Selective Ensemble Based on Preference-Directed Multi-Objective Bandits

DGX agent

arXiv:2606.21929v1 Announce Type: new Abstract: Selective ensemble for modern machine learning systems requires choosing promising model candidates under limited evaluation budgets, while downstream t

tutorialsarxiv-cs-lg

23 Jun 2026

Sparse Point-Guided Fusion of Supervised and Self-Supervised Learning Model for Seaweed Segmentation

arXiv:2606.21026v1 Announce Type: new Abstract: The ocean plays a critical role in sustainable development, particularly in climate change mitigation. Among marine ecosystems, blue carbon ecosystems a

Spectral GS-SLAM: Observability-Aware, Degeneracy-Robust Tracking for Real-Time 3D Gaussian Splatting SLAM

arXiv:2606.21258v1 Announce Type: cross Abstract: Recent 3DGS-SLAM systems enable real-time operation by leveraging conventional feature matching or ICP-based tracking, thereby avoiding the heavy dens

Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space

arXiv:2606.21705v1 Announce Type: new Abstract: Dataset distillation (DD) has proven to reduce training cost while preserving accuracy. While promising, the factors that make one distilled dataset mor

The New Associationism: Lessons from Deep Learning

arXiv:2606.20600v1 Announce Type: cross Abstract: What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a m

VT-DUDA: Visual Token Conditioning for Diffusion-guided Unsupervised Domain Adaptation

arXiv:2606.21700v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shif

What Do Lorentz-Equivariant Jet Taggers Learn?

arXiv:2606.21790v1 Announce Type: new Abstract: We study what Lorentz-equivariant jet taggers learn internally, using equivariance tests, linear probes and grade ablations across five models including

What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study

arXiv:2606.22020v1 Announce Type: cross Abstract: Neural networks outperform classical GCC-PHAT for Time-Difference-of-Arrival (TDOA) estimation in noise and reverberation, yet their internal strategy

What Does a Chemical Language Model Know About Molecules?

arXiv:2606.23443v1 Announce Type: new Abstract: Chemical language models (cLMs) are widely assumed to learn surface-level syntactic patterns rather than learning meaningful molecular semantics. Here,

Words as Difference Makers: How Large Language Models Determine Causal Structure in Text

arXiv:2606.22430v1 Announce Type: cross Abstract: Because large language models (LLMs) are impressively successful in predicting text, it appears that they must have access to a 'world model' represen

Accurate and Resource-Efficient Federated Continual Learning

arXiv:2606.11480v1 Announce Type: new Abstract: Federated continual learning (FCL) must learn from distributed task streams under limited resources, such as communication, computation, memory, and lab

AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction

arXiv:2606.12126v1 Announce Type: new Abstract: Existing computational pathology methods predominantly operate within whole-slide image (WSI)-level multiple instance learning (MIL) paradigms, while pa

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal

arXiv:2606.12360v1 Announce Type: new Abstract: Language-model post-training is the main stage at which model behavior is shaped, yet it still largely involves optimization of scalar rewards that summ

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching

arXiv:2606.11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs

CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

arXiv:2605.06100v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) positioning is widely used for urban navigation, but the covariance reported by the GNSS solver is o

DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics

arXiv:2606.11651v1 Announce Type: new Abstract: Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials. These

DepthMaster: Unified Monocular Depth Estimation for Perspective and Panoramic Images

arXiv:2606.12368v1 Announce Type: new Abstract: While monocular depth estimation has achieved significant progress, achieving generalized metric depth estimation for both narrow field-of-view (FoV) pe

FitVTON: Fit-aware Virtual Try-On via Body-Garment Size Control

arXiv:2606.12012v1 Announce Type: new Abstract: While diffusion-based virtual try-on has achieved impressive visual realism, most methods treat the task as 2D inpainting, prioritizing texture preserva

FOCUS: DLLMs Know How to Tame Their Compute Bound

arXiv:2601.23278v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto-Regressive models, but their deployment is constrained by high

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

arXiv:2606.12182v1 Announce Type: new Abstract: Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches

Information-Theoretic Decomposition for Multimodal Interaction Learning

arXiv:2606.11614v1 Announce Type: cross Abstract: Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal int

Integral Formulation of QENDy for Robust Nonlinear System Identification

arXiv:2606.11629v1 Announce Type: cross Abstract: This manuscript proposes an integral formulation of the newly defined quadratic embedding method for identifying nonlinear systems (QENDy). In the ori

Learning Patterns and Abstractions from Perceptual Sequences

arXiv:2503.10973v2 Announce Type: replace Abstract: Cognition swiftly breaks high-dimensional sensory streams into familiar parts and uncovers their relations. Why do structures emerge, and how do the

Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems

arXiv:2606.11251v1 Announce Type: new Abstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden. Existing appro

Nonslop: A Gamified Experiment in Human-AI Collaborative Writing

arXiv:2606.12350v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) raises critical questions about human creativity and individual expression in an era of AI-assis

Planning under Distribution Shifts with Causal POMDPs

arXiv:2602.23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

arXiv:2504.09762v4 Announce Type: replace Abstract: Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of l

Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network

arXiv:2606.11663v1 Announce Type: cross Abstract: Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches

Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning

arXiv:2606.12211v1 Announce Type: cross Abstract: A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expr

Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study

arXiv:2606.12231v1 Announce Type: cross Abstract: The adoption of AI-powered Integrated Development Environments (AI IDEs) has introduced 'Rules' as a novel software artifact, allowing developers to p

SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing

arXiv:2606.11424v1 Announce Type: new Abstract: Natural language interfaces to databases aim to translate user questions into executable SQL, yet remain brittle in real-world settings where questions

TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data

arXiv:2606.11844v1 Announce Type: new Abstract: Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schema

TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animation

arXiv:2606.12153v1 Announce Type: new Abstract: The explosion of generative 3D assets has created a massive demand for animation, yet current motion capture methods remain brittle, restricted to speci

When is Your LLM Steerable?

arXiv:2606.11599v1 Announce Type: new Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depen

A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems

arXiv:2606.10782v1 Announce Type: cross Abstract: The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and ava

A Note on the Strategic Confinement Problem

arXiv:2606.09931v1 Announce Type: cross Abstract: Lampson's confinement problem asks how to prevent a program that processes confidential information from leaking it to a third party. We introduce the

A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design

arXiv:2606.11189v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory. However, an observed token can be non-uni

Aesthetic Perspectives in Information Systems Research: A Hermeneutic Analysis

arXiv:2606.09839v1 Announce Type: cross Abstract: How might implicit aesthetic perspectives shape what Information Systems (IS) scholarship recognises as worthy of study (or not)? In this hermeneutic

An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration

arXiv:2606.10200v1 Announce Type: cross Abstract: An improved GAN-based imaging logging image restoration method is presented in this paper for solving the problem of partially missing micro-resistivi

Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions

arXiv:2409.02426v5 Announce Type: replace-cross Abstract: Despite their empirical success across a wide range of generative tasks, the fundamental principles underlying the ability of diffusion models

Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images

arXiv:2606.10328v1 Announce Type: cross Abstract: The integration of spatial and spectral information is beneficial to the improvement of change detection performance. However, existing methods cannot

Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

arXiv:2606.10796v1 Announce Type: cross Abstract: Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two

Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News

arXiv:2606.11116v1 Announce Type: cross Abstract: As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust. C

DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

arXiv:2606.11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cos

Entropy, Disagreement, and the Limits of Foundation Models in Genomics

arXiv:2604.04287v2 Announce Type: replace-cross Abstract: Foundation models in genomics have shown mixed success compared to their counterparts in natural language processing. Yet, the reasons for the

Fast Exact Nearest-Neighbor Learning for High-Frequency Financial Time Series

arXiv:2606.10219v1 Announce Type: cross Abstract: AI efficiency at scale is becoming critical in finance as market data volumes surge across equities, ETFs, FX, options, and high-frequency trading str

GeoLoom: High-quality Geometric Diagram Generation from Textual Input

arXiv:2512.08180v2 Announce Type: replace Abstract: High-quality geometric diagram generation presents both a challenge and an opportunity: it demands strict spatial accuracy while offering well-defin