Search: “pretraining”

An overview of 3D Vision-Language Models

arXiv:2609.05583v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are reshaping computer vision by aligning visual and textual embeddings, allowing models to recognize visual concepts and

BIFTA: Brain-Inspired Few-Shot Tactile Adaptation for Unknown Sensors

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DGX agent

arXiv:2609.08673v1 Announce Type: new Abstract: Advances in tactile sensing have made contact-rich perception possible, accelerating progress in robotic manipulation, material understanding, and embod

researcharxiv-cs-ro

9 Sept 2026

Tutorials

Capsule Lens: Locating and Tracking Concept Geometry in Model Representations

arXiv:2609.05575v1 Announce Type: cross Abstract: Understanding how concepts are encoded in the internal representations of machine learning models is a central problem in mechanistic interpretability

tutorialsarxiv-cs-cv

Disparity Has a Sign: Stereo Matching Beyond the Zero-Disparity Plane

model-releasesarxiv-cs-cv

arXiv:2609.06809v1 Announce Type: new Abstract: Modern stereo matching models fail when disparity crosses zero, with end-point error (EPE) rising by 4.6-37imes. Yet stereoscopic content, from cinema 3

Effects of model architecture and learning strategies on deep learning-based recognition of activated sludge microscopic images and comparison with quantitative image analysis

arXiv:2609.08570v1 Announce Type: new Abstract: Microscopic image analysis has long been recognized as a promising approach for monitoring activated sludge. In recent years, deep learning-based image

GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation

arXiv:2609.07006v1 Announce Type: new Abstract: Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guida

IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring

arXiv:2609.08375v1 Announce Type: new Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants. Current practice remains a one-task-one-mo

safetyarxiv-cs-lg

Linear Algebra Foundations of Efficient Attention: A Phase Reversal in Rank Collapse Under SVD Compression

arXiv:2609.06341v1 Announce Type: new Abstract: Linear algebra provides the framework of concepts (matrix rank, singular value decomposition (SVD), and eigendecomposition) that modern artificial intel

Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation

arXiv:2609.06165v1 Announce Type: new Abstract: Whole-body diffusion-weighted imaging (WB-DWI) is widely used for multiple myeloma (MM) assessment, yet automated lesion segmentation remains challengin

Prevalence calibration as shortcut mitigation

arXiv:2609.07922v1 Announce Type: cross Abstract: Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing miti

Reason Through the Latent! Making Latent Visual Reasoning Necessary

arXiv:2609.06746v1 Announce Type: cross Abstract: Latent visual reasoning aims to perform multimodal reasoning through hidden-state computation rather than explicit textual chains of thought. However,

MEOX: Compact Multimodal Mixture-of-Experts for Earth Observation

model-releasesarxiv-cs-cv

arXiv:2609.05351v1 Announce Type: new Abstract: Recent advances in Earth Observation representation learning accommodate heterogeneous sensors and missing observations, often through larger architectu

7 Sept 2026

Mitra-v2 Technical Report

model-releasesarxiv-cs-lg

arXiv:2609.04540v1 Announce Type: new Abstract: We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from

7 Sept 2026

Anonymization, Not Elimination: Utility-Preserved Speech Anonymization

arXiv:2604.17000v1 Announce Type: cross Abstract: The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degra

Causal Foundation Models

arXiv:2609.03003v1 Announce Type: new Abstract: Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for eve

Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Data

arXiv:2609.03391v1 Announce Type: cross Abstract: Contrastive language-image learning (CLIP) has become a key paradigm for remote sensing vision-language understanding. However, existing remote sensin

safetyarxiv-cs-ai

ProgResViT: Progressive Resolution and Width for Adaptive Vision Transformers

arXiv:2609.03216v1 Announce Type: new Abstract: Vision Transformers (ViTs) typically process every image using a fixed input resolution and model width, even though many images can be classified with

Tensor-based Brain Surface Modeling and Analysis

arXiv:2609.03302v1 Announce Type: new Abstract: We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape differences between two clinical groups bas

Text2Thermal: Physics-Aware Thermal Image Synthesis from Textual Priors

arXiv:2609.03585v1 Announce Type: new Abstract: Thermal infrared imaging offers reliable perception in darkness and adverse weather, but thermal datasets remain scarce, motivating extensive work on tr

VisCAD: A Foundation Model Suite with Multimodal Industrial CAD Intelligence

arXiv:2609.03811v1 Announce Type: cross Abstract: AI-assisted computer-aided design (CAD) for industrial products involves two challenging phases. Part-level generation maps diverse forms of user inte

researcharxiv-cs-cl

Entangled Representations Amplify Collateral Damage in Unlearning

model-releasesarxiv-cs-cl

arXiv:2609.02285v1 Announce Type: cross Abstract: A long-held intuition in interpretability research is that representational entanglement, the sharing of structure between knowledge domains in a neur

Applications

General Demographic Pre-trained Models for Enhancing Predictive Performance Across Diseases and Population

arXiv:2509.07330v3 Announce Type: replace-cross Abstract: Foundation models for healthcare require balancing robust generalization across heterogeneous clinical populations and disease settings with t

applicationsarxiv-cs-ai

HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design

arXiv:2609.02746v1 Announce Type: cross Abstract: Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made signi

Improved Automatic Target Recognition in Synthetic Aperture Sonar Imagery Using Large Deep Neural Networks

arXiv:2609.01800v1 Announce Type: new Abstract: Automatic Target Recognition (ATR) in Synthetic Aperture Sonar (SAS) is a task largely dominated by deep neural networks (DNNs). Most SAS-ATR models use

InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation

arXiv:2609.02004v1 Announce Type: cross Abstract: Accurate segmentation of polyps and skin lesions is pivotal for clinical diagnosis, yet existing methods struggle with low contrast, ambiguous boundar

Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization

model-releasesarxiv-cs-lg

arXiv:2609.02126v1 Announce Type: new Abstract: Estimating physical parameters from scientific images is a common inverse problem in materials characterization that often relies on expensive physics-b

The Dynamics of Continuous Mixture Collapse in Language Models

arXiv:2609.02049v1 Announce Type: cross Abstract: LLMs latent-state reasoning methods replace discrete intermediate tokens with continuous states, such as weighted mixtures of token embeddings, to ret

researcharxiv-cs-cl

AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

arXiv:2609.00488v1 Announce Type: new Abstract: Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamics

BS: Take the Hint - Interactive Multitracer PET/CT Lesion Segmentation with a Scribble-Conditioned ResEnc U-Net

arXiv:2609.01554v1 Announce Type: cross Abstract: Automated lesion segmentation in whole-body PET/CT is complicated by the variety of physiological tracer uptake patterns and by the differing appearan

C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation

arXiv:2411.10788v4 Announce Type: replace Abstract: Synthetic Aperture Radar (SAR) imagery provides robust environmental and temporal coverage (e.g., during clouds, seasons, day-night cycles), yet its

CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction

model-releasesarxiv-cs-ai

arXiv:2609.00242v1 Announce Type: cross Abstract: Long-tail autonomous driving failures are often framed as rare-object recognition errors. We argue that this view is incomplete: the decision-critical

Denoising Diffusion Generative Models Secretly Calculate Attentions

arXiv:2609.00885v1 Announce Type: new Abstract: Denoising diffusion models are the dominant architecture for image generation, whereas most natural language generation and modeling are primarily handl

Local Ai

FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation

arXiv:2609.00704v1 Announce Type: new Abstract: Functional tissue units (FTUs), including tertiary lymphoid structures (TLSs), blood vessels, and glands, encode localized immune, vascular, and epithel

local-aiarxiv-cs-cv

How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?

arXiv:2607.18114v2 Announce Type: replace-cross Abstract: Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-s

safetyarxiv-cs-ai

Tutorials

PredErase: Training-Free Object-and-Effect Removal with Predictive Latent Guidance

arXiv:2609.00956v1 Announce Type: new Abstract: Removing an object is not the same as filling its mask. Cast shadows and contact shading usually lie outside the user-provided instance mask M_obj, so a

tutorialsarxiv-cs-cv

SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning

arXiv:2609.00342v1 Announce Type: new Abstract: Whole-slide images (WSIs) are challenging for vision-language reasoning because diagnostically relevant morphology is sparse, heterogeneous, and distrib

Tutorials

What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models

arXiv:2609.01551v1 Announce Type: new Abstract: Self-supervised video foundation models learn rich spatiotemporal representations, yet it remains unclear what visual concepts these representations enc

tutorialsarxiv-cs-cv

3D-MRL: Nested Multimodal 3D Representations via Matryoshka Representation Learning

arXiv:2608.29285v1 Announce Type: new Abstract: Vision-Language Models align point clouds with image and text embeddings, enabling zero-shot recognition, retrieval, and open-vocabulary understanding o

Beat-Synchronous Tokenization for ECG Transformers

arXiv:2608.30367v1 Announce Type: new Abstract: Transformer-based electrocardiogram (ECG) models commonly tokenize waveforms into fixed temporal patches. Though convenient, fixed patching can split he

Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRI

arXiv:2608.28787v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) have primarily been developed for self-supervised representation learning. Denoising JEPA (D-JEPA) rece

Classification Drives Geographic Bias in Street Scene Segmentation

arXiv:2412.11061v2 Announce Type: replace Abstract: Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier wor

Cross-Modal Visuo-Tactile Representation Learning with Action Chunking Transformers for Contact-Rich Manipulation

arXiv:2602.00514v3 Announce Type: replace Abstract: Tactile feedback is important for contact-rich robotic manipulation, yet effective use of tactile observations remains challenging when tactile sign

safetyarxiv-cs-ro

Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model

arXiv:2603.16622v2 Announce Type: replace Abstract: Instead of directly distilling a language model, this study addresses the problem of aligning a base model with a target model in distribution by de

safetyarxiv-cs-cl

Is Deformable Image Registration Ready for Brain Metastasis Reirradiation Dose Accumulation? A Longitudinal MRI Benchmark of Registration Accuracy

model-releasesarxiv-cs-cv

arXiv:2608.28705v1 Announce Type: cross Abstract: Dose accumulation is increasingly important in adaptive radiation therapy and reirradiation, but its clinical validity depends on the performance of d

Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL

model-releasesarxiv-cs-ai

arXiv:2608.29054v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly in

Liquid Gated Attention

arXiv:2608.30695v1 Announce Type: new Abstract: Real-world time series often exhibit irregular sampling and extended temporal horizons, requiring models to capture continuous-time dynamics across arbi

safetyarxiv-cs-lg

MedTVL: Harnessing Vision and Language for Medical Time Series Classification

arXiv:2608.28605v1 Announce Type: new Abstract: Recent advancements in multimodal learning for medical time series (MedTS) classification highlight the benefits of integrating complementary modalities

MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

arXiv:2608.30636v1 Announce Type: new Abstract: Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning model