Search: “pretraining”

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

4 Sept 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Entangled Representations Amplify Collateral Damage in Unlearning

DGX agent

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

model-releasesarxiv-cs-cl

3 Sept 2026

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

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

safetyarxiv-cs-cv

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

safetyarxiv-cs-cv

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

Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study

arXiv:2608.29144v1 Announce Type: new Abstract: Synthetic data generation has become a cornerstone for advancing large language models. However, the lack of the quantitative analysis for error toleran

Seeing Through Extreme Visual Sparsity: Surface Understanding from a Single Random Visual Patch

arXiv:2608.29475v1 Announce Type: new Abstract: Surface material recognition from incomplete visual observations remains a challenging problem in robotic perception and environmental understanding. Th

Self-Supervised Pretext Tasks for Infant Cry Analysis: A Controlled Comparison and a Cautionary Result on Donateacry

model-releasesarxiv-cs-lg

arXiv:2608.30456v1 Announce Type: new Abstract: We compare six self-supervised pretext tasks for infant cry analysis under a fixed budget, meaning the same compact encoder of 1.17M parameters, the sam

SmoothRL: Online Reinforcement Learning During Asynchronous Execution

arXiv:2608.29768v1 Announce Type: new Abstract: Deploying robot policies in the physical world requires satisfying two fundamental desiderata: reliability and smooth real-time execution. However, depl

safetyarxiv-cs-ro

VeriCam: A Verification Baseline for the Classification of Unknown Data

model-releasesarxiv-cs-cv

arXiv:2608.31107v1 Announce Type: new Abstract: The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalization

FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization

model-releasesarxiv-cs-cv

arXiv:2608.28302v1 Announce Type: new Abstract: Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize.

Local Ai

GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perception

arXiv:2608.27971v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) multimodal perception integrates visible (RGB), infrared (IR), synthetic aperture radar (SAR), and depth sensors for scene

local-aiarxiv-cs-cv

NEW WORLD MODEL: @ylecun's team is back with an efficient model! This project involves @ylecun, @lukaskuhn77, @lucasmaes_, @quentinlldc, and…

model-releasesyann-lecun--x

NEW WORLD MODEL: @ylecun's team is back with an efficient model! This project involves @ylecun, @lukaskuhn77, @lucasmaes_, @quentinlldc, and @randall_balestr. A couple definitions first: - DINO: self-

OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion

arXiv:2512.00234v3 Announce Type: replace Abstract: There has been significant progress in open-source text-only translation large language models (LLMs) with better language coverage and quality. How

Hardware

SOMTab: Set-Order Mamba for Efficient Tabular In-Context Learning

arXiv:2608.27882v1 Announce Type: new Abstract: Tabular foundation models based on in-context learning have recently emerged as strong alternatives to task-specific model fitting. However, the current

hardwarearxiv-cs-lg

Local Ai

WALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes

arXiv:2608.28216v1 Announce Type: new Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is abs

local-aiarxiv-cs-cv

A Single Suffix to Break Them All: Basin-Aware Jailbreaks for Merged Model Families

arXiv:2608.26506v1 Announce Type: cross Abstract: Model merging enables combining multiple fine-tuned models without additional training, but its safety implications remain poorly understood. Prior wo

safetyarxiv-cs-cl

DINOcular: Self-Supervised Visuospatial Representations

arXiv:2608.27226v1 Announce Type: new Abstract: We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations. While modern vision foundation models

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

arXiv:2608.26962v1 Announce Type: new Abstract: Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecu

ProofEvolve: Neuro-Symbolic Evolution for Formal Automated Theorem Proving

arXiv:2608.26334v1 Announce Type: new Abstract: Automated theorem proving offers a natural foundation for recursive self-improvement in scientific discovery. However, existing neural provers do not fu

Riemann-1.0: An Embodied World Action Model for Physical AI

model-releasesarxiv-cs-ro

arXiv:2608.27033v1 Announce Type: new Abstract: We introduce Riemann-1.0, a fully causal autoregressive World Action Model for embodied intelligence. Riemann-1.0 jointly models multi-view visual obser

Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable

arXiv:2608.26958v1 Announce Type: cross Abstract: Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger s

Squeezing More from Limited Data with Recursive Transformers

model-releasesarxiv-cs-cl

arXiv:2608.26973v1 Announce Type: new Abstract: Pre-training under limited data requires a different view of scaling than web-scale language modeling. With a fixed data budget but relatively abundant

Unsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentation

arXiv:2608.27190v1 Announce Type: new Abstract: Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compoun

safetyarxiv-cs-cv