Search: “pretraining”

FD^2: A Dedicated Framework for Fine-Grained Dataset Distillation

arXiv:2603.25144v2 Announce Type: replace-cross Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong

researcharxiv-cs-ai

Heterogeneous Tactile Transformer

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2606.29948v1 Announce Type: new Abstract: Tactile sensors are inherently heterogeneous: a model trained on one sensor cannot be directly used on another, which limits learning contact-rich manip

model-releasesarxiv-cs-ro

30 Jun 2026

How to Train Your Long-Context Visual Document Model

arXiv:2602.15257v3 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-docum

LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation

arXiv:2603.17576v3 Announce Type: replace Abstract: Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are essential for planning therapy and guiding surgical

NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment

arXiv:2606.18066v2 Announce Type: replace Abstract: We introduce the Noise-Tilted Reverse Kernel (NTRK), a reward-guided diffusion sampler that injects reward gradients through the noise term, leaving

On the Vulnerability of Parameter-Level Defenses to Model Merging

arXiv:2606.30360v1 Announce Type: cross Abstract: The training-free integration of expert models via model merging has exposed significant security risks, enabling free-riders to combine specialized m

Probing and Leveraging Video Diffusion Transformer Features for Robust Point Tracking

arXiv:2512.20606v2 Announce Type: replace Abstract: Despite achieving strong results on standard benchmarks, current point tracking methods rely on feature backbones that are rarely designed with the

applicationsarxiv-cs-cv

ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision Language Models

arXiv:2606.29579v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent challenge for many vision language models (VLMs), and improving it typically requires fine-tuning with substant

Seed-to-Seed: Unpaired Image Translation in Diffusion Seed Space

arXiv:2409.00654v2 Announce Type: replace Abstract: We introduce Seed-to-Seed Translation (StS), a novel approach that combines GANs and diffusion models (DMs) for unpaired Image-to-Image Translation.

Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss

arXiv:2606.29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data. Recent

researcharxiv-cs-ai

SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation

arXiv:2606.28626v1 Announce Type: new Abstract: Sign language translation (SLT) remains challenging due to its high spatio-temporal complexity, long sequences, and the need to model multiple articulat

SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport

arXiv:2602.23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model

safetyarxiv-cs-ai

Spatially Localized Image Degradation Embeddings for Image Quality Assessment

arXiv:2606.29162v1 Announce Type: new Abstract: Self-supervised learning (SSL) currently drives state-of-the-art performance in no-reference image quality assessment (NR-IQA). However, standard SSL pi

applicationsarxiv-cs-cv

Towards Physical Intuitions for Alignment Dynamics: A Case Study With Randomness Crystallization

arXiv:2606.29933v1 Announce Type: new Abstract: The alignment of language models is typically studied through the lens of capability benchmarks, but the dynamics of how models change during post-train

safetyarxiv-cs-cl

Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation

arXiv:2606.27935v1 Announce Type: new Abstract: Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models cr

DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand

arXiv:2606.28323v1 Announce Type: cross Abstract: Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging. Addi

safetyarxiv-cs-ai

MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction

arXiv:2603.03710v3 Announce Type: replace-cross Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-pos

safetyarxiv-cs-ai

PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction

arXiv:2606.27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. While recent gen

TextDS: Parameter-Efficient Representation Alignment for Scene Text Detection under Distribution Shifts

arXiv:2606.28077v1 Announce Type: new Abstract: In real-world deployments, scene text detectors inevitably face distribution shifts beyond the training distribution. Prior work often depends on large-

A Generalization Theory for JEPA-Based World Models

arXiv:2606.27014v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a l

Local Ai

A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets

arXiv:2606.27304v1 Announce Type: new Abstract: Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engine

local-aiarxiv-cs-lg

Local Ai

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT

arXiv:2606.27084v1 Announce Type: new Abstract: Reliable organ localization in abdominal CT can provide spatial priors for downstream trauma analysis. We propose CT-3GDINO, a lightweight 3D detector t

local-aiarxiv-cs-cv

RayPE: Ray-Space Positional Encoding for 3D-Aware Video Generation

arXiv:2606.27345v1 Announce Type: new Abstract: Modern video diffusion transformers position their tokens through RoPE on the (u,v,t) axes -- a description of the camera's sampling grid that says noth

Sculpting NeRF Geometry: Human-Preference Fine-Tuning of a 3D-Aware Face GAN

arXiv:2606.27305v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise ex

Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset

arXiv:2510.11047v2 Announce Type: replace Abstract: Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Larynge

Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study

arXiv:2606.25579v1 Announce Type: cross Abstract: Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using curr

Learning Diachronic Representations of Ancient Greek Letterforms

arXiv:2606.24984v1 Announce Type: cross Abstract: Learning representations that remain robust across centuries of variation in handwriting is a key challenge in diachronic representation learning. Tak

applicationsarxiv-cs-cl

Small Initialization Matters for Large Language Models

arXiv:2606.17945v2 Announce Type: replace Abstract: Large language models provide a tractable system for asking how intelligence itself emerges, rather than only how LLMs can be engineered. Although p

Towards Scalable Multi-Task Reinforcement Learning with Large Decision Models

arXiv:2606.24962v1 Announce Type: new Abstract: Recent progress in large-scale sequence modeling has shown that a single model can learn useful representations across highly diverse data distributions

Flood Mapping from RGB imagery using a Vision Foundation Model

arXiv:2606.24120v1 Announce Type: new Abstract: Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB ima

LaGO: Latent Action Guidance for Online Reinforcement Learning

arXiv:2606.24669v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for planning and sequential decision-making, but prior work often relies on using them as direc

LangMAP: A Language-Adaptive Approach to Tokenization

model-releasesarxiv-cs-cl

arXiv:2606.23566v2 Announce Type: replace Abstract: Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. However, using such a tokeniz

Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image

arXiv:2606.24138v1 Announce Type: new Abstract: Generating explicit 3D city assets from a single satellite image is important for digital twins, urban simulation, and geospatial intelligence. Unlike s

applicationsarxiv-cs-cv

video-SALMONN-R^3: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding

arXiv:2606.24477v1 Announce Type: cross Abstract: Video large language models (LLMs) are often constrained by computation and memory budgets, leading them to use reduced frame rates and spatial resolu

BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling

arXiv:2606.21014v1 Announce Type: new Abstract: Robots must generate trajectories that remain faithful to learned expert behavior while satisfying safety constraints and task-specific objectives speci

safetyarxiv-cs-ro

B[FM]^2: Brain Foundation Model via Flow Matching with SplitUNet

arXiv:2606.20812v1 Announce Type: new Abstract: EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical an

DiT-Reward: Generative Representations for Text-to-Image Reward Modeling

arXiv:2606.23626v1 Announce Type: new Abstract: Can representations learned for image generation also support the evaluation of generated images? We study text-to-image reward prediction as a downstre

FlowCodec: One-Step Flow Prior for Generative Image Compression

arXiv:2606.21030v1 Announce Type: cross Abstract: Diffusion-based image compression methods, leveraging powerful generative priors, have demonstrated remarkable perceptual quality at ultra-low bitrate

Gradual Capacity Growth for Sparse Network Discovery

arXiv:2509.25665v2 Announce Type: replace Abstract: Sparse neural network methods typically assume that the target sparsity (or density) is fixed in advance, even though the relationship between netwo

Agents

LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms

arXiv:2606.20729v1 Announce Type: cross Abstract: Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed app

agentsarxiv-cs-lg

Hardware

Multi-cancer detection using a computationally efficient CNN with transfer learning

arXiv:2606.22400v1 Announce Type: new Abstract: This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detec

hardwarearxiv-cs-cv

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

arXiv:2606.20673v1 Announce Type: cross Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained. In particular, vari

Tutorials

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

arXiv:2606.17276v3 Announce Type: replace-cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been

tutorialsarxiv-cs-lg

Robust Representation Learning in Masked Autoencoders

arXiv:2602.03531v2 Announce Type: replace-cross Abstract: Masked Autoencoders (MAEs) achieve impressive performance in image classification tasks, yet the internal representations they learn remain le

safetyarxiv-cs-cv

RocketPFN: Accurate Time Series Classification via In-Context Learning

arXiv:2606.21786v1 Announce Type: new Abstract: We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-c

Small LLMs: Pruning vs. Training from Scratch

model-releasesarxiv-cs-lg

arXiv:2606.14150v2 Announce Type: replace Abstract: Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama-3.1-8B at pruning ratios of 0.5-

Hardware

SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning

arXiv:2606.21973v1 Announce Type: new Abstract: Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractiv

hardwarearxiv-cs-lg

Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6G

arXiv:2606.20670v1 Announce Type: new Abstract: Wireless foundation models offer a path toward reusable channel state information (CSI) intelligence for sixth-generation (6G) systems. However, existin