Search: “pretraining”

The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order

arXiv:2606.24993v1 Announce Type: new Abstract: Sequential learning is order-dependent: from Pile-style next-token domain adaptation to instruction-SFT and DPO, N candidate sources induce N! possible

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Applications

The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning

DGX agent

arXiv:2512.18471v2 Announce Type: replace Abstract: Continual learning systems face a fundamental geometric obstacle: as experience accumulates on a fixed-capacity manifold, covering numbers grow line

applicationsarxiv-cs-lg

25 Jun 2026

Agents

To View Transform or Not to View Transform: NeRF-based Pre-training Perspective

arXiv:2603.28090v2 Announce Type: replace Abstract: Neural radiance fields (NeRFs) have emerged as a prominent pre-training paradigm for vision-centric autonomous driving, which enhances 3D geometry a

agentsarxiv-cs-cv

Training for the Model You Return: Improving Optimization for Iterate-Averaged Language Models

model-releasesarxiv-cs-lg

arXiv:2606.25086v1 Announce Type: new Abstract: Many modern Language Model (LM) pipelines return an averaged model, such as an exponential moving average of the training iterates, rather than the fina

What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment

arXiv:2606.25718v1 Announce Type: new Abstract: Zero-shot visual decoding from electroencephalography (EEG) aims to infer visual semantics from non-invasive neural recordings, but remains challenging

Tutorials

What's in an Earth Embedding? An Explainability Analysis of Location Encoders

arXiv:2606.24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into

tutorialsarxiv-cs-lg

Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety

arXiv:2606.25034v1 Announce Type: new Abstract: General-purpose models often struggle to reliably identify and understand real-world multimodal risks, largely due to the inherent multimodal adversaria

safetyarxiv-cs-cv

Local Ai

3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy

arXiv:2606.23964v1 Announce Type: cross Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells. We prese

local-aiarxiv-cs-cv

A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling

model-releasesarxiv-cs-lg

arXiv:2606.24696v1 Announce Type: cross Abstract: Physics-informed surrogate models can accelerate computational fluid dynamics simulations. However, many existing methods reproduce global flow patter

Aligning Audio Captions with Human Preferences

arXiv:2509.14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human pre

BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming

arXiv:2606.24740v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) such as CLIP have demonstrated strong generalization across natural-image domains. However, adapting th

Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation

arXiv:2606.24464v1 Announce Type: new Abstract: Text-driven Referring Video Object Segmentation (RVOS) aims to locate and segment target objects in videos given natural language. However, existing mod

safetyarxiv-cs-cv

Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training

arXiv:2606.24164v1 Announce Type: cross Abstract: Recent end-to-end models for EEG-guided target speech extraction report impressive results, underscoring potential for neuro-steered hearing technolog

safetyarxiv-cs-ai

DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

model-releasesarxiv-cs-ai

arXiv:2606.24779v1 Announce Type: cross Abstract: Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and

Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement

arXiv:2606.24552v1 Announce Type: new Abstract: Simulator-in-the-loop optimization offers a promising inference-time mechanism for robot manipulation. It uses a physical simulator as a backend rollout

safetyarxiv-cs-ro

Geometric Action Model for Robot Policy Learning

arXiv:2606.17046v2 Announce Type: replace-cross Abstract: Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physi

safetyarxiv-cs-cv

HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models

arXiv:2606.23843v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are typically pre-trained on large-scale image-text datasets to capture semantic correspondences between visual content an

Ingredient-Level Food Image Segmentation for Nutrition Awareness

arXiv:2606.24059v1 Announce Type: new Abstract: Food images often contain several visible ingredients, so assigning one dish label to an entire image hides important visual structure. This work studie

ModTGCN: Modularity-aware Graph Neural Networks for Text Classification

arXiv:2606.23694v1 Announce Type: new Abstract: Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic docume

researcharxiv-cs-cl

MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network

arXiv:2606.24214v1 Announce Type: new Abstract: Accurate pulmonary vessel segmentation remains challenging due to the sparse, tortuous, and multi-scale nature of vascular structures, where small branc

Navigating User Behavior toward Personalized Multimodal Generation

model-releasesarxiv-cs-ai

arXiv:2606.24196v1 Announce Type: new Abstract: Modern AIGC pipelines deliver high-fidelity images and videos but presuppose a well-formed creation instruction, while end users rarely articulate visua

Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification

arXiv:2606.23744v1 Announce Type: cross Abstract: Deep learning has become an important tool in computational pathology, enabling automated analysis of histopathological images. While convolutional ne

Revealing Training Data Exposure in Vision Language Large Models via Parameter Gradients

arXiv:2606.24774v1 Announce Type: new Abstract: Vision-Language Large Models (VLLMs) trained on massive crawled corpora raise pressing copyright and data-provenance concerns. These concerns are partic

Similarity of Neural Network Representations in Superposition

arXiv:2604.00208v2 Announce Type: replace Abstract: Comparing internal representations is a central goal in neuroscience and machine learning, but standard linear alignment metrics (Representational S

Social Structure Matters in 3D Human-Human Interaction Generation

arXiv:2606.24255v1 Announce Type: cross Abstract: Although text-to-motion generation has achieved strong progress in synthesizing realistic single-person motions from language, extending it to text-dr

safetyarxiv-cs-ai

Tutorials

Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

arXiv:2512.10244v2 Announce Type: replace Abstract: Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as auto-annotation, as it aims to learn a model from a few labeled

tutorialsarxiv-cs-cv

Transformation Behavior of Images in Latent Space

arXiv:2606.24430v1 Announce Type: cross Abstract: Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and

researcharxiv-cs-ai

Tri-Efficient Transfer Learning for Point Cloud Videos

arXiv:2606.24175v1 Announce Type: new Abstract: While point cloud foundation models have significantly advanced point cloud video understanding, existing parameter-efficient fine-tuning (PEFT) methods

Verifiable Foundation Models for Robot Safety

arXiv:2606.23754v1 Announce Type: cross Abstract: Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes the

World Value Models for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.24742v1 Announce Type: new Abstract: Generalist value models play a pivotal role in scaling robotic policy learning from large-scale, mixed-quality data. Mathematically, accurate value esti

A Latent Representation Learning Framework for Hyperspectral Image Emulation in Remote Sensing

arXiv:2603.21911v2 Announce Type: replace Abstract: Synthetic hyperspectral image (HSI) generation is essential for large-scale simulation, algorithm development, and mission design, yet traditional r

All Routes Lead to Collapse

arXiv:2606.22325v1 Announce Type: new Abstract: Attention sinks, representation collapse, and norm stratification are treated as transformer-specific pathologies. We show they are not specific to atte

Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning

arXiv:2606.21938v1 Announce Type: new Abstract: Reconstructing articulated objects from sparse images requires recovering complete geometry, movable parts, and motion parameters. Recent methods typica

Atomistic Language Models Understand and Generate Materials

model-releasesarxiv-cs-lg

arXiv:2606.21395v1 Announce Type: new Abstract: Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-

AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery

arXiv:2606.23542v1 Announce Type: new Abstract: Forest imagery analysis often involves multiple tightly coupled vision tasks, which must be performed under substantial variation in geographic regions,

Beyond ROC-AUC: Operating-Point Performance Reporting for Biometric Verification

arXiv:2606.20680v1 Announce Type: new Abstract: A biometric verifier is often deployed with a strict false match budget, so only a narrow, low false match rate (FMR) slice of the score range is used.

BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language

arXiv:2606.22138v1 Announce Type: cross Abstract: We present BioMatrix, the first multimodal foundation model that natively integrates sequences, structures, and natural language for both molecules an

Local Ai

Channel Location Constrains the Auditability of Subliminal Learning

arXiv:2606.22019v1 Announce Type: new Abstract: Subliminal learning lets a student inherit a teacher's hidden trait from distillation data that never names it. We ask when such transfer can be audited

local-aiarxiv-cs-lg

Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts

arXiv:2606.21885v1 Announce Type: new Abstract: Foundation models have achieved remarkable performance across medical question answering, imaging, and electronic health record (EHR) tasks, yet reliabl

ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading

arXiv:2606.21116v1 Announce Type: new Abstract: Proofreading--correcting segmentation errors in 3D brain reconstructions--is the rate-limiting step in synapse-resolution connectomics. We release Conne

Decodable but Not Faithful: Coupling Natural-Language Rationales to Programmatic Verifiers

arXiv:2606.21678v1 Announce Type: new Abstract: Language models can generate plausible rationales for their predictions, but these explanations may not faithfully represent the model's internal reason

Duet: Dual-Robot Understanding via Efficient Teaching

model-releasesarxiv-cs-ro

arXiv:2606.20990v1 Announce Type: new Abstract: Dual-robot collaboration enables tasks that exceed the reach and payload of a single robot, such as collaboratively transporting objects across environm

EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors

arXiv:2602.21218v2 Announce Type: replace-cross Abstract: High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic dat

FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection

arXiv:2606.22487v1 Announce Type: new Abstract: Automated frame selection for fetal biometry remains under addressed, with most prior work targeting generic quality assessment or downstream measuremen

Hardware

FGGS-LiDAR: Ultra-Fast, GPU-Accelerated Simulation from General 3DGS Models to LiDAR

arXiv:2509.17390v2 Announce Type: replace Abstract: While 3D Gaussian Splatting (3DGS) has emerged as a strong representation for photorealistic rendering, its vast ecosystem of assets remains difficu

hardwarearxiv-cs-ro

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

arXiv:2606.22346v1 Announce Type: cross Abstract: Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow An

FORGE: Fused On-Register Gradient Elimination for Memory-Efficient LLM Training

arXiv:2606.22932v1 Announce Type: new Abstract: Reverse-mode differentiation computes every weight gradient, writes it to memory, and only then lets the optimizer read it back. This two-phase schedule

Generative Modeling via Kernelized Stochastic Interpolants

arXiv:2602.20070v3 Announce Type: replace Abstract: We develop a kernel method for generative modeling within the stochastic interpolant framework, replacing neural network training with linear system