Search: “pretraining”

Directional Constraints for Efficient Exploration in Safe Reinforcement Learning

arXiv:2607.12784v1 Announce Type: cross Abstract: Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However

safetyarxiv-cs-lg

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Tutorials

DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery

DGX agent

arXiv:2607.12319v1 Announce Type: new Abstract: As vision-language models (VLMs) are increasingly deployed in geospatial question answering and visual scene understanding, improving their spatial cogn

tutorialsarxiv-cs-cv

15 Jul 2026

Hardware

Gradient-Free Topology Adaptation for Power Flow Surrogates via In-Context Whitening

arXiv:2607.12241v1 Announce Type: cross Abstract: Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of

hardwarearxiv-cs-lg

Syntheses

Wiki Lint Report — 2026-07-15

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Automated lint: 26 errors, 6728 warnings, 3 info

VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression

arXiv:2607.12756v1 Announce Type: new Abstract: Vision-language models (VLMs) process large numbers of visual tokens, resulting in substantial inference latency and memory overhead. This has motivated

congrats on the launch! As the task horizons on agents gets longer, we need more work evaluating and training models to be better in long-ru…

congrats on the launch! As the task horizons on agents gets longer, we need more work evaluating and training models to be better in long-running, open-ended, evolving real-world environments. Today w

applicationsjerry-liu--x

13 Jul 2026

Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung

model-releasesarxiv-cs-cl

arXiv:2607.08362v1 Announce Type: new Abstract: Vietnam's ethnic minority languages are almost absent from the field of Natural Language Processing (NLP), and the challenge goes beyond data scarcity:

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

model-releasesarxiv-cs-lg

arXiv:2607.07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM_{2.5} concentrations that pose severe public health risks, yet forecasting rare, hazardous-level spikes remains

Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models

arXiv:2607.08186v1 Announce Type: new Abstract: Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another

researcharxiv-cs-cl

Hardware

SLORR: Simple and Efficient In-Training Low-Rank Regularization

arXiv:2607.08754v1 Announce Type: cross Abstract: Low-rank factorization is widely used to compress neural networks, but modern models are often not naturally amenable to aggressive factorization with

hardwarearxiv-cs-ai

SpO_2 Predictor-Guided Stage-Wise Time-Frequency Reconstruction of Low-Quality Dual-Wavelength PPG for Oxygen Saturation Estimation

arXiv:2607.07996v1 Announce Type: cross Abstract: Continuous oxygen saturation (SpO_2) estimation from wearable photoplethysmography (PPG) is important for long-term health monitoring, but low-quality

ToDMA: Large Model-Driven Massive Token Communications for Semantic Multiple Access

arXiv:2505.10946v3 Announce Type: replace-cross Abstract: Token communications (TokenCom) is an emerging generative semantic communication paradigm, where tokens serve as compact representation units

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607.07179v1 Announce Type: new Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information

DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation

arXiv:2607.07669v1 Announce Type: cross Abstract: Large language models increasingly understand dialectal English, yet still produce only standard, US-leaning English, leaving dialectal generation, th

Inertia-1: An Open Exploration of Wearable Motion Foundation Models

arXiv:2607.06617v1 Announce Type: cross Abstract: Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet i

applicationsarxiv-cs-ai

TimEE: End-to-end Time Series Classification via In-Context Learning

arXiv:2607.07500v1 Announce Type: cross Abstract: Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pre

Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation

arXiv:2607.07219v1 Announce Type: cross Abstract: Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain het

Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift

arXiv:2607.05908v1 Announce Type: new Abstract: Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine

applicationsarxiv-cs-lg

From Foundation to Application: Improving VLA Models in Practice

model-releasesarxiv-cs-ro

arXiv:2607.06403v1 Announce Type: new Abstract: Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their prac

Multi-Teacher Contrastive Distillation for Edge-Efficient Pathology Foundation Models

arXiv:2607.05533v1 Announce Type: new Abstract: Computational pathology foundation models (PFMs) have advanced whole-slide image analysis. However, their size and inference cost hinder local deploymen

RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models

arXiv:2607.06504v1 Announce Type: new Abstract: Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot general

applicationsarxiv-cs-ai

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607.06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes,

EmCom-Diffusion: Probing Visual Reflection in Emergent Languages via Image Generation

arXiv:2607.03752v1 Announce Type: cross Abstract: Measuring the extent to which emergent languages encode the visual content of their inputs is an open problem. We refer to this property as visual ref

Enhancement of E-commerce Sponsored Search Relevancy with LLM

arXiv:2607.03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the us

Local Ai

From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model

arXiv:2607.03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics. Traditional limited area m

local-aiarxiv-cs-lg

GenShin: Guiding Rational Liposome Design by Ranking Liposomal Protein Corona through a Docking-Pose-Free GNN

arXiv:2504.13853v2 Announce Type: replace-cross Abstract: Rational design of lipid nanoparticles (LNPs) for tissue-specific delivery critically depends on predicting the composition of the protein cor

Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models

arXiv:2607.04546v1 Announce Type: cross Abstract: Action-conditioned world models allow robots to predict the future consequences of candidate actions without additional physical interaction, supporti

PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space

arXiv:2607.05373v1 Announce Type: new Abstract: 3D reconstruction and generation are commonly tackled by separate paradigms: pixel-based regression for reconstruction, and latent diffusion for generat

researcharxiv-cs-cv

RayTun3R: Online Camera Adaptation in 3D Foundation Models

arXiv:2607.02711v1 Announce Type: new Abstract: Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, pi^3, and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole i

RL Forgets! Towards Continual Policy Optimization

model-releasesarxiv-cs-lg

arXiv:2607.04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks. Recent work has increasingly favored reinf

Self-Improving Diffusion Classifiers with Minority Preference Optimization

arXiv:2607.03770v1 Announce Type: cross Abstract: Prior studies have demonstrated that diffusion classifiers achieve robust zero-shot classification performance. However, their effectiveness is strong

StageCraft: Execution Aware Mitigation of Distractor and Obstruction Failures in VLA Models

arXiv:2603.20659v2 Announce Type: replace Abstract: Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generaliz

safetyarxiv-cs-ro

TESSERA v2: Scaling Pixel-wise Earth Foundation Models

arXiv:2607.03949v1 Announce Type: new Abstract: Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these

Walrus: A Cross-Domain Foundation Model for Continuum Dynamics

arXiv:2511.15684v2 Announce Type: replace-cross Abstract: Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a

LLM Priors for ERM over Programs

arXiv:2510.14331v3 Announce Type: replace Abstract: We study program-learning methods that are efficient in both samples and computation. Classical learning theory suggests that when the target admits

researcharxiv-cs-lg

3 Jul 2026

A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data

model-releasesarxiv-cs-lg

arXiv:2607.01145v1 Announce Type: new Abstract: Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distri

Does Your ViT Still Need U-Net for Segmentation?

arXiv:2607.00223v1 Announce Type: new Abstract: Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of

E-TIDE: Fast, Structure-Preserving Motion Forecasting from Event Sequences

arXiv:2603.27757v2 Announce Type: replace Abstract: Event-based cameras capture visual information as asynchronous streams of per-pixel brightness changes, generating sparse, temporally precise data.

researcharxiv-cs-cv

ECoSim: Data Efficient Fine-Tuning for Controllable Traffic Simulation

arXiv:2607.00545v1 Announce Type: new Abstract: Controllable traffic simulation is critical for testing autonomous driving systems, yet existing approaches often require retraining large generative mo

safetyarxiv-cs-cv

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning

arXiv:2607.00958v1 Announce Type: new Abstract: Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self

applicationsarxiv-cs-lg

MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation

arXiv:2607.00409v1 Announce Type: new Abstract: Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level pr

safetyarxiv-cs-cv

BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations

arXiv:2606.30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks. We present BEST-RQ-2, an evolution of BEST-RQ that reta

Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?

arXiv:2606.31126v1 Announce Type: new Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design. As strong pretrai

safetyarxiv-cs-lg

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

arXiv:2606.28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

arXiv:2601.18778v3 Announce Type: replace-cross Abstract: RL methods for scaling large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigat

researcharxiv-cs-cl

CellDETR: A Detection-Guided Framework for Scalable Cell Representation Learning from Histopathology Images

arXiv:2606.29463v1 Announce Type: new Abstract: Recent advances in pathology foundation models have substantially improved patch and slide level representation learning from whole-slide images (WSIs).

researcharxiv-cs-cv

30 Jun 2026

DTI: Dynamic Trajectory Initialization for Generative Face Video Super-Resolution

arXiv:2606.29198v1 Announce Type: new Abstract: As the most perceptually powerful Face Video Super-Resolution (FVSR) method, existing works in Generative FVSR (GFVSR) mainly exploit the generative pri

safetyarxiv-cs-cv

30 Jun 2026

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