Search: “pretraining”

GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generation

arXiv:2609.37158v1 Announce Type: new Abstract: Generative models for crystals enable the discovery of novel structures, but scaling all-atom generation to larger systems such as metal--organic framew

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Model Releases

Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters

DGX agent

arXiv:2609.36584v1 Announce Type: new Abstract: Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, sc

model-releasesarxiv-cs-lg

30 Sept 2026

MatToolBench: Benchmarking Multimodal Agents in Real-World Materials Science Workflows

model-releasesarxiv-cs-ai

arXiv:2609.37053v1 Announce Type: new Abstract: Multimodal GUI agents have achieved impressive results on general software benchmarks, yet their ability to operate professional scientific software rem

Message Passing Does More with Less for In-Context Learning on Graphs

arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeate

PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG

model-releasesarxiv-cs-ai

arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristics

Hardware

Probe-Space Preconditioning for Fast and Stable Zero-Order Training

arXiv:2609.38095v1 Announce Type: new Abstract: Backpropagation (BP) dominates deep learning but imposes a massive memory tax. For example, training OPT-30B with Adam requires approx 600GB of GPU memo

hardwarearxiv-cs-lg

Representational and Functional Robustness to Electrode Montages in EEG Foundation Models

arXiv:2609.36288v1 Announce Type: new Abstract: EEG foundation models (EEG-FMs) are intended to generalize across different datasets by learning representations that, ideally, are invariant to dataset

Scheduling Recursive Reasoning in Looped Transformers

arXiv:2609.36653v1 Announce Type: new Abstract: Recurrent reasoning models have attracted growing attention for scaling test-time computation, typically by iteratively refining latent states with shar

Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training

arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instability

safetyarxiv-cs-ai

Adjoint Guidance Flow: Amortized Critic Guidance for VLA Policies

arXiv:2609.34944v1 Announce Type: cross Abstract: Flow-based Vision-Language-Action (VLA) policies are typically trained by behavior cloning and thus do not explicitly optimize long-term task return.

safetyarxiv-cs-cv

Copper-Policy: Focus on the Representation for Robust Robot Manipulation

model-releasesarxiv-cs-lg

arXiv:2609.32779v1 Announce Type: cross Abstract: World Action Models (WAMs) acquire behavioral priors by modeling future scene evolution, but predicting detailed futures in pixel or latent space incu

FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL

arXiv:2609.34488v1 Announce Type: new Abstract: Looped architectures scale computation by reusing the same parameters across recurrent steps, and recent work shows that they substantially improve deep

safetyarxiv-cs-lg

From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations

arXiv:2609.35375v1 Announce Type: new Abstract: Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world robot data. While recent approaches leverage human video demonst

safetyarxiv-cs-ro

Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning

arXiv:2609.35341v1 Announce Type: new Abstract: Evaluating semantic similarity between videos is a fundamental challenge in computer vision, essential for tasks ranging from out-of-distribution (OOD)

tutorialsarxiv-cs-cv

How Does 'English (US)' Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline

arXiv:2604.04204v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly embedded in educational, professional, and public infrastructure, yet widely used platforms expo

safetyarxiv-cs-lg

Improving Video Sparse Attention with Fine-grained Router and Sparse Rebasing

arXiv:2609.32882v1 Announce Type: new Abstract: We present VSA2, a frontier trainable sparse attention for video DiTs. VSA2 includes a variety of new architectural features and training procedures tha

Learning Geometrically-Grounded Amodal 3D Representations for View-Generalizable Robotic Manipulation

arXiv:2601.22988v2 Announce Type: replace Abstract: Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-a

safetyarxiv-cs-ro

LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR

arXiv:2506.19065v3 Announce Type: replace Abstract: We propose Legato, a new end-to-end model for optical music recognition (OMR), a task of converting music score images to machine-readable documents

Livin' on a Prior: Likelihood Score Approximation for Inverse Problems

model-releasesarxiv-cs-lg

arXiv:2609.34446v1 Announce Type: new Abstract: Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrai

OneCanvas: 3D Scene Understanding via Panoramic Reprojection

arXiv:2606.19253v2 Announce Type: replace Abstract: Existing approaches to 3D scene understanding in Vision-Language Models (VLMs) either rely on complex, model-specific geometry encoders or large tra

OneSign: Unifying Sign Language Understanding Tasks with One Model

arXiv:2609.33090v1 Announce Type: cross Abstract: SLU encompasses a diverse set of tasks, including ISLR, CSLR, and SLT. Although these tasks share basic semantic and linguistic foundations, they are

PanoFuse: Panorama-Enhanced Vision-Language-Action Learning with Decoupled Semantic-Geometric Routing

arXiv:2609.31717v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have shown promising performance in language-conditioned robotic manipulation. However, most existing VLA systems

QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities

arXiv:2609.34842v1 Announce Type: new Abstract: Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the di

tutorialsarxiv-cs-lg

Query Expansion and Key Specialization in Transformer Attention Geometry

arXiv:2609.34273v1 Announce Type: cross Abstract: The projection of queries and keys are central to the attention mechanism in Transformer architectures. While they are mathematically symmetric, they

REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection

arXiv:2609.32734v1 Announce Type: new Abstract: AI-generated image detectors are often evaluated on benchmarks where real and synthetic images differ in content, quality, or generation artifacts, allo

Local Ai

SpatialSpeak: QA-Native Reconstruction with Local and Global Context for Spatial Chain-of-Thought Reasoning

arXiv:2609.33616v1 Announce Type: new Abstract: Vision-language models (VLMs) can benefit from geometric priors for multi-view spatial reasoning, yet answer-only training does not directly supervise t

local-aiarxiv-cs-cv

Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation

arXiv:2609.35450v1 Announce Type: new Abstract: Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to

tutorialsarxiv-cs-ro

Local Ai

Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks

arXiv:2609.30569v1 Announce Type: new Abstract: CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep le

local-aiarxiv-cs-ai

Aurora-X: Built for Extreme Time Series Forecasting

arXiv:2609.31038v1 Announce Type: new Abstract: Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by under

tutorialsarxiv-cs-lg

Applications

BreathGRU: A Novel Semi-Supervised Bidirectional Gated Recurrent Unit Framework for Speech and Breath Segmentation for Respiratory Audio

arXiv:2609.31165v1 Announce Type: cross Abstract: Speech-breath segmentation is a fundamental preprocessing step in respiratory audio analysis, enabling applications such as respiratory acoustic bioma

applicationsarxiv-cs-lg

Decodable In-Context State and Model Output Across Training

arXiv:2609.31401v1 Announce Type: new Abstract: Prior work established that a probe can decode an in-context binding on model errors and that probe-guided steering can repair some of them. We follow p

Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward

arXiv:2511.20561v3 Announce Type: replace-cross Abstract: Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly in

researcharxiv-cs-cl

Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence

arXiv:2609.30535v1 Announce Type: new Abstract: Children in multilingual communities often code-switch, using multiple languages in a single utterance. Can we induce cross-lingual alignment in languag

safetyarxiv-cs-cl

Latent Generative Solvers for Generalizable Long-Term Physics Simulation

arXiv:2602.11229v3 Announce Type: replace Abstract: Reliable physics simulation demands two capabilities that today's neural PDE solvers do not deliver together: generalization across heterogeneous PD

researcharxiv-cs-ai

Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

arXiv:2609.30539v1 Announce Type: cross Abstract: Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, all

Samples, Sources, Space: Decomposing Data Scale in Spatially Structured Representation Learning of Human Brain Microarchitecture

arXiv:2609.31201v1 Announce Type: new Abstract: Scaling studies typically represent training data by a single count of samples. For hierarchically and spatially structured data, however, the same numb

researcharxiv-cs-ai

Spectral Feedback for Test-Time Alignment of Protein Diffusion Models

arXiv:2609.30456v1 Announce Type: new Abstract: Reward maximization alignment methods for discrete diffusion models have primarily focused on steering the reverse process, either by influencing token

safetyarxiv-cs-ai

StarWM: Self-Supervised Trained Attention Routing for Robust World Models

arXiv:2609.30667v1 Announce Type: new Abstract: A robust world model must strike the balance between faithfully capturing environmental dynamics and abstracting away from irrelevant content. While rec

safetyarxiv-cs-cv

Structure-Guided Masked Autoencoders for Ultra-High Resolution Scientific Image Understanding

arXiv:2609.30682v1 Announce Type: new Abstract: Self-supervised pre-training with Vision Transformers, including Masked Autoencoders (MAE), is difficult to apply to gigapixel scientific images. Random

tutorialsarxiv-cs-cv

Training Graph Foundation Models on The Web Graph

arXiv:2609.30894v1 Announce Type: new Abstract: We introduce Acacia, a graph foundation model, trained on the web graph. Acacia (i) supports arbitrary feature dimensionalities and semantics without ad

researcharxiv-cs-ai

VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation

arXiv:2503.10685v3 Announce Type: replace Abstract: Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited da

Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation

arXiv:2505.11909v2 Announce Type: replace-cross Abstract: This paper addresses cross-modal medical image segmentation, focusing on MRI-CT transfer in a source-only domain generalization setting. Durin

Local Ai

CoSWA-YOLOv12: Scale-Invariant Tiny Object Detection and Segmentation of Malaria Parasites

arXiv:2609.29527v1 Announce Type: cross Abstract: Automated microscopy could widen access to malaria diagnosis in low-resource settings, but the deadliest species, P. falciparum, presents in its early

local-aiarxiv-cs-ai

EndoFSA: Endoscopic Few-Shot Image Generation via Rank-Constrained Parameter Adaptation

arXiv:2609.29930v1 Announce Type: new Abstract: WCE produces large-scale gastrointestinal image data yet pathological findings remain significantly underrepresented limiting the generalization perform

Applications

M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis

arXiv:2609.30056v1 Announce Type: cross Abstract: Robotic novel view synthesis (NVS) must recover both visual appearance and metric 3D structure, yet most generative NVS methods rely only on images, o

applicationsarxiv-cs-cv

Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation

arXiv:2609.30023v1 Announce Type: new Abstract: Imitation learning enables robots to acquire manipulation skills from demonstrations, but the resulting policies can fail outside the training data, whi

safetyarxiv-cs-ro

Hardware

WildHSR: Metric Feed-Forward 4D People-Scene Reconstruction from a 3D Foundation Model

arXiv:2609.29106v1 Announce Type: cross Abstract: 3D foundation models recover video cameras and geometry in one forward pass, but some of the strongest are up to scale. Joint people-scene reconstruct

hardwarearxiv-cs-ai

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation