Search: “pretraining”

Routing Policy Optimization for Online Diffusion Reinforcement Learning

arXiv:2610.22282v1 Announce Type: new Abstract: Reinforcement learning can align visual generators with reward signals beyond their pretraining objectives, but existing score-function methods typicall

safetyarxiv-cs-cv

16 Oct 2026

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Research

SATOR: Backpropagation-Free 3D Test-Time Adaptation via Sample-Adaptive Token Routing

DGX agent

arXiv:2610.21367v1 Announce Type: new Abstract: Test-time adaptation (TTA) mitigates the performance degradation of source-pretrained models on unlabeled test data under distribution shifts. Recent ba

researcharxiv-cs-cv

16 Oct 2026

Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution

arXiv:2610.22262v1 Announce Type: new Abstract: Scientific super-resolution (SR) models are typically optimized with pointwise reconstruction losses, yet scientific interpretation often hinges on extr

16 Oct 2026

Hardware

AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents

arXiv:2610.20474v1 Announce Type: new Abstract: Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, b

hardwarearxiv-cs-cl

Can LLMs Access their World Knowledge for Event Prediction?

model-releasesarxiv-cs-cl

arXiv:2610.19021v1 Announce Type: new Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LL

From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents

arXiv:2610.19420v1 Announce Type: cross Abstract: Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agen

Tutorials

GeoThinker-F: Endogenous Predictive World Representations for Fast Spatial Intelligence

arXiv:2610.20592v1 Announce Type: new Abstract: Existing geometry-aware Multimodal Large Language Models (MLLMs) increasingly rely on external 3D encoders to provide structural priors, but executing t

tutorialsarxiv-cs-cv

Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand

arXiv:2610.20351v1 Announce Type: new Abstract: Human demonstrations are today used largely for pretraining vision-language-action models, with little ability to use the same data for target-embodimen

Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model

arXiv:2610.20462v1 Announce Type: new Abstract: Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investiga

Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling

arXiv:2610.19559v1 Announce Type: new Abstract: Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work

UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition

arXiv:2610.16942v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized domai

Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost

arXiv:2610.20035v1 Announce Type: cross Abstract: We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward. We derive it

ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation

arXiv:2510.02298v2 Announce Type: replace Abstract: Imitation learning has shown promise in learning from large-scale real-world datasets. However, pretrained policies usually perform poorly without s

CatPortrait: Taming Video Generative Models for Portrait Animation via In-context Conditioning

arXiv:2610.14957v1 Announce Type: new Abstract: Recent video generative models have shown strong capability in synthesizing high-quality and temporally coherent videos. Yet, adapting them for controll

researcharxiv-cs-cv

Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability

arXiv:2605.06538v2 Announce Type: replace Abstract: Diffusion-based posterior samplers draw from measurement- or reward-conditioned posteriors using pretrained diffusion priors. Though widely used for

DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding

arXiv:2610.17015v1 Announce Type: new Abstract: Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit dimini

researcharxiv-cs-cv

DR-VLA: Learning Human-Like Quadruped Navigation from Egocentric Web Videos via Domain-Invariant Scene Representations

arXiv:2610.18373v1 Announce Type: new Abstract: Quadruped robots performing outdoor deliveries must interpret complex scenes, anticipate changes, and navigate in socially compliant ways. Robot demonst

Growing and Transferring Pathways for LLM Reinforcement Learning

arXiv:2610.16489v1 Announce Type: new Abstract: Reinforcement learning (RL) can substantially improve the reasoning capabilities of large language models, but an effective training run requires consid

Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models

arXiv:2610.15781v1 Announce Type: new Abstract: Training-free pruning removes weights from pretrained large language models to reduce their deployment cost. However, existing approaches typically cons

On Sleep-Wake Disorder Identification from Polysomnographic Representations

arXiv:2610.17451v1 Announce Type: new Abstract: Self-supervised polysomnography foundation models (PSG FMs) aim to capture clinically useful information beyond sleep staging, yet are rarely compared w

PRISM: Precision Forcing for Generalist Vision-Language-Action Policies

arXiv:2610.14704v1 Announce Type: new Abstract: Coupling a pretrained vision-language model (VLM) with a diffusion or flow-matching action expert has become the mainstream vision-language-action (VLA)

Pristine Models

arXiv:2610.14091v1 Announce Type: new Abstract: Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting t

safetyarxiv-cs-cv

RAIN: Robotic Task Generalization with Region-Aware Interaction Networks

arXiv:2610.14039v1 Announce Type: cross Abstract: Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limitin

Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches

arXiv:2610.15761v1 Announce Type: cross Abstract: For languages with limited resources, Sentiment Analysis remains a challenging problem due to limited annotated data and strong domain variability acr

Applications

StreamingWAM: Streaming World Action Modeling via Chunk-Wise Staircase Flow Matching

arXiv:2610.15585v1 Announce Type: new Abstract: World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and ope

applicationsarxiv-cs-ro

Trust-Calibrated VLA Policy Post-Training with Evolving Imagination

arXiv:2610.16517v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are often pretrained via imitation learning, making reinforcement learning (RL) post-training important when deplo

A Categorical Atlas of Parameter-Efficient Fine-Tuning: Cones, Orbits, and Gauges

arXiv:2610.12728v1 Announce Type: new Abstract: Parameter-efficient fine-tuning has hundreds of methods, usually compared by parameter count and benchmark score. We compare them as maps. A pretrained

Forward Dynamics Models

arXiv:2610.13117v1 Announce Type: new Abstract: Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language model

Gamma: Grounded Multi-Agent Memory with Mechanical Audit for Memory-Intensive Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.12793v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies fail on memory tasks, behaviour that depends on events observed long before the current frame, because

Local Ai

GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs

arXiv:2610.13035v1 Announce Type: cross Abstract: Multi-view representations enable pretrained image and video vision-language models to understand 3D scenes, but their long visual token sequences mak

local-aiarxiv-cs-ai

Representation Alignment from a Few Paired Data via Distilled Optimal Transport

arXiv:2610.12781v1 Announce Type: new Abstract: Aligning two independently pretrained unimodal encoders typically demands millions of paired examples. In this paper, we study the regime where only N s

safetyarxiv-cs-ai

Self-Preservation in Language Models

arXiv:2610.12602v1 Announce Type: cross Abstract: Continual learning (CL) seeks to adapt pretrained models while preserving prior capabilities. Yet the tasks supplied for preservation cover only part

Dynamics as Code: On Model Compression via Dynamic System

arXiv:2610.11115v1 Announce Type: new Abstract: The escalating size of pretrained neural networks has rendered model compression a prerequisite for deployment under stringent memory and compute constr

How Much Audio Is Left In An Embedding? An Inversion Audit Of Audio Encoders

arXiv:2610.12250v1 Announce Type: cross Abstract: Pretrained audio encoders are reused for downstream tasks that are often unknown when the encoder is trained, so their usefulness depends partly on wh

researcharxiv-cs-ai

Tutorials

RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation

arXiv:2602.14032v2 Announce Type: replace Abstract: Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on l

tutorialsarxiv-cs-ro

Stability-Plasticity Balance via Singular-Vector Selection in LLM Continual Learning

arXiv:2610.11076v1 Announce Type: cross Abstract: Domain-specific continual adaptation of LLMs risks catastrophic forgetting, creating a fundamental tension between acquiring new capabilities and pres

Strategic Governance of AI Models in Earth Science

arXiv:2610.10560v1 Announce Type: cross Abstract: AI foundation models pretrained on weather and climate data are increasingly fine-tuned to Earth science tasks well beyond weather forecasting. Their

Unifying Policy Learning and State Prediction through Spatial Language Modeling

arXiv:2610.12172v1 Announce Type: cross Abstract: Learning how actions change scene geometry can provide complementary supervision for goal-directed manipulation. We introduce Spatial Language Modelin

safetyarxiv-cs-ai

WAM-Cache: Staleness-Bounded KV Reuse for Efficient World Action Models

arXiv:2610.11401v1 Announce Type: cross Abstract: World Action Models (WAMs) enable generalist robot manipulation by conditioning an action expert on representations from a pretrained video Diffusion

safetyarxiv-cs-cv

Breaking Adversarial Transferability in Fine-Tuned Speech Recognition

arXiv:2610.09109v1 Announce Type: new Abstract: Many organizations fine-tune publicly available pretrained Automatic Speech Recognition (ASR) models and deploy them in black-box settings, assuming lim

Hardware

HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption

arXiv:2610.08255v1 Announce Type: cross Abstract: Many organizations adapt large pretrained models to their own tasks by fine-tuning on private data. Several of these parties often hold data for the s

hardwarearxiv-cs-lg

Iris-3B: Going Beyond the Latent with Pixel-Space Diffusion Training, Conversion and Fine-Tuning

arXiv:2610.09450v1 Announce Type: new Abstract: Pixel-space diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks where fine-grained detail matters. We

Local Ai

Long-WAM: Scaling the Context of World-Action Models

arXiv:2610.10528v1 Announce Type: cross Abstract: Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long

local-aiarxiv-cs-cv

Noise, Denoise, Correct: MCMC Posterior Sampling with Diffusion Priors in Three Steps

arXiv:2610.09407v1 Announce Type: new Abstract: Pretrained diffusion models are powerful priors for inverse problems, but posterior sampling under nonlinear, non-differentiable forward models remain h

Sparse Feature Policy Unlearning Mitigates State Hallucination in Vision-Language-Action Models

arXiv:2610.09496v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by leveraging rich representations from pretrained vision

Steerspeech: Activation Steering For Emotion Control In Generated Speech

arXiv:2610.10415v1 Announce Type: cross Abstract: Pretrained text-to-speech (TTS) models can generate expressive speech, but reliable inference-time emotion control remains challenging: prompts and re

Thinking in Depth: Retrospective Inference for Tabular Foundation Models

arXiv:2610.10317v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled exa

Transferability and operational reliability of a Prithvi crop classification foundation model under phenological and geographic shift across three continents

arXiv:2610.08810v1 Announce Type: new Abstract: Fine-tuned geospatial foundation models (GeoFMs) pretrained on large satellite archives have been shown to improve crop classification accuracy and geog