Search: “pretraining”

arXiv:2609.02341v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) have shown strong potential in autonomous driving by leveraging multimodal pretraining for instruction following, v

agentsarxiv-cs-cv

3 Sept 2026

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CMRVision: A Foundation Model for Cardiac MR Image Analysis

DGX agent

arXiv:2609.01308v1 Announce Type: new Abstract: Cardiac magnetic resonance (CMR) imaging provides complementary information on cardiac anatomy, function, and tissue characterization across multiple se

model-releasesarxiv-cs-cv

2 Sept 2026

ReFlowSET: Representation-Aligned Latent Flow Matching for SAR-to-EO Image Translation

arXiv:2609.00968v1 Announce Type: new Abstract: SAR-to-EO image translation aims to generate electro-optical (EO) imagery from synthetic aperture radar (SAR) observations. Existing latent diffusion ap

safetyarxiv-cs-cv

Replicating TRACE: A Practitioner's Guide to Its Threshold and Particle Budget

arXiv:2609.01108v1 Announce Type: new Abstract: TRACE (Math & Lienhart, arXiv:2602.01135) reads causal graphs over event types out of a pretrained autoregressive sequence model by thresholding a per-p

Tutorials

SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding

arXiv:2609.01353v1 Announce Type: new Abstract: Protein inverse folding aims to recover amino acid sequences for a given 3D protein structure, underpinning broad applications such as enzyme engineerin

tutorialsarxiv-cs-ai

Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs

model-releasesarxiv-cs-cl

arXiv:2609.00184v1 Announce Type: new Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evaluatin

TimeSteer: Inference-Time Speech Scheduling in Joint Audio-Visual Diffusion Models

arXiv:2609.01277v1 Announce Type: cross Abstract: Although pretrained joint audio-visual diffusion models offer rich control over what to generate, they provide no explicit control over when an uttera

AdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs

arXiv:2601.06022v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding b

safetyarxiv-cs-ai

Asymmetric Within-Document Predictive Learning for Scientific Document Representation

arXiv:2608.28625v1 Announce Type: cross Abstract: We study predictive pretraining for scientific document representation using the discourse structure of papers. We propose SciJEPA, a citation-free fr

safetyarxiv-cs-ai

Continual Test-Time Adaptation via Entropy Sensitivity-Guidance in Strict Online Setting

arXiv:2608.29920v1 Announce Type: new Abstract: Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA wit

Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs

arXiv:2507.00754v3 Announce Type: replace Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the

General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level

arXiv:2311.13892v4 Announce Type: replace-cross Abstract: The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application. Compared to nu

MR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI

arXiv:2608.30975v1 Announce Type: cross Abstract: Cardiac magnetic resonance imaging (CMR) produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep lear

researcharxiv-cs-ai

QAQ: Bidirectional Semantic Coherence for Selecting High-Quality Synthetic Code Instructions

arXiv:2603.12165v3 Announce Type: replace Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult t

Randomized YaRN Improves Length Generalization for Long-Context Reasoning

arXiv:2606.23687v2 Announce Type: replace Abstract: Large language models (LLMs) are typically pretrained on short sequences and then extended to work on longer sequences with additional training. How

Test-Time Training with Next-Token Prediction

model-releasesarxiv-cs-cl

arXiv:2606.21803v2 Announce Type: replace Abstract: Next-token prediction is the self-supervised signal that trains language models, and every observed prompt token provides the same signal at test ti

TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI

arXiv:2608.30567v1 Announce Type: new Abstract: We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-con

Universal Redundancies in Time Series Foundation Models

arXiv:2602.01605v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for

safetyarxiv-cs-lg

Vision Models Predict Urban Scene Appraisal with Limited Neural Alignment

arXiv:2608.30964v1 Announce Type: new Abstract: Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated

When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models

arXiv:2608.30395v1 Announce Type: new Abstract: As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inf

Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation

arXiv:2608.30880v1 Announce Type: new Abstract: Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argu

safetyarxiv-cs-ro

GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation

arXiv:2608.27079v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies provide strong priors for robot manipulation, yet adapting them online to fine-grained biomedical tasks

safetyarxiv-cs-ro

28 Aug 2026

Tutorials

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

arXiv:2604.02324v2 Announce Type: replace-cross Abstract: Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in

tutorialsarxiv-cs-ai

28 Aug 2026

TemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation

arXiv:2608.26821v1 Announce Type: new Abstract: Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not

researcharxiv-cs-ro

28 Aug 2026

CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression

arXiv:2608.25568v1 Announce Type: new Abstract: To reduce deployment cost and retraining overhead, adapting pretrained learned image compression (LIC) models to downstream machine vision tasks has att

MeMark: Membrane-Space Watermarking for Spiking Neural Networks

arXiv:2608.25738v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are increasingly distributed as pretrained checkpoints and reused as backbones for new tasks. However, current SNN wate

MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations

arXiv:2608.25575v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and rela

Tutorials

Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon

arXiv:2608.25990v1 Announce Type: new Abstract: Orthogonal optimisers such as Muon can substantially accelerate large language model pretraining relative to Adam, yet the mechanism remains incompletel

tutorialsarxiv-cs-lg

Towards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning

arXiv:2608.25100v1 Announce Type: cross Abstract: Large Language Models (LLMs) are powerful but limited by static parametric knowledge that becomes outdated once pretraining ends. Knowledge editing ad

UltraPIPS: Improving model perception in B-mode ultrasound with foundation models

arXiv:2608.26033v1 Announce Type: new Abstract: In medical imaging, it is common to use learned perceptual image patch similarity (LPIPS) to compare images semantically in feature space. Although back

Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation

arXiv:2608.23992v1 Announce Type: cross Abstract: Large language model (LLM) agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protoco

Iwin Transformer: Hierarchical Vision Transformer using Interleaved Windows

arXiv:2507.18405v3 Announce Type: replace Abstract: Vision Transformers (ViTs) face two limitations: the rigid resolution dependency of positional embeddings, which complicates cross-resolution fine-t

NAIMA: Semantics Aware RGB Guided Depth Super-Resolution

arXiv:2604.04407v2 Announce Type: replace-cross Abstract: Guided depth super-resolution (GDSR) is a multi-modal approach for depth map super-resolution that relies on a low-resolution depth map and a

safetyarxiv-cs-cv

Hardware

Output-Level Regularization Eliminates the Seed Lottery in Single-GPU VLA Fine-Tuning

arXiv:2606.13856v2 Announce Type: replace Abstract: Fine-tuning a vision-language-action model (VLA-JEPA) on a single GPU should be simple: load a pretrained checkpoint, run training, deploy. There is

hardwarearxiv-cs-ro

CALIBURN: Self-Calibrated LLM Unlearning Alignment

arXiv:2602.02824v2 Announce Type: replace Abstract: LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language models, which offers a practical mechanism for address

safetyarxiv-cs-cl

Forgetting to Forget: Attention Sink as A Gateway for Backdooring LLM Unlearning

arXiv:2510.17021v2 Announce Type: replace-cross Abstract: Large language model (LLM) unlearning is a key approach for removing undesired data, knowledge, or behaviors from pretrained models while reta

Local Ai

One Inverse Step is a Convex Program: Bayes-Limit Calibration of Diffusion Inversion

arXiv:2608.23094v1 Announce Type: cross Abstract: One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local manifold geometry. It is the stationarity

local-aiarxiv-cs-lg

SAM3Dual: A 3rd Place Solution to the MOSEv2 Track, 8th LSVOS Challenge

arXiv:2608.22193v1 Announce Type: new Abstract: We present SAM3Dual, our third-place solution to the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. SAM3D

Hardware

Tracing the Unlabeled Storm: Cross-Variable Transfer in a Lagrangian Atmospheric JEPA Framework

arXiv:2608.22358v1 Announce Type: new Abstract: Deep atmospheric convection governs South Asian monsoon variability, yet attempting to learn its latent world model directly from zero-inflated, heavy-t

hardwarearxiv-cs-lg

UniMem: Unifying Multimodal Memory and Control for Vision-Language-Action Models

arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long-

safetyarxiv-cs-cv

Human-JEPA: A Human-Centric Vision Model that Perceives and Anticipates

arXiv:2608.21160v1 Announce Type: new Abstract: Machines that understand humans should perceive the present and anticipate the future. Existing human-centric vision model are pretrained on human image

24 Aug 2026

InfinityEdit: Infinite Video Editing with a Lightweight Edit-Ignition Adapter

arXiv:2608.20910v1 Announce Type: new Abstract: With large pretrained models, existing methods have effectively improved instruction-based video editing. However, most of them rely on an in-place edit

24 Aug 2026

Learning to Orchestrate Vision Foundation Models for Multi-Task Dense Prediction

arXiv:2606.15765v2 Announce Type: replace Abstract: Vision foundation models (VFMs) exhibit complementary strengths shaped by their pretraining objectives. Yet prevailing methods for multi-task dense

24 Aug 2026

Manifold Drift in Flow Preference Optimization: A Root Cause of Reward Hacking

arXiv:2608.20011v1 Announce Type: new Abstract: Preference optimization is a standard alignment method for generative models, yet extending it to continuous-time dynamics remains non-trivial. In flow

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

arXiv:2608.19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP

RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations

arXiv:2608.19735v1 Announce Type: new Abstract: We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation. RecPFN is pretrained entirely on synthetic cli

researcharxiv-cs-lg

Systematic Evaluation of TabPFN-TS for Zero-Shot Probabilistic Heat Load Forecasting in District Heating Networks

arXiv:2608.20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-

researcharxiv-cs-lg

ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning