Search: “pretraining”

MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs

arXiv:2506.01850v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in instruction-following tasks by integrating pretrained visual enco

8 Jun 2026

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Research

One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models

DGX agent

arXiv:2602.00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical me

researcharxiv-cs-lg

8 Jun 2026

Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning

arXiv:2606.06835v1 Announce Type: new Abstract: The performance gap across languages in LLMs is well documented, and closing it natively requires pretraining or fine-tuning on corpora that, for most l

safetyarxiv-cs-cl

8 Jun 2026

Local Ai

AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding

arXiv:2606.06155v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robo

local-aiarxiv-cs-cv

Flow-based Policy Adaptation without Policy Updates

arXiv:2606.06461v1 Announce Type: new Abstract: Leveraging prior knowledge from pretrained policies, foundation models, or human operators offers an efficient alternative to learning robot skills from

safetyarxiv-cs-ro

Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning

arXiv:2606.05506v1 Announce Type: new Abstract: We propose a sensor-guided adaptive contrastive learning framework for visual representation learning in PointGoal navigation. During training, privileg

safetyarxiv-cs-cv

Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning

arXiv:2601.21700v3 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining dat

safetyarxiv-cs-cl

Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction

arXiv:2602.23214v2 Announce Type: replace Abstract: Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained gen

safetyarxiv-cs-cv

4 Jun 2026

SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization

arXiv:2505.11166v3 Announce Type: replace-cross Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-

4 Jun 2026

An Asymptotic Theory of Chain-of-Thought in In-Context Learning

model-releasesarxiv-cs-lg

arXiv:2606.03217v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermed

Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition

arXiv:2606.03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data. This requires more than recalling a functi

How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models

arXiv:2601.22841v2 Announce Type: replace Abstract: Large-scale foundation models (FMs) in remote sensing (RS) (denoted as RS FMs) are developed following paradigms established in computer vision (CV)

researcharxiv-cs-cv

Investigating Adversarial Robustness of Multi-modal Large Language Models

model-releasesarxiv-cs-cv

arXiv:2606.03713v1 Announce Type: new Abstract: Multi-modal Large Language Models (MLLMs) achieve strong performance on vision-language tasks, but incorporating visual inputs through a vision encoder

Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models

arXiv:2303.15619v2 Announce Type: replace-cross Abstract: The choice of which tokens to mask is a central, under-examined design decision in masked language modeling (MLM). Standard pretraining masks

A Foundation Model for Wearable Movement Data in Mental Health Research

arXiv:2411.15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, r

applicationsarxiv-cs-ai

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606.02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robot

safetyarxiv-cs-lg

Echo: A Joint-Embedding Predictive Architecture for Speaker Diarization and Speech Recognition in a Shared Latent Space

arXiv:2606.01909v1 Announce Type: cross Abstract: We present Echo, a proof-of-concept audio system built around a single 25 M-parameter ViT encoder. The encoder is pretrained with a JEPA objective and

Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models

arXiv:2504.03635v4 Announce Type: replace Abstract: Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pre

From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data

arXiv:2606.00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most exi

applicationsarxiv-cs-ai

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation

arXiv:2606.00703v1 Announce Type: cross Abstract: Low-precision pretraining (FP8, MXFP4, NVFP4) is now standard for frontier language models, yet the literature is almost entirely achievability -- alg

KDH-CAD: Knowledge-data hybrid CAD learning under data scarcity

arXiv:2606.01702v1 Announce Type: cross Abstract: Deep learning in computer-aided design (CAD) remains fundamentally constrained by the data scarcity challenge: authentic CAD data is difficult to coll

applicationsarxiv-cs-lg

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning

arXiv:2602.08689v2 Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the samp

safetyarxiv-cs-lg

On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

arXiv:2512.10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities construction

Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models

model-releasesarxiv-cs-cl

arXiv:2606.00284v1 Announce Type: new Abstract: While continual pretraining~(CPT) is a practical way to extend large language models to new languages, naive finetuning on targeted data erodes existing

RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models

model-releasesarxiv-cs-ro

arXiv:2606.02277v1 Announce Type: new Abstract: Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should gu

Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression

arXiv:2606.00512v1 Announce Type: new Abstract: In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce

TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge

model-releasesarxiv-cs-cv

arXiv:2605.24470v2 Announce Type: replace Abstract: Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an im

Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation

arXiv:2606.02237v1 Announce Type: new Abstract: Distribution Matching Distillation (DMD) compresses pretrained diffusion models into efficient few-step generators by aligning their noised distribution

Chatterbox-Flash: Prior-Calibrated Block Diffusion for Streaming Zero-Shot TTS

arXiv:2605.30748v1 Announce Type: cross Abstract: We present Chatterbox-Flash, a zero-shot text-to-speech model obtained by fine-tuning a pretrained autoregressive TTS decoder into a block-diffusion d

NumLeak: Public Numeric Benchmarks as Latent Labels in Foundation Models

arXiv:2605.30393v1 Announce Type: cross Abstract: Public numeric benchmarks appear in pretraining, so an evaluation that conditions on a date may be measuring memorized recall rather than out-of-sampl

applicationsarxiv-cs-ai

Parallel Tempering Initial Sampling in Inference-Time Reward Alignment

arXiv:2605.30991v1 Announce Type: cross Abstract: Inference-time reward alignment steers pretrained diffusion and flow-based generative models to satisfy user-specified rewards without retraining. Rec

safetyarxiv-cs-cv

Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems

arXiv:2506.00175v5 Announce Type: replace-cross Abstract: Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, whe

AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching

arXiv:2603.01006v2 Announce Type: replace-cross Abstract: REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teacher

COMET: Concept Space Dissection of the Modality Gap in Audio-Text Multimodal Contrastive Embeddings

arXiv:2605.29628v1 Announce Type: cross Abstract: Contrastive Language-Audio Pretraining (CLAP) models are widely used for audio understanding and support modality-agnostic condition swapping in many

Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts

arXiv:2605.29283v1 Announce Type: cross Abstract: Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single aver

Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models

arXiv:2601.14758v4 Announce Type: replace-cross Abstract: Post-training pretrained autoregressive models (ARMs) into masked diffusion models (MDMs) has emerged as a cost-effective way to overcome the

Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2605.30257v1 Announce Type: new Abstract: We present Stable-Layers, a reinforcement learning framework that eliminates the need for paired supervision by fine-tuning a pretrained layer decomposi

The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling

arXiv:2605.28864v1 Announce Type: new Abstract: The Cognitive Categorical Transformer (CCT) is a 306M-parameter architecture that augments a pretrained GPT-2 Small backbone with cognitively grounded c

Local Ai

Unsupervised Semantic Segmentation Facilitates Model Understanding

arXiv:2605.29691v1 Announce Type: new Abstract: Self-supervised learning (SSL) has produced a diverse landscape of vision transformers (ViTs) whose pretrained representations support a wide range of d

local-aiarxiv-cs-cv

Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data

arXiv:2603.10961v2 Announce Type: replace Abstract: Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labe

Entropy-aware Masking for Masked Language Modeling

arXiv:2605.28526v1 Announce Type: new Abstract: Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the

Tutorials

Formula-One Prompting: A Composable Equation-First Prefix for Applied Mathematics

arXiv:2601.19302v3 Announce Type: replace Abstract: This paper introduces Formula Prompting (FP) and Formula-One Prompting (F-1), two single-call methods that elicit governing equations before solving

tutorialsarxiv-cs-cl

Hardware

Heterogeneous Parallelism for Multimodal Large Language Model Training

arXiv:2605.27678v1 Announce Type: new Abstract: Foundation model training is becoming multimodal, from post-training pipelines to large-scale pretraining. As modality coverage broadens, context window

hardwarearxiv-cs-lg

On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation

arXiv:2601.06329v2 Announce Type: replace-cross Abstract: Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content while preserving a

Safe In-Context Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

Sense Representations Are Inducible Interfaces

arXiv:2605.28669v1 Announce Type: cross Abstract: Sense representations (explicit, per-token meaning decompositions) are useful for disambiguation, steering, and cross-lingual alignment, but existing

Sparse Scheduled Diffusion Guidance for Inverse Problems

arXiv:2603.07860v2 Announce Type: replace Abstract: Pretrained diffusion models are effective priors for Bayesian inverse problems, but posterior sampling with these priors is often costly because dat

Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models

arXiv:2605.26332v1 Announce Type: cross Abstract: Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been