Search: “pretraining”

Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport

arXiv:2608.17151v1 Announce Type: new Abstract: Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context

19 Aug 2026

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

TokEval: A Tokenizer Evaluation Suite

DGX agent

arXiv:2608.18062v1 Announce Type: new Abstract: Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities.

safetyarxiv-cs-cl

19 Aug 2026

When to Review: Spaced Repetition for Continual Pre-Training of Language Models

arXiv:2608.17530v1 Announce Type: new Abstract: Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a globa

19 Aug 2026

Tutorials

An Analytical-Prior Framework for Data-Efficient Prediction of Sound-Reduction Frequencies in Rectangular Side-Branch Helmholtz Resonators

arXiv:2608.16873v1 Announce Type: new Abstract: High-fidelity finite-element simulations can provide accurate numerical predictions for side-branch resonators, but large simulation datasets are expens

tutorialsarxiv-cs-lg

Decorrelation Is Not Complementarity: Skill, Not Lineage, Governs Trusted-Monitor Ensembles

arXiv:2608.16190v1 Announce Type: cross Abstract: Trusted monitoring has a cheap, trusted model score a stronger untrusted model's actions, and a diverse ensemble of them beats a single stronger monit

researcharxiv-cs-lg

Applications

Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity

arXiv:2608.16612v1 Announce Type: cross Abstract: An accurate estimation of the state of health (SOH) underpins a safe and optimized use of the battery system. Although compelling, data-driven SOH est

applicationsarxiv-cs-ai

Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning

arXiv:2608.14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual varia

EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation

arXiv:2603.18739v4 Announce Type: replace Abstract: Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict computation and memory bud

From Generalist to Specialist: A Context-Fusion Framework for Endoscopic Polyp Reporting with a Frozen VLM

arXiv:2608.15580v1 Announce Type: new Abstract: Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morpho

GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture

model-releasesarxiv-cs-ro

arXiv:2608.15875v1 Announce Type: new Abstract: Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task c

Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning

arXiv:2608.15770v1 Announce Type: new Abstract: Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the diff

safetyarxiv-cs-lg

pico-type: A 1.5M-Parameter Byte-Level Multi-Head Content Classifier

arXiv:2608.14658v1 Announce Type: cross Abstract: We introduce pico-type, a byte-level multi-head content classifier with approximately 1.5 million parameters that simultaneously predicts seven conten

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

model-releasesarxiv-cs-cv

arXiv:2608.16793v1 Announce Type: new Abstract: Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model.

Training-Free Multi-Concept Image Editing

arXiv:2602.20839v3 Announce Type: replace Abstract: Training-free image editing with diffusion models is highly desirable yet is complex and remains a significant challenge. While recent optimisation-

Turning spectra into images improves plant trait retrieval with 2D-CNNs

arXiv:2608.16661v1 Announce Type: new Abstract: Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spect

Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models

arXiv:2608.13760v1 Announce Type: cross Abstract: Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors? This

safetyarxiv-cs-ai

Attributing Preprocessing Invariance in Spectral Foundation Models

arXiv:2608.14227v1 Announce Type: new Abstract: Preprocessing invariance is an appealing goal for spectral foundation models: a frozen model should remain useful when laboratories preprocess spectra d

CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models

arXiv:2607.14975v2 Announce Type: replace Abstract: Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data, radio configurations, partitions, adaptatio

Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification

arXiv:2608.13866v1 Announce Type: cross Abstract: Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some

researcharxiv-cs-cl

Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion

arXiv:2608.13947v1 Announce Type: new Abstract: High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the stru

researcharxiv-cs-cl

Secret-Stego Dissimilarity as a Design Axis: Invertible Coverless Image Steganography with Diffusion Models

arXiv:2608.13597v1 Announce Type: cross Abstract: Coverless image steganography (CIS) synthesizes a stego image rather than modifying an existing cover image, enabling authorized recipients to reconst

Source-Agnostic Image Translation Based on Latent Aware Adaptive Masking

arXiv:2608.14046v1 Announce Type: new Abstract: In this work, we propose a source-agnostic framework that dynamically refines a binary mask throughout the reverse diffusion process by computing the di

Bias Mitigation in Face Recognition via Demographic-based Supervised Contrastive Learning

arXiv:2608.12971v1 Announce Type: new Abstract: Face recognition systems have been shown to be biased toward certain demographic groups by exhibiting different error rates across gender, age, or ethni

safetyarxiv-cs-cv

EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

arXiv:2608.13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and indi

Agents

MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification

arXiv:2608.13463v1 Announce Type: cross Abstract: Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty

agentsarxiv-cs-ai

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement

arXiv:2608.13129v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong results on mathematical reasoning benchmarks yet remain unreliable on elementary numerical tasks, including

Towards Context-Aware Clinical Motion Understanding in Daily Living at Home: Freezing of Gait Detection with Egocentric Vision

arXiv:2608.13283v1 Announce Type: new Abstract: Understanding motion in daily living requires context beyond kinematics, because similar inertial patterns during activities of daily living (ADLs) can

Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models

arXiv:2608.12078v1 Announce Type: cross Abstract: Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, wh

safetyarxiv-cs-ai

13 Aug 2026

Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension

model-releasesarxiv-cs-cl

arXiv:2608.10296v1 Announce Type: new Abstract: One might imagine that architectural variations within the dense transformer paradigm have a limited effect on accuracy. However, we demonstrate that th

Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models

arXiv:2608.10525v1 Announce Type: cross Abstract: Historical context integration presents a fundamental challenge for Vision-Language Models (VLMs) in sequential decision-making tasks. Current VLMs pr

Four small architecture decisions can cost up to 47% of a model's long-context performance. New research from Ai2, Carnegie Mellon, and the …

Four small architecture decisions can cost up to 47% of a model's long-context performance. New research from Ai2, Carnegie Mellon, and the University of Washington isolates them. Normalization, GQA,

model-releasesdair-ai--x

Multi-View Relational Distillation for Spatial Reasoning with Vision-Language Models

arXiv:2608.10864v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong image and video understanding, yet their visual-spatial representations remain geometrically fragile,

safetyarxiv-cs-cv

Multilingual Embedding Probes Fail to Generalize Across Learner Corpora

arXiv:2604.07095v2 Announce Type: replace Abstract: Do multilingual embedding models encode a language-general representation of proficiency? We investigate this by training linear and non-linear prob

researcharxiv-cs-cl

SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

model-releasesarxiv-cs-cv

arXiv:2608.11142v1 Announce Type: new Abstract: Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them

Self-Geometry: GT-Free and Plug-and-Play Test-Time Adaptation for Geometrically Consistent 3D Vision Foundation Models

arXiv:2608.10708v1 Announce Type: new Abstract: Recent Vision Foundation Models (VFMs) predict depth, camera pose, and pointmap in a single forward pass without per-scene optimization, achieving stron

Local Ai

Structural Guidance for Unified Joint Demosaicing and Denoising

arXiv:2608.09995v1 Announce Type: cross Abstract: Joint demosaicing and denoising is a fundamental step in camera image signal processing, yet remains challenging because different Bayer-like color fi

local-aiarxiv-cs-cv

Watching Synthetic Videos: Aligning Cross-modal Representations with Visual Synthesis for Zero-shot Video Captioning

arXiv:2608.11013v1 Announce Type: new Abstract: Text-only training is a popular paradigm in zero-shot video captioning, where the video distribution is not available to the model during training, lead

safetyarxiv-cs-cv

Whisper-Aware LLM: Self-Supervised Uncertainty Learning for Robust Whispered Speech Recognition

arXiv:2608.10836v1 Announce Type: cross Abstract: The signal ambiguity of whispered speech drives ASR systems toward two opposing failure modes: failing to capture whispered speech or hallucinatory tr

A continually expandable foundation model for brain MRI

arXiv:2608.08319v1 Announce Type: new Abstract: Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, po

Local Ai

Classical SU(2) Models Match or Exceed Shallow Variational Quantum Circuits on Vision Benchmarks

arXiv:2608.07822v1 Announce Type: cross Abstract: Quaternion-valued neural networks and variational quantum circuits (VQCs) both derive local transformations from SU(2) geometry, yet their performance

local-aiarxiv-cs-lg

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting

arXiv:2608.07693v1 Announce Type: cross Abstract: Generative traffic video forecasting aims to synthesize long-horizon, temporally coherent future videos of traffic scenes from a short observation his

Applications

Distilling Physical Priors into Streaming World Models

arXiv:2608.07981v1 Announce Type: new Abstract: Streaming world models predict future visual states online while maintaining physically coherent dynamics over long horizons. However, their rollouts of

applicationsarxiv-cs-cv

DoGMA: A Central-Dogma-Guided Foundation Model for Multi-Omics Alignment and Multi-Task Learning in Oncology

arXiv:2608.08148v1 Announce Type: cross Abstract: Attention mechanisms have been widely utilized in modern deep learning, and many existing multi-omics models inherit their conventional use to allow u

safetyarxiv-cs-ai

GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction

model-releasesarxiv-cs-cv

arXiv:2608.09493v1 Announce Type: new Abstract: Long-horizon future-frame prediction is important for autonomous driving, traffic surveillance, and intelligent transportation systems, yet remains chal

LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608.07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs. We

researcharxiv-cs-lg

LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection

arXiv:2608.09633v1 Announce Type: new Abstract: Face presentation attack detection (PAD) aims to reliably detect a wide range of presentation attacks. While PAD methods achieve strong performance with

NeuPAT: Neuron-aware Plasticity Allocation Tuning for Language-Preserving MLLMs

arXiv:2608.08107v1 Announce Type: cross Abstract: Multimodal expansion of large language models (LLMs) enables new perceptual capabilities but often compromises the language intelligence acquired duri

Test-time Generalization for Physics through Neural Operator Splitting

model-releasesarxiv-cs-lg

arXiv:2602.00884v2 Announce Type: replace Abstract: Neural operators have shown promise in learning solution maps of partial differential equations (PDEs), but they often struggle to generalize when t