Search: “pretraining”

arXiv:2603.13059v2 Announce Type: replace-cross Abstract: Cost-per-click (CPC) in paid search is an auction-generated outcome shaped by a competitive landscape that is only partially observable from a

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Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning

DGX agent

arXiv:2607.00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and e

researcharxiv-cs-ai

2 Jul 2026

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning

arXiv:2607.00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT). How

DriveVA: Video Action Models are Zero-Shot Drivers

arXiv:2604.04198v2 Announce Type: replace Abstract: Generalization is a central challenge in autonomous driving, as real-world deployment requires robust performance under unseen scenarios, sensor dom

EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology

arXiv:2607.01039v1 Announce Type: cross Abstract: Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk str

Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition

arXiv:2607.00535v1 Announce Type: cross Abstract: Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between nois

Applications

Geo-ID: Test-Time Geometric Consensus for Cross-View Consistent Intrinsics

arXiv:2603.13859v2 Announce Type: replace Abstract: Intrinsic image decomposition aims to estimate physically based rendering (PBR) parameters such as albedo, roughness, and metallicity from images. W

applicationsarxiv-cs-cv

Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization

model-releasesarxiv-cs-lg

arXiv:2607.00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention modu

HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding

arXiv:2607.00428v1 Announce Type: new Abstract: CLIP (Contrastive Language-Image Pre-training) has become a de facto paradigm for image-text alignment, but it struggles with long-context descriptions

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Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models

arXiv:2607.01222v1 Announce Type: new Abstract: Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due t

Linkify: Learning from Interface-Augmented Assembly Graphs

arXiv:2607.01205v1 Announce Type: new Abstract: We present Linkify, a framework for learning from interface-augmented assembly graphs to enable context-aware part retrieval in mechanical assemblies. W

Loss Smoothing for Stable Adaptation Under Distribution Shift

arXiv:2607.00634v1 Announce Type: cross Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods ty

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs

arXiv:2602.05275v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown immense promise in universal multimodal retrieval, which aims to find relevant items of various

MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos

arXiv:2607.00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input. Exis

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

arXiv:2602.21397v2 Announce Type: replace Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrai

Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors

arXiv:2603.15129v3 Announce Type: replace Abstract: We present a novel paradigm for ultra-low-bitrate image compression (ULB-IC) that exploits the ``temporal'' evolution in generative image compressio

PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding

arXiv:2512.20907v2 Announce Type: replace Abstract: 3D Visual Grounding (3DVG) is a critical bridge from vision-language perception to robotics, requiring both language understanding and 3D scene reas

SleepLM: Natural-Language Intelligence for Human Sleep

arXiv:2602.23605v2 Announce Type: replace Abstract: We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural lan

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Applications

Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns

arXiv:2607.00829v1 Announce Type: new Abstract: While garments are essential for realistic digital humans, their topological variety makes them much harder to model than parametric bodies. Traditional

applicationsarxiv-cs-cv

The State-Prediction Separation Hypothesis

arXiv:2607.01218v1 Announce Type: cross Abstract: Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions. We formulate

Applications

TiRex-2: Generalizing TiRex to Multivariate Data and Streaming

arXiv:2607.01204v1 Announce Type: new Abstract: We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both p

applicationsarxiv-cs-lg

Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors

arXiv:2607.00447v1 Announce Type: new Abstract: Large language models often produce hallucinated answers that violate prompt-level constraints. A key diagnostic question is whether these failures refl

safetyarxiv-cs-cl

Wake up for Touch! Mask-isolated Tactile Alignment Learning in MLLMs

arXiv:2607.00302v1 Announce Type: new Abstract: Touch supplies the physical grounding needed to perceive intrinsic material properties, such as friction and compliance, that vision alone often cannot

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

arXiv:2606.31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environ

safetyarxiv-cs-ai

A Realistic Protocol for Evaluation of Weakly Supervised Object Localization

arXiv:2404.10034v3 Announce Type: replace Abstract: Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-

A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management

arXiv:2606.30997v1 Announce Type: new Abstract: We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior f

ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models

arXiv:2606.30682v1 Announce Type: cross Abstract: Recent advances in language--audio retrieval have been largely driven by contrastive dual-encoder architectures that align audio and text in a shared

Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation

arXiv:2606.31198v1 Announce Type: cross Abstract: Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions. While conventional 2D metho

E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes

arXiv:2604.04834v2 Announce Type: replace Abstract: Robotic Vision-Language-Action (VLA) models generalize well for open-ended manipulation, but their perception is fragile under sensing-stage degrada

ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies

arXiv:2606.31132v1 Announce Type: new Abstract: Generative control policies (GCPs), such as diffusion policies and flow-based vision-language-action models, enable test-time scaling in robot control.

safetyarxiv-cs-ro

EpiMask: Leveraging Epipolar Distance Based Masks in Cross-Attention for Satellite Image Matching

arXiv:2603.21463v2 Announce Type: replace Abstract: The deep-learning based image matching networks can now handle significantly larger variations in viewpoints and illuminations while providing match

FaceMoE: Mixture of Experts for Low-Resolution Face Recognition

arXiv:2606.32040v1 Announce Type: new Abstract: Low-resolution face recognition (LR-FR) remains a challenging task due to poor feature extraction and aggregation, as probe images often contain limited

FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics

arXiv:2606.31136v1 Announce Type: new Abstract: Few-shot segmentation asks a model to delineate a target class in a query image from only a handful of annotated examples, a setting most acute in remot

G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation

arXiv:2601.03510v3 Announce Type: replace Abstract: Point cloud segmentation is critical for 3D scene understanding. However, sparse and irregular point distributions provide limited appearance eviden

safetyarxiv-cs-cv

Generating consensus and dissent on massive discussion platforms with a semantic-vector model

model-releasesarxiv-cs-cl

arXiv:2601.13932v2 Announce Type: replace-cross Abstract: Reaching consensus on massive discussion networks is critical for reducing noise and achieving optimal collective outcomes. However, the natur

Local Ai

Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization

arXiv:2606.30813v1 Announce Type: cross Abstract: Deep neural networks with repeated architectural blocks, such as transformers, often exhibit structured relationships across layers that emerge during

local-aiarxiv-cs-ai

Learning to Select, Not Relearn: Hard-Routed Mixtures of Reasoning LoRAs

arXiv:2606.31413v1 Announce Type: new Abstract: Composing independently trained LoRA adapters into a single large language model is useful for multi-domain adaptation, especially when the original tra

LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR

arXiv:2601.14251v2 Announce Type: replace Abstract: We present LightOnOCR-2-1B, a 1B-parameter end-to-end multilingual vision--language model that converts document images (e.g., PDFs) into clean, nat

Making Sense of Touch from the Child's View for Contrastive Learning

arXiv:2606.31943v1 Announce Type: new Abstract: Is the sense of touch a mechanism for human babies' learning of visual concepts? If so, can we quantify its importance, and to what extent do babies rel

researcharxiv-cs-lg

MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents

arXiv:2606.31167v1 Announce Type: cross Abstract: VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control. However, current s

MSNN-LINet: Cross-Modal Learning via Continuous Linear Integration

arXiv:2606.31135v1 Announce Type: new Abstract: We present LINet (Linear Integration Network), a Multi-Stream Neural Network (MSNN) for RGB-D scene classification. Current multi-modal architectures tr

{Phi}eat: Physically Grounded Material Feature Representation

arXiv:2511.11270v2 Announce Type: replace Abstract: While foundation models have emerged as general-purpose visual backbones, their representations are primarily optimized for semantics and lack expli

PolyFlow: Continuous Topology Embedding Flow Matching for Artist-style Mesh Generation

arXiv:2606.30673v1 Announce Type: cross Abstract: Autoregressive Transformers dominate high-quality mesh generation by producing artist-worthy topologies, yet their inherent sequential decoding induce

Local Ai

PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding

arXiv:2606.31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform rea

local-aiarxiv-cs-ai

SPFSplatV2: Efficient Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views

arXiv:2509.17246v2 Announce Type: replace Abstract: We introduce SPFSplatV2, an efficient feed-forward framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth pose

StemVLA:An Open-Source Vision-Language-Action Model with Future 3D Spatial Geometry Knowledge and 4D Historical Representation

arXiv:2602.23721v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models integrate visual observations and language instructions to predict robot actions, demonstrating promising

SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation

arXiv:2606.31259v1 Announce Type: cross Abstract: Diffusion-based text-to-audio (TTA) models achieve impressive synthesis quality but suffer from high inference latency due to iterative multi-step den

Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents

arXiv:2606.31648v1 Announce Type: new Abstract: We present LuckyStar 111B, a 111B-parameter hybrid reasoning model developed through a collaboration between Cohere and LG CNS for Korean-English enterp