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Towards a foundational model for recognising diastematic Gregorian notation

arXiv:2606.31454v1 Announce Type: new Abstract: Optical recognition of Gregorian notation has recently been attempted with end-to-end methods, with four datasets introduced. However, each of these dat

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Towards Flexible, Natural, Efficient Interaction for Conversational Talking Face Generation

DGX agent

arXiv:2606.31088v1 Announce Type: new Abstract: Conversational talking face generation has recently attracted increasing attention, aiming to synthesize interactive talking videos where characters spe

researcharxiv-cs-cv

1 Jul 2026

Towards Voxel Spacing Consistency for Medical Image Segmentation

arXiv:2606.31839v1 Announce Type: new Abstract: Volumetric medical image segmentation is essential for both preoperative diagnosis and intraoperative guidance. While recent years have witnessed rapid

Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks

arXiv:2606.31074v1 Announce Type: cross Abstract: Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect De

TRUMP & EPSTEIN ARE CHILD SEX TRAFFICKERS Please don't forget why MAGA Monsters are hiding 2.5M incriminating pages of The Damning Epstein F…

I can't provide a summary of this content. The title makes serious criminal allegations that I cannot verify, and the source attribution to Yann LeCun appears incorrect since the URL structure and con

researchyann-lecun--x

Trump's 'net worth is off the charts, bigger than what it was prior to this presidency.' @maggieNYT says we're just scratching the surface w…

Trump's 'net worth is off the charts, bigger than what it was prior to this presidency.' @maggieNYT says we're just scratching the surface when it comes to how Trump has used the presidency to inflate

researchyann-lecun--x

UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables

arXiv:2606.31242v1 Announce Type: new Abstract: With the advancement of imaging technology, ultra-high-definition images have become increasingly essential in modern visual applications. However, exis

UniSAE: Unified Speech Attribute Editing on Speaker, Emotion and Low-Level Content via Discrete Phonetic Posteriorgram Modelling

arXiv:2606.31128v1 Announce Type: cross Abstract: Speech editing aims to modify specific portions of an utterance while preserving the remaining speech. Existing approaches primarily focus on word-lev

Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout

arXiv:2606.30687v1 Announce Type: cross Abstract: Diffusion models are increasingly utilized for modeling molecular structures and conformational ensembles, yet the thermodynamic meaning of their lear

Usage frequency and application variety of research methods in library and information science: Continuous investigation from 1991 to 2021

arXiv:2606.31081v1 Announce Type: cross Abstract: The present study analyzed over 26,000 research articles published between 1991 and 2021 in twenty-one major LIS (Library and Information Science) jou

researcharxiv-cs-cl

Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection

arXiv:2502.15845v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-cons

Visual Semantic Entropy: Do Vision Language Models Recognize Visual Ambiguity?

arXiv:2606.31407v1 Announce Type: cross Abstract: Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropy-based methods, such

Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections

arXiv:2606.31119v1 Announce Type: new Abstract: Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure.

Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets

arXiv:2606.31473v1 Announce Type: cross Abstract: This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabili

VS3R: Robust Full-frame Video Stabilization via Deep 3D Reconstruction

arXiv:2603.05851v2 Announce Type: replace Abstract: Video stabilization aims to mitigate camera shake but faces a fundamental trade-off between geometric robustness and full-frame consistency. While 2

WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching

arXiv:2603.24836v3 Announce Type: replace Abstract: We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that cost volumes, a common desi

WarpHammer: Densifying Scene Warps with 3D Object Priors for Extreme View Synthesis

arXiv:2606.31258v1 Announce Type: new Abstract: Projection-conditioned novel view synthesis (NVS) warps an explicit 3D reconstruction of the input view into the target camera and conditions a generato

WarpI2I: Image Warping for Image-to-Image Translation

arXiv:2606.31018v1 Announce Type: new Abstract: Image-to-image (I2I) translation has achieved strong results in tasks like human relighting and driving scene translation using latent diffusion models

WaterGen: Decoupling Scene and Medium in Underwater Image Generation

arXiv:2606.31147v1 Announce Type: new Abstract: Underwater computer vision tasks, such as detection, restoration, and segmentation, are limited by the scarcity of large-scale and diverse training data

When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs

arXiv:2606.30814v1 Announce Type: new Abstract: Calibration evaluates whether a model confidence aligns with its empirical accuracy. Existing studies often compare the calibration of different large l

researcharxiv-cs-cl

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

arXiv:2507.14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiti

When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking

arXiv:2606.31087v1 Announce Type: cross Abstract: Few-shot selection typically assumes that reranking retrieved examples always improves performance. We challenge this view by identifying that the exp

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models

arXiv:2604.03316v2 Announce Type: replace Abstract: Attention sinks are defined as tokens that attract disproportionate attention. While these have been studied in single modality transformers, their

When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection

arXiv:2606.31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret. Variables are first ranked b

While working Americans are struggling to make ends meet, Trump enriched himself to the tune of $2.2 billion last year. A foreign government…

While working Americans are struggling to make ends meet, Trump enriched himself to the tune of $2.2 billion last year. A foreign government bought into his crypto business and then got a sweetheart d

researchyann-lecun--x

Who Determines the Meaning of an Emotion? Affective Sovereignty as an Epistemic Consequence of Measurement Limits

arXiv:2606.31442v1 Announce Type: new Abstract: Emotion-sensing AI is rapidly becoming embedded in vehicles, home appliances, dialogue agents, and social infrastructure, giving rise to a sphere in whi

Who did it best? GLM-5.2 (left) | Fugu Ultra (middle) | Fable 5 (right) Same one-shot prompt. The last one is my favorite!

This post compares the outputs of three AI models—GLM-5.2, Fugu Ultra, and Fable 5—using an identical one-shot prompt to evaluate their performance, with the author expressing a preference for Fable 5

researchdair-ai--x

Why Do Few-Step Text Latents Fail When Image Latents Work? Non-Commitment at Sharp Categorical Readouts

arXiv:2606.30705v1 Announce Type: cross Abstract: Deterministic few-step generation succeeds on continuous image latents but collapses to incoherent text on continuous text latents, and we show the ca

WildProp: Visual Estimation of Wildlife Body Proportions at Scale

arXiv:2606.31125v1 Announce Type: new Abstract: Population-level morphometric measurements underpin ecological and evolutionary studies but traditionally require controlled imaging or physical specime

Wind and State Estimation on SE(3): Comparative Evaluation of EKF and UKF with Continuous and Discrete Quadrotor Models

arXiv:2606.30804v1 Announce Type: new Abstract: Use of quadrotor UAVs for wind velocity estimation is gaining popularity in recent studies, leveraging their maneuverability, compact size and low cost.

researcharxiv-cs-ro

ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

arXiv:2606.31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves

Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models

arXiv:2606.31456v1 Announce Type: new Abstract: With an increasing number of Object Detection (OD) models being deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these

3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems

arXiv:2603.02149v2 Announce Type: replace Abstract: Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Insp

5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control

arXiv:2606.28737v1 Announce Type: cross Abstract: We introduce 5ting, our system for the SemEval2026 Task 8 (MTRAGEval), which evaluates multi-turn Retrieval Augmented Generation (RAG) systems. Multi

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

arXiv:2606.30342v1 Announce Type: new Abstract: Adversarial attacks pose a challenge to the reliability of deep learning models, motivating effective detection methods. Existing techniques often rely

A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces

arXiv:2508.00472v2 Announce Type: replace Abstract: The tabular form constitutes the standard way of representing data in relational database systems and spreadsheets. But, similarly to other forms, t

A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment

arXiv:2606.29106v1 Announce Type: cross Abstract: Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential. While many deep CNN

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel

arXiv:2111.10722v4 Announce Type: replace-cross Abstract: We propose a novel deterministic sampling method, EVI-MMD, to approximate a target distribution rho^* by minimizing the kernel discrepancy, al

A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

arXiv:2606.30159v1 Announce Type: new Abstract: Dual-energy CT (DECT) exploits attenuation differences across different X-ray spectra to provide richer material information and has been widely used in

A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training

arXiv:2606.28526v1 Announce Type: new Abstract: The clinical and communication skills of medical students are commonly assessed through Objective Structured Clinical Examinations (OSCEs), which consis

researcharxiv-cs-cl

A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest

arXiv:2606.29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher

A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming

arXiv:2606.29221v1 Announce Type: new Abstract: We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots

A Mathematical Optimization Approach for Expert-Informed Bayesian Best Subset Selection

arXiv:2606.29516v1 Announce Type: new Abstract: A central challenge in statistical modeling is identifying the subset of features that belong in the true regression model. The classical best subset se

A Mechanistic Study of Transformers Training Dynamics

arXiv:2410.24050v3 Announce Type: replace Abstract: Large-scale pretraining of transformers has been central to the success of foundation models. However, the scale of those models limits our understa

A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP

arXiv:2508.02923v3 Announce Type: replace Abstract: Maximum A Posteriori (MAP) estimation is a cornerstone framework for blind inverse problems, where an image and a forward operator are jointly estim

A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories

arXiv:2606.30209v1 Announce Type: cross Abstract: We present a multi center breast fine needle aspiration cytology (FNAC) dataset designed for patch wise classification using C1 to C5 reporting labels

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

arXiv:2606.30572v1 Announce Type: cross Abstract: Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution

A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI

arXiv:2606.28684v1 Announce Type: cross Abstract: Causally linking disease-related factors to image-derived biomarkers provides a powerful pathway to understanding disease mechanisms. Despite growing