applications

Causal Discovery Should Embrace the Wisdom of the Crowd

arXiv:2603.02678v3 Announce Type: replace Abstract: This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and r

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CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks

DGX agent

arXiv:2601.22427v2 Announce Type: replace-cross Abstract: Temporal link prediction is crucial for rapidly growing social networks. Existing methods often overlook the underlying causal mechanisms that

applicationsarxiv-cs-ai

12 May 2026

Complete Evidence Extraction with Model Ensembles: A Case Study on Medical Coding

arXiv:2511.07055v3 Announce Type: replace Abstract: High-stakes decisions informed by decision support systems require explicit evidence. While prior work focuses on short sufficient evidence, regulat

applicationsarxiv-cs-cl

Conditional anomaly detection methods for patient-management alert systems

arXiv:2605.10847v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f

Continuum Robot Localization using Distributed Time-of-Flight Sensors

arXiv:2602.07209v2 Announce Type: replace Abstract: Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g

applicationsarxiv-cs-ro

Count Anything at Any Granularity

arXiv:2605.10887v1 Announce Type: new Abstract: Open-world object counting remains brittle: despite rapid advances in vision-language models (VLMs), reliably counting the objects a user intends is far

Counterfactual Stress Testing for Image Classification Models

arXiv:2605.10894v1 Announce Type: new Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner hardwa

DANCE: Detect and Classify Events in EEG

arXiv:2605.10688v1 Announce Type: new Abstract: Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to

Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning

arXiv:2605.10166v1 Announce Type: new Abstract: Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or e

applicationsarxiv-cs-ro

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis

arXiv:2605.08138v1 Announce Type: new Abstract: Synthetic data has emerged as a crucial solution to the data scarcity bottleneck in large language models (LLMs), particularly for specialized domains a

Debiased Front-Door Learners for Heterogeneous Effects

arXiv:2509.22531v2 Announce Type: replace-cross Abstract: In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-do

Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection

arXiv:2605.09028v1 Announce Type: new Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perfor

Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency

arXiv:2510.07136v2 Announce Type: replace-cross Abstract: We study spectral graph clustering under edge differential privacy. We propose a matrix shuffling mechanism that combines randomized edge flip

Digital Image Forgery Detection Using Transfer Learning

arXiv:2605.08167v1 Announce Type: cross Abstract: The increasing availability of advanced image editing tools has led to a significant rise in manipulated digital content, posing serious challenges fo

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605.09697v1 Announce Type: new Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a

Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT

arXiv:2605.09719v1 Announce Type: cross Abstract: Large-scale 3D vision-language models (VLMs) like LLaVA-3D offer strong spatial reasoning but are difficult to deploy due to high computational costs.

Dolphin-CN-Dialect: Where Chinese Dialects Matter

arXiv:2605.08961v1 Announce Type: new Abstract: We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolph

applicationsarxiv-cs-cl

Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning

arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap

EchoFake: A Replay-Aware Dataset for Practical Speech Deepfake Detection

arXiv:2510.19414v2 Announce Type: replace-cross Abstract: The growing prevalence of speech deepfakes has raised serious concerns, particularly in real-world scenarios such as telephone fraud and ident

Efficient Prompt Learning for Traffic Forecasting

arXiv:2605.08273v1 Announce Type: cross Abstract: Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administrat

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts

arXiv:2509.21892v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models typically fix the number of activated experts k at both training and inference. However, real-world deployment

Elite Polarization in European Parliamentary Speeches: a Novel Measurement Approach Using Large Language Models

arXiv:2507.06658v2 Announce Type: replace-cross Abstract: Theories of democratic stability, populism, and party-system crisis often point to a form of polarization that comparative research rarely mea

enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways

arXiv:2604.16838v2 Announce Type: replace-cross Abstract: We present enclawed, a hard-fork hardening framework built on the OpenClaw AI assistant gateway. enclawed targets deployments that need attest

Equivariant Volumetric Grasping

arXiv:2507.18847v3 Announce Type: replace-cross Abstract: We propose a new volumetric grasp model that is equivariant to rotations around the vertical axis, leading to a significant improvement in sam

ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning

arXiv:2605.09982v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) enable large language models (LLMs) to process high-resolution images, significantly improving real

Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework

arXiv:2605.09181v1 Announce Type: new Abstract: Retinal image-based eye tracking is widely used in ophthalmic imaging and vision science, and is a promising path to deliver higher gaze accuracy than t

EventTSF: Event-Aware Non-Stationary Time Series Forecasting

arXiv:2508.13434v2 Announce Type: replace-cross Abstract: Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined wi

Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis

arXiv:2507.11185v2 Announce Type: replace-cross Abstract: Heart disease continues to pose a critical worldwide health issue, more specifically in areas with insufficient access to healthcare infrastru

Extrusion Segmentation Strategy to improve CAD Reconstruction from Point Cloud

arXiv:2605.08971v1 Announce Type: cross Abstract: Computer-Aided Design is ubiquitous in todays world, as almost every manufactured object begins as a digital model across industries. At the same time

Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices

arXiv:2509.21000v2 Announce Type: replace Abstract: Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting

arXiv:2602.07126v2 Announce Type: replace Abstract: Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table s

First Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits

arXiv:2605.09277v1 Announce Type: new Abstract: We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy c

For a driver born without arms, FSD Supervised is life-changing accessibility “I was born without arms and have driven with my feet my entir…

For a driver born without arms, FSD Supervised is life-changing accessibility “I was born without arms and have driven with my feet my entire life. I’m a fully licensed driver, and traditionally I dro

applicationselon-musk--x

Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring

arXiv:2605.08140v1 Announce Type: cross Abstract: The Karlsruhe Tritium Neutrino Experiment (KATRIN) aims to measure the absolute neutrino mass with unprecedented sensitivity, requiring precise monito

Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes

arXiv:2605.10474v1 Announce Type: cross Abstract: Analog neural networks are gaining attention due to their efficiency in terms of power consumption and processing speed. However, since analog neural

FrequencyCT: Frequency domain pseudo-label generation for self-supervised low-dose CT denoising

arXiv:2605.10583v1 Announce Type: new Abstract: Despite extensive research on computed tomography (CT) denoising, few studies exploit projection-domain data characteristics to mitigate noise correlati

From a pure 'do good for the world' mission perspective, having the acting like a solid personalized tutor is one of the better uses of AI. …

applicationsethan-mollick--x

From a pure 'do good for the world' mission perspective, having the acting like a solid personalized tutor is one of the better uses of AI. If OpenAI cares about the mission of making the world a bett

From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World

arXiv:2605.10834v1 Announce Type: new Abstract: AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will perfor

From Historical Tabular Image to Knowledge Graphs: A Provenance-Aware Modular Pipeline

arXiv:2605.08222v1 Announce Type: cross Abstract: Handwritten archival tables contain rich historical information, yet transforming them into structured representations, such as Knowledge Graphs, requ

FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time

arXiv:2405.09570v2 Announce Type: replace-cross Abstract: Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmur

gpt-realtime-2 is a great voice model (with a typically bad OpenAI name). Voice models are natively processing speech, not transcribing it, …

applicationsethan-mollick--x

gpt-realtime-2 is a great voice model (with a typically bad OpenAI name). Voice models are natively processing speech, not transcribing it, so the intelligence of the model matters. The old voice mode

Had an interesting exchange with roon of OpenAI last night over whether super intelligent AI would actually be able to navigate organization…

applicationsethan-mollick--x

Had an interesting exchange with roon of OpenAI last night over whether super intelligent AI would actually be able to navigate organizational challenges. @tszzl I think it is a reasonable argument to

HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models

arXiv:2605.10942v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics. Current WAMs generally follow two parad

applicationsarxiv-cs-ro

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting

arXiv:2509.23668v3 Announce Type: replace Abstract: Time series forecasting occurs in a range of financial applications providing essential decision-making support to investors, regulatory institution

How Amazon Finance streamlines regulatory inquiries by using generative AI on AWS

In this post, we demonstrate how Amazon FinTech teams are using Amazon Bedrock and other AWS services to build a scalable AI application to transform how regulatory inquiries are handled. Each team us

applicationsaws-ml-blog

How finance teams use Codex

This OpenAI Academy article explores how finance teams leverage Codex, OpenAI's code generation model, to automate financial analysis, reporting, and data processing tasks. It likely covers practical

applicationsopenai

How Sapu Indexed 28 Million PubMed Abstracts to Accelerate Cancer Research with Qdrant

Sapu is an early-stage biopharmaceutical company developing treatments for hard-to-treat cancers. From its San Diego facility, the team is pioneering a nanomedicine pipeline that takes existing FDA-ap

applicationsqdrant

HPE expands hybrid cloud stack with unified private cloud and AI-ready storage updates

Hewlett Packard Enterprise Co. today is expanding its hybrid cloud and data platform portfolio with new private cloud, storage and cyber resilience offerings designed to simplify infrastructure modern

applicationssiliconangle