Search: “applications”

Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials

arXiv:2606.00315v1 Announce Type: new Abstract: Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning o

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Applications

Custom model training is bringing enterprise AI from experimentation to production

DGX agent

As artificial intelligence moves from proof of concept into enterprise production, custom model training on governed data is emerging as the critical unlock for organizations that need domain-specific

applicationssiliconangle

2 Jun 2026

Degeneration of Sliding-Window Factor Graph Optimization into Iterated Extended Kalman Filtering

arXiv:2511.00306v2 Announce Type: replace Abstract: Sliding window factor graph optimization (SW-FGO) is widely recognized for its robustness, yet its theoretical relationship with the extended Kalman

applicationsarxiv-cs-ro

Research

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs

arXiv:2509.23544v2 Announce Type: replace-cross Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric posit

researcharxiv-cs-ai

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

arXiv:2606.00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced m

Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization

arXiv:2606.00116v1 Announce Type: cross Abstract: This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resou

Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models

arXiv:2602.23197v2 Announce Type: replace Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrati

Safety

Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts

arXiv:2606.00967v1 Announce Type: new Abstract: Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for

safetyarxiv-cs-cv

For organizations already using Azure and Fabric, this means a much faster path from governed data to internal software. • Build apps secure…

For organizations already using Azure and Fabric, this means a much faster path from governed data to internal software. • Build apps securely on top of governed Fabric data • Use existing authenticat

applicationsreplit--x

From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation

arXiv:2606.02167v1 Announce Type: new Abstract: Engineers designing production systems need to verify that a given layout supports all required production sequences. Automated planning techniques can

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

From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets

arXiv:2606.00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models. These 'Rashomon sets' pose a range of challenges and opportunities for uncer

FW-NKF: Frequency-Weighted Neural Kalman Filters

arXiv:2606.02251v1 Announce Type: cross Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch

GIFT: Geometry-Induced Functional Transfer for Category-level Object Manipulation

arXiv:2503.15371v2 Announce Type: replace-cross Abstract: Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities. We propose a new ski

Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study

arXiv:2606.00062v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become foundational for grounding large language models in domain-specific corpora, yet conventional vector-bas

GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping

arXiv:2606.00998v1 Announce Type: new Abstract: We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to no

applicationsarxiv-cs-ro

Here's the paper, which confirms that big changes are happening very quickly. Some really interesting approaches to teasing out what is happ…

applicationsethan-mollick--x

Here's the paper, which confirms that big changes are happening very quickly. Some really interesting approaches to teasing out what is happening in the software development cycle as well: https://pap

HPE posts huge earnings beat thanks to AI servers, sending its stock skyward

Hewlett Packard Enterprise Co.’s artificial intelligence servers sold like crazy during its fiscal second quarter, powering the company to an impressive earnings and revenue beat, and the company’s st

applicationssiliconangle

Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics

arXiv:2606.02145v1 Announce Type: new Abstract: Accurate prediction of polymerization dynamics is essential for process design, control, and optimization. Yet, purely mechanistic models require labor-

I have seen senior managers outpace ICs, and I have seen ICs run circles around the org chart. Titles tell you almost nothing about who is a…

applicationsallie-k--miller--x

I have seen senior managers outpace ICs, and I have seen ICs run circles around the org chart. Titles tell you almost nothing about who is actually going to operate well in the next few years (and mig

ImagineUAV: Aerial Vision-Language Navigation via World-Action Modeling and Kinodynamic Planning

arXiv:2606.01205v1 Announce Type: new Abstract: Vision-language navigation (VLN) for UAVs demands grounding free-form instructions into 6-DoF flight under partial observability. While Vision-Language-

applicationsarxiv-cs-ro

Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways

arXiv:2606.00041v1 Announce Type: cross Abstract: This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transf

Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective

arXiv:2603.28825v2 Announce Type: replace-cross Abstract: Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-

InfoMerge: Information-aware Token Compression for Efficient Video Large Language Models

arXiv:2606.02161v1 Announce Type: cross Abstract: Video Large Language Models (Video-LLMs) achieve strong performance in video understanding, but their excessive visual tokens bring substantial comput

Intel introduces Xeon 6+ server processors, previews upcoming graphics cards

Intel Corp. today debuted a line of central processing units based on its latest manufacturing process and shared new details about its next-generation graphics cards. Both product families are geared

applicationssiliconangle

Interpreting FCDNNs via RG on Exponential Family

arXiv:2606.00157v1 Announce Type: cross Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization gr

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it…

applicationsethan-mollick--x

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it really hard to try its models upon release (a general issue

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

KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering

arXiv:2606.00328v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a ques

Knowledge-Informed Kernel State Reconstruction from Heterogeneous Partial Observations

arXiv:2601.22328v2 Announce Type: replace Abstract: Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, n

Research

Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks

arXiv:2606.01432v1 Announce Type: new Abstract: Accurate modeling of leaf spectral reflectance from physiological and biochemical traits is essential for advancing remote sensing applications in plant

researcharxiv-cs-lg

Linear Strategic Classification with Endogenous Improvements

arXiv:2606.01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost. Classical models

Microsoft and Mayo Clinic partner for an AI model trained on Mayo's medical data, with plans to build an AI healthcare assistant and AI tools for clinicians (Clare Duffy/CNN)

Clare Duffy / CNN: Microsoft and Mayo Clinic partner for an AI model trained on Mayo's medical data, with plans to build an AI healthcare assistant and AI tools for clinicians — People have been seeki

applicationstechmeme

MiCU: End-to-End Smart Home Command Understanding with Large Language Model

arXiv:2606.01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perf

MindZero: Learning Online Mental Reasoning With Zero Annotations

arXiv:2606.00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent a

MineDraft: A Framework for Batch Parallel Speculative Decoding

arXiv:2603.18016v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequen

Tutorials

MINES: Explainable Anomaly Detection through Web API Invariant Inference

arXiv:2512.06906v2 Announce Type: replace-cross Abstract: Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing

tutorialsarxiv-cs-lg

Move from test to production by running high-performance inference directly on Foundry. At #MSBuild, we demoed an end-to-end workflow showin…

applicationsfireworks-ai--x

Move from test to production by running high-performance inference directly on Foundry. At #MSBuild, we demoed an end-to-end workflow showing how unified infrastructure improves latency, reduces cost,

NAPPure: Adversarial Purification for Robust Image Classification under Non-Additive Perturbations

arXiv:2510.14025v2 Announce Type: replace Abstract: Adversarial purification has achieved great success in combating adversarial image perturbations, which are usually assumed to be additive. However,

applicationsarxiv-cs-cv

Near-Optimal Pure Machine Unlearning for Smooth Strongly Convex Losses

arXiv:2606.01527v1 Announce Type: new Abstract: Machine unlearning is motivated by legal and user-facing requirements to remove the influence of individuals' data from trained models, such as the righ

Network Learning with Semi-relaxed Gromov-Wasserstein

arXiv:2606.02223v1 Announce Type: new Abstract: Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning. It requires the identification o

Neural Acquisition & Representation of Subsurface Scattering

arXiv:2606.02292v1 Announce Type: new Abstract: We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel fo

applicationsarxiv-cs-cv

Tutorials

Object detection with Amazon Nova 2 Lite

In this post, we'll walk through implementing object detection with Amazon Nova 2 Lite. You'll learn how to deploy an object detection application using Amazon Bedrock, AWS Lambda, and Amazon API Gate

tutorialsaws-ml-blog

Model Releases

On Wednesdays, We Ask Questions: Optimizing 'Active Listening' in Automated Legal Triage and Referral

model-releasesarxiv-cs-ai

arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. I

PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation

arXiv:2606.00515v1 Announce Type: cross Abstract: Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language

Tutorials

PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery

arXiv:2606.01265v1 Announce Type: cross Abstract: This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical

tutorialsarxiv-cs-ai

Parametric Social Identity Injection and Diversification in Public Opinion Simulation

arXiv:2603.16142v2 Announce Type: replace Abstract: Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costl

Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel

arXiv:2606.02156v1 Announce Type: cross Abstract: Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery