Search: “applications”

Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams

arXiv:2607.27126v1 Announce Type: new Abstract: Persistence Diagram (PD) is known to capture point cloud topology effectively, but its computation has high time complexity. Expected Persistence Diagra

30 Jul 2026

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Applications

When Do Learned Diffusion Proposals Help Constraint Solving? A Controlled Study on Continuous Algebraic Systems

DGX agent

arXiv:2607.27169v1 Announce Type: new Abstract: Solving a continuous algebraic constraint system requires two decisions: which values satisfy the constraints, and which structural augmentation renders

applicationsarxiv-cs-lg

30 Jul 2026

A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields

arXiv:2607.25885v1 Announce Type: cross Abstract: In this paper, we present an automated data-driven workflow using Machine Learning (ML) for gas lift optimization in unconventional fields. This workf

A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks

arXiv:2607.25875v1 Announce Type: cross Abstract: Traffic forecasting is important for efficient traffic management and route planning in smart cities. Existing traffic forecasting studies typically a

AngelSpec: Towards Real-World High Performance Inference with Speculative Decoding

arXiv:2607.25852v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference without changing the target distribution, but no single drafting structure performs best

applicationsarxiv-cs-cl

Belief-Aware Influence and Trust (BAIT): Shaping Human Belief During Repeated Human-Robot Interaction

arXiv:2607.25327v1 Announce Type: new Abstract: Repeated human-robot interaction (HRI) requires proactively accounting for humans who continually adapt to evolving beliefs about the robot. Prior frame

applicationsarxiv-cs-ro

Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields?

arXiv:2607.25929v1 Announce Type: cross Abstract: Deep generative models (DGMs) are widely used for complex high-dimensional data and increasingly applied to spatial and spatio-temporal modeling. Thei

Cerebras and AMD partner to build the world’s fastest disaggregated AI inference solution

Disaggregated AI inference is proving to be more than a complementary answer to the prefill and decode bottleneck slowing enterprise AI at scale, and Cerebras and AMD just announced a partnership to b

applicationssiliconangle

Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy

arXiv:2607.25070v1 Announce Type: cross Abstract: Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedd

CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer

arXiv:2607.26023v1 Announce Type: new Abstract: Graph foundation models (GFMs) have emerged as a promising paradigm for transferring knowledge across graph domains and tasks. Real-world graphs associa

Computational Extraction of Legal Causes via al-Sabr wa al-Taqsim: A Set-Theoretic Formalization for Closed Fiqh Chapters

arXiv:2607.25605v1 Announce Type: new Abstract: This paper presents a set-theoretic formalization of the classical usuli method of al-Sabr wa al-Taqsim (Examination and Division) for extracting legal

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations

arXiv:2607.25397v1 Announce Type: new Abstract: Successfully automating dexterous, long-horizon robotic manipulation requires frameworks capable of both high-level reasoning and fine-grained execution

applicationsarxiv-cs-ro

Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow

arXiv:2601.18260v3 Announce Type: replace Abstract: In clinical radiology, accurate patient table positioning is essential to align specific internal organs of interest with the scanner imaging isocen

Detecting CSAM Text-to-Image LoRAs From Weights

arXiv:2607.25750v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) fine-tuning has made it cheap and easy to customize open-weight image generation models for specific tasks, including the pro

DSevolve: Enabling Real-Time Adaptive Scheduling on Dynamic Flexible Job Shop with LLM-Evolved Heuristic Portfolios

arXiv:2603.27628v2 Announce Type: replace Abstract: In dynamic flexible job shops, order arrivals, machine breakdowns, and processing-time deviations continually reshape the scheduling state and the p

Dynamic Multi-Criteria Bottleneck Severity Index (DMBSI) for Semiconductor Wafer Manufacturing: A Genetically Optimised Framework for Reentrant Production Systems

arXiv:2607.24819v1 Announce Type: cross Abstract: Wafer fabrication exhibits unique characteristics, including reentrant process flows, variable bottlenecks, and highly variable process conditions. In

Eliminating Propagation Delay: Attention-Based Spatial-Temporal Fusion Graph Convolution Network for Traffic Flow Prediction

arXiv:2607.24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility. Many graph convolution-based models have been pr

Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models

arXiv:2607.26000v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to en

Enabling Fully Integer-Only Inference for Lightweight Detection Transformers

arXiv:2607.24981v1 Announce Type: new Abstract: Vision Transformer detectors now approach the accuracy of CNNs but remain difficult to deploy on NPUs and microcontrollers because key components, inclu

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

arXiv:2607.25348v1 Announce Type: cross Abstract: Chronic Kidney Disease (CKD), characterized by the gradual loss of kidney function, remains a significant public health challenge. Early detection is

Extreme Event Aware (eta-) Learning

arXiv:2510.19161v2 Announce Type: replace-cross Abstract: Quantifying and predicting rare and extreme events is challenging because such events are infrequent, severe, and expensive to simulate. Exist

Model Releases

Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion

model-releasesarxiv-cs-cv

arXiv:2607.25820v1 Announce Type: new Abstract: Food image segmentation plays a vital role in health-related applications such as nutrition tracking and personalized health monitoring. However, existi

Graphs move from niche database to enterprise knowledge layer for AI systems

As generative AI matures beyond its early experimentation phase, enterprises are converging on a shared architecture for grounding large language models in trustworthy data: the enterprise knowledge l

applicationssiliconangle

How a scalable intelligence layer turns enterprise data into production A

Enterprises are discovering that a scalable intelligence layer built on knowledge graphs delivers the institutional context that turns generic AI models into reliable production systems. As companies

applicationssiliconangle

How Often Should a Recommender Call an LLM? Value-Weighted Routing, Monitoring, and Seasonal Robustness

arXiv:2607.25068v1 Announce Type: new Abstract: Routing decisions between a cheap heuristic and an expensive large language model (LLM) are typically framed as a difficulty problem: send the hard case

Human Preference aligned Tabular Similarity

arXiv:2607.24880v1 Announce Type: cross Abstract: Task-agnostic tabular embeddings are increasingly used for similarity search in real-world business systems such as Product Lifecycle Management (PLM)

Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data

arXiv:2607.25710v1 Announce Type: new Abstract: Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any even

In an internal meeting, OpenAI finance chief Sarah Friar told employees that the company's annualized recurring revenue in July was higher than in Q2 as a whole (CNBC)

CNBC: In an internal meeting, OpenAI finance chief Sarah Friar told employees that the company's annualized recurring revenue in July was higher than in Q2 as a whole — As OpenAI chases rival Anthropi

applicationstechmeme

Inferring Missing Trajectory Data with Temporal Convolutional Networks

arXiv:2607.25147v1 Announce Type: new Abstract: Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion. We address the task o

Lloyd's K-Means Clustering Algorithm Is Frank-Wolfe in Disguise

arXiv:2607.25190v1 Announce Type: cross Abstract: Lloyd's K-means algorithm, also known as naive K-means, is a widely used ad hoc optimization heuristic, designed to minimize the sum of squared errors

Many-body Tipping Dynamics of ChatGPT-like AIs

arXiv:2607.25279v1 Announce Type: new Abstract: Why do ChatGPT-like AIs, despite major architectural and training differences, unexpectedly tip to undesirable content (e.g. harmful, misleading, repeti

MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence

arXiv:2603.27176v2 Announce Type: replace Abstract: Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical Vision-Language

Memory Layer: Train the In-Model Cache for Recommendation Models

arXiv:2607.25110v1 Announce Type: cross Abstract: Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Becau

Noise-Free One-Step LoRA for Task-Driven Image Restoration with Diffusion Priors

arXiv:2607.25390v1 Announce Type: new Abstract: Degraded images not only reduce visual quality but also impair downstream high-level vision tasks. Task-driven image restoration (TDIR) addresses this i

Operational evaluation of data-driven forest fire forecasting models

arXiv:2603.25469v2 Announce Type: replace Abstract: A growing body of literature has focused on predicting wildfire occurrence using machine learning methods, capitalizing on high-resolution data and

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers

arXiv:2512.17351v2 Announce Type: replace Abstract: Understanding architectural differences in language models is challenging, especially at academic-scale pretraining (e.g., 1.3B parameters, 100B tok

applicationsarxiv-cs-cl

Reading Without a Reader: Large Language Models Collapse Reading and Writing into a Single Entangled Code

arXiv:2607.24797v1 Announce Type: cross Abstract: In the literate human brain, reading and writing are two doubly-dissociable systems: a ventral decoding route (impaired in pure alexia) and a fronto-p

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

arXiv:2607.25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evi

Model Releases

ScoreShield: Differentially Private Release of Similarity Scores

model-releasesarxiv-cs-cv

arXiv:2607.25041v1 Announce Type: cross Abstract: A growing number of applications, such as biometrics and retrieval-augmented generation (RAG), rely on cosine similarity scores computed between vecto

SPARC Segmentation to Prediction via Affine Regression and Counterfactuals

arXiv:2607.25413v1 Announce Type: new Abstract: Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procuremen

SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics

arXiv:2607.25790v1 Announce Type: cross Abstract: This paper proposes a novel physics-guided spectral deep operator network, termed SpectONet, for solving Euler-Bernoulli beam (EBB) vibration problems

Model Releases

The borderless Lakehouse: Bring AWS, Databricks and Snowflake data to your AI agents

model-releasesgoogle-cloud-ai

Today’s data lakehouse is no longer mere data repository, but increasingly a system of action, actively executing tasks via always-on, autonomous AI agents. Rather than waiting for static reports, the

Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems

arXiv:2607.24865v1 Announce Type: cross Abstract: Large-scale recommendation systems face 'Memory Wall' bottlenecks due to massive, dense embedding tables. While generative retrieval uses discrete tok

VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation

arXiv:2607.25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization. Ho

We hope these experiments serve as a reminder that evals rarely measure models in isolation—they also measure a bundle of less visible choic…

We hope these experiments serve as a reminder that evals rarely measure models in isolation—they also measure a bundle of less visible choices about API settings, harness design, and prompting. If you

applicationsopenai--x

'We'll have to see how it works': An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research

arXiv:2311.18424v3 Announce Type: replace-cross Abstract: Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients an

When Algorithms Meet Artists: Semantic Compression and Stake-holder Marginalisation in Public AI-Art Discourse (2013-2025)

arXiv:2508.03037v5 Announce Type: replace Abstract: Artists occupy a paradoxical position in generative AI. Their own work trains models that now compete with them, replicate their styles, and reshape

applicationsarxiv-cs-cl

A Case Study on the Acceptance of a Humanoid Robotic Head Employed in Three Public Spaces

arXiv:2607.24113v1 Announce Type: cross Abstract: Previous research has shown that a human-like robot's acceptance heavily depends on the setting in which it operates and its ability to perform releva