Search: “applications”

LLM Inference in a Flash!

model-releasesarxiv-cs-lg

arXiv:2609.16161v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown impressive capabilities across a range of natural language processing tasks, and LLM inference has emerged as a

16 Sept 2026

Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion

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DGX agent

arXiv:2609.17427v1 Announce Type: cross Abstract: Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear fr

researcharxiv-cs-ai

16 Sept 2026

Write up on building and improving reliability of agentic harnesses + a big dataset from my work.

model-releasesr-localllama

I've been working on something for this community (and other self-hosted communities) for almost 5 months now. Personally, I would grade it as probably the closest open source equivalent of Manus and

16 Sept 2026

AI models need more data about biology, and OpenAI is paying to create it

Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies. By bidding at their bankruptcy p

safetymit-tech-review

Closed-form Bayesian homography estimation from noisy point correspondences

arXiv:2609.15227v1 Announce Type: new Abstract: While homographies are fundamental to many computer vision tasks, the majority of conventional estimation techniques provide only point estimates withou

safetyarxiv-cs-cv

Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models

arXiv:2609.14821v1 Announce Type: new Abstract: Earth system modeling is shifting from task-specific predictors toward foundation models with general spatiotemporal representation capabilities. Althou

Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial

arXiv:2609.13295v1 Announce Type: cross Abstract: Diffusion learning leverages the statistical mechanism of diffusion processes for learning, reasoning, and inferring complex distributions from data.

tutorialsarxiv-cs-lg

JSolver: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections

arXiv:2505.08123v2 Announce Type: replace-cross Abstract: Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of cl

safetyarxiv-cs-ai

Map Users and Mapmakers: The Scope of Cognitive Attribution from Acquired Representations

arXiv:2609.13879v1 Announce Type: new Abstract: An acquired representation can enlarge a system's cognitive repertoire without transferring the capacities exercised in producing that representation. T

researcharxiv-cs-ai

Agents

MedSAM3: Delving into Segment Anything with Medical Concepts

arXiv:2511.19046v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consumin

agentsarxiv-cs-ai

ModiGen: A Large Language Model-Based Workflow for Multi-Task Modelica Code Generation

model-releasesarxiv-cs-ai

arXiv:2503.18460v2 Announce Type: replace-cross Abstract: Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substanti

Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models

arXiv:2609.13514v1 Announce Type: new Abstract: Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when groun

Scaling Telco Autonomy: Leveraging GNNs with Distributed GraphFlow

model-releasesgoogle-cloud-ai

The telecommunications industry is currently undergoing a paradigm shift, moving from traditional manual human-driven operations to fully Autonomous Network Operations. Modern networks have grown incr

Hardware

Well.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals …

Well.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals instead of pacing the frontier… Simpler times! But Aaron’s a

hardwaresonya-huang--x

What resources are needed for optimal learning of bosonic Gaussian states?

arXiv:2603.18136v2 Announce Type: replace-cross Abstract: Continuous-variable systems enable key quantum technologies in computation, communication, and sensing. Bosonic Gaussian states emerge natural

tutorialsarxiv-cs-lg

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases

arXiv:2504.07133v2 Announce Type: replace-cross Abstract: We revisit the problem of estimating k linear regressors with self-selection bias in d dimensions with the maximum selection criterion, as int

14 Sept 2026

Google is a leader in The Forrester Wave™: Public Cloud Platforms, Q3 2026

model-releasesgoogle-cloud-ai

We are excited to share that Google Cloud was named a Leader and received the highest score in the ‘current offering’ category in the Forrester Wave™: Public Cloud Platforms, Q3 2026 report, which exa

14 Sept 2026

Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning

arXiv:2609.12018v1 Announce Type: new Abstract: Railway engineers need simulation models that predict vehicle responses across operating scenarios that cannot be tested exhaustively. Agreement with re

14 Sept 2026

Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature

arXiv:2609.10652v1 Announce Type: cross Abstract: Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. H

researcharxiv-cs-cl

Diversity of EML-type operators

arXiv:2609.11210v1 Announce Type: cross Abstract: The discovery of the EML operator, sufficient to evaluate the standard explicit purely transcendental elementary functions, has led to considerable in

General Quantification of Covariate and Concept Shifts

arXiv:2609.11918v1 Announce Type: new Abstract: Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, id

Generalized Score Matching for Parameter Estimation on Convex Domains

model-releasesarxiv-cs-lg

arXiv:2609.11521v1 Announce Type: new Abstract: Maximum likelihood (ML) estimation is a principled and statistically efficient approach for learning probabilistic models. However, for unnormalized mod

LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics

arXiv:2609.11639v1 Announce Type: new Abstract: The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances

Rethinking Handwritten Character Recognition

arXiv:2609.10572v1 Announce Type: new Abstract: Non-Latin handwritten character recognition (HCR) remains understudied. Dominant methods consider it as generic image classification, which uses model s

tutorialsarxiv-cs-cv

A statistical approach to bias in zero-shot learning: the lens of handwriting recognition

arXiv:2609.10084v1 Announce Type: cross Abstract: Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were no

safetyarxiv-cs-cv

Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending

model-releasesarxiv-cs-lg

arXiv:2609.09945v1 Announce Type: new Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where appli

Applying foundation model embeddings towards urban livability evaluation

arXiv:2609.09429v1 Announce Type: new Abstract: While accurate measurement of socioeconomic indicators remains challenging in data-scarce regions, which limits policy interventions and resource alloca

Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning

arXiv:2603.15203v1 Announce Type: cross Abstract: Nuclear mass prediction is one of the core issues in nuclear physics research, yet it faces the challenge of small-sample datasets with high complexit

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

arXiv:2411.10406v4 Announce Type: replace-cross Abstract: In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, sma

tutorialsarxiv-cs-ai

Multi-Functional Embedding Models for Funder Name Disambiguation in Scientific Publication Records

model-releasesarxiv-cs-cl

arXiv:2609.09984v1 Announce Type: new Abstract: Understanding the historical allocation and distribution of research funding advances our knowledge of how scientific research is supported across field

Agents

Time-Varying Data as Sheaves: an Invitation to Narratives

arXiv:2609.09056v1 Announce Type: new Abstract: Modern science and engineering increasingly rely on time-varying data, yet the mathematical tools used to model temporal phenomena are often developed w

agentsarxiv-cs-ai

XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction

arXiv:2609.09388v1 Announce Type: new Abstract: Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reprod

A Systematic Analysis of Automatic Differentiation versus Discretization-based Constraints for Physics-Informed PDE Solvers

arXiv:2609.07437v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) represent a growing frontier in using artificial intelligence to solve partial differential equations (PDEs).

From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences

arXiv:2506.12045v2 Announce Type: replace Abstract: Accurate reconstruction of latent environmental fields from sparse, indirect observations is a fundamental challenge across scientific domains, from

Mind the Gap: Navigating Inference with Optimal Transport Maps

arXiv:2507.08867v3 Announce Type: replace-cross Abstract: Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics

Unraveling the Real Working Mechanism and Inherent Flaws of GAE: A Method for Interpreting Transformer Processes from an Economic Perspective

arXiv:2609.07213v1 Announce Type: cross Abstract: We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on

Weakly-supervised Kidney Tumor Classification from CT Scans with Multi-Instance Learning and Anatomical Filtering

arXiv:2609.07178v1 Announce Type: new Abstract: Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations, which require significant radiologist ef

Local Ai

An open-source context layer for building AI on top of company data

We’ve been building PipesHub for a while now, and I’d love to get more teams to try it and tell us where it breaks. The problem we kept running into was pretty simple: Building an AI app over company

local-air-localllama

8 Sept 2026

E-RGB-D: Real-Time Event-Based Perception with Structured Light

arXiv:2512.18429v2 Announce Type: replace Abstract: Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed and eff

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

arXiv:2609.04501v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, wher

LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs

model-releasesarxiv-cs-cl

arXiv:2609.04511v1 Announce Type: new Abstract: Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities within free text-an area where traditional

My Qwen3.8-27B task-aware quant reaches 99% of BF16 reasoning performance at 15% of the size.

model-releasesr-localllama

TL;DR My TAK quant of Qwen 3.8 27b scored 82.81% on reasoning, comparted with 77.34% for the byte matched Unsloth UD IQ2_S and 83.59% for BF16. Edit: Some of you have tried coding with this reasoning-

Local Ai

Uncensored Open-weight Models: Redistribution as the Persistence Layer

arXiv:2609.05241v1 Announce Type: new Abstract: A rapidly expanding ecosystem of actors is removing built-in safety guardrails from open-weight AI models. We profile this ecosystem by identifying key

local-aiarxiv-cs-ai

WorldSculpt: Generating Compositional Worlds from Grounded Videos

model-releasesarxiv-cs-cv

arXiv:2609.05416v1 Announce Type: new Abstract: We study the problem of generating a compositional 3D representation of a cluttered scene containing hundreds of objects. The goal is to represent the s

EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering

model-releasesarxiv-cs-ai

arXiv:2509.25175v3 Announce Type: replace-cross Abstract: Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted mani

Modern Transformers Are Implicit Hybrids: From Functional Differentiation to Principled Hybrid Architecture Design

arXiv:2609.02986v1 Announce Type: new Abstract: Hybrid architectures combining Full Attention (FA) and Linear Attention (LA) are increasingly prominent, yet their allocation remains heuristic. We seek

Occlusion-Robust Multimodal Emotion Recognition in VR via Fusion of Facial Images and EMG

arXiv:2609.03569v1 Announce Type: new Abstract: Head-mounted displays (HMDs) fundamentally limit emotion recognition in virtual reality (VR): by occluding the upper face, they render conventional imag

Local Ai

Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift

arXiv:2504.02694v3 Announce Type: replace-cross Abstract: We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data.

local-aiarxiv-cs-lg