Tootfinder

Explainable and Resilient ML-Based Physical-Layer Attack Detectors
Aleksandra Knapi\'nska, Marija Furdek
https://arxiv.org/abs/2509.26530 https://arxiv…

Explainable and Resilient ML-Based Physical-Layer Attack Detectors
Detection of emerging attacks on network infrastructure is a critical aspect of security management. To meet the growing scale and complexity of modern threats, machine learning (ML) techniques offer valuable tools for automating the detection of malicious activities. However, as these techniques become more complex, their internal operations grow increasingly opaque. In this context, we address the need for explainable physical-layer attack detection methods. First, we analyze the inner workin…

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes
Guangwen Li, Xiao Yu
https://arxiv.org/abs/2509.25580 https://arxiv.org/pdf…

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes
This paper introduces a hybrid decoding architecture that serially couples a normalized min-sum (NMS) decoder with reinforced ordered statistics decoding (OSD) to achieve near-maximum likelihood (ML) performance for short linear block codes. The framework incorporates several key innovations: a decoding information aggregation model that employs a convolutional neural network to refine bit reliability estimates for OSD using the soft-output trajectory of the NMS decoder; an adaptive decoding pa…

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination
Karl C. Linne (Kai Li), Sho Uemura, Yue Ji, Andrew Karmen, Allen Zang, Alex Kolar, Ian Chin, Martin Di Federico, Gustavo Cancelo, Orlando Quaranta, Debashri Roy, Tian Zhong
https://arxiv.org/abs/2509.26315

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination
Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons…

Pangram Labs: ~21% of the 75,800 peer reviews submitted for ICLR 2026, a major ML conference, were fully AI-generated, and 50% contained signs of AI use (Miryam Naddaf/Nature)
https://www.nature.com/articles/d41586-025-03506-6

Major AI conference flooded with peer reviews written fully by AI
Controversy has erupted after 21% of manuscript reviews for an international AI conference were found to be generated by artificial intelligence.

Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
Hira Naveed, John Grundy, Chetan Arora, Hourieh Khalajzadeh, Omar Haggag
https://arxiv.org/abs/2509.25195

Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
Given the inherent non-deterministic nature of machine learning (ML) systems, their behavior in production environments can lead to unforeseen and potentially dangerous outcomes. For a timely detection of unwanted behavior and to prevent organizations from financial and reputational damage, monitoring these systems is essential. This paper explores the strategies, challenges, and improvement opportunities for monitoring ML systems from the practitioners perspective. We conducted a global survey…

from my link log —
A brief history of NSA backdoors. (2013)
https://www.ethanheilman.com/x/12/index.html
saved 2025-11-29 https://d…

Мне очень жаль терять мой стрик в Duolingo... Но введение энергии, которая тратится на каждое выполненное задание в уроке, вместо сердец, которые тратятся только когда ты ошибся — это прямо красная линия для меня. За безошибочный урок я получил -5 энергии.
Пришёл момент когда надо закончить эти абьюзивные отношения с зелёной птицей
#duolingo

A Practitioner's Guide to AI ML in Portfolio Investing
Mehmet Caner Qingliang Fan
https://arxiv.org/abs/2509.25456 https://arxiv.org/pdf/2509.25456

A Practitioner's Guide to AI+ML in Portfolio Investing
In this review, we provide practical guidance on some of the main machine learning tools used in portfolio weight formation. This is not an exhaustive list, but a fraction of the ones used and have some statistical analysis behind it. All this research is essentially tied to precision matrix of excess asset returns. Our main point is that the techniques should be used in conjunction with outlined objective functions. In other words, there should be joint analysis of Machine Learning (ML) techni…

Accelerated Discovery of High-\k{appa} Oxides with Physics-Based Factorized Machine Learning
Atsushi Takigawa, Shin Kiyohara, Yu Kumagai
https://arxiv.org/abs/2509.26022 https:/…

Accelerated Discovery of High-\k{appa} Oxides with Physics-Based Factorized Machine Learning
Considerable effort continues to be devoted to the exploration of next-generation high-\k{appa} materials that combine a high dielectric constant with a wide band gap. However, machine learning (ML)-based virtual screening has remained challenging, primarily due to the low accuracy in predicting the ionic contribution to the dielectric tensor, which dominates the dielectric performance of high-\k{appa} materials. We here propose a joint ML model that predicts Born effective charges using an equ…

[2025-10-01 Wed (UTC), 15 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

RE: #Merz during visit in

TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
Arjan Blankestijn, Uraz Odyurt, Amirreza Yousefzadeh
https://arxiv.org/abs/2509.26335 https://

TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
The Transformer Machine Learning (ML) architecture has been gaining considerable momentum in recent years. In particular, computational High-Energy Physics tasks such as jet tagging and particle track reconstruction (tracking), have either achieved proper solutions, or reached considerable milestones using Transformers. On the other hand, the use of specialised hardware accelerators, especially FPGAs, is an effective method to achieve online, or pseudo-online latencies. The development and inte…

BABY 1L: First Tritium Breeding Campaign Results
R\'emi Delaporte-Mathurin, Nikola Goles, Collin Dunn, Emily Edwards, Sara Ferry, Ross MacDonald, Ethan Peterson, Davide Pettinari, Stefano Segantin, Weiyue Zhou, Kevin B. Woller
https://arxiv.org/abs/2509.26174

BABY 1L: First Tritium Breeding Campaign Results
Achieving tritium self-sufficiency is a critical challenge for future fusion power plants. The BABY 1L experiment, part of the LIBRA project at MIT, aims to benchmark tritium breeding and release in molten salt breeder systems under deuterium-tritium (DT) neutron irradiation. Building on the initial \SI{100}{mL} campaign, BABY 1L introduces a tenfold increase in breeder volume, improved thermal and gas handling systems, and enhanced neutron diagnostics, including a proton recoil telescope. We r…

I so hope this blows up in his face spectacularly.
Note that I do not believe that any LLM can become as skilled as the least skilled #InfoSec professional using conventional tools, so I’m quite confident this will not reach any useful goal.
I’m saying he needs to learn a lesson, and it would be great if it were useful for others as well. @…

SPAM's Carbon Footprint @Home (2017) - The carbon cost of fending off SPAM (and other networked malice) at home... - https://m.earth.org.uk/SPAM-and-CO2.html

SPAMs Carbon Footprint @Home (2017)
The carbon cost of fending off SPAM (and other networked malice) at home...

I finally got Link Previews in Firefox today. I disabled the ‘AI’ feature and then disabled link previews altogether as I played with it.
But the setting disappeared completely, so I couldn’t turn it on again.
If this happens to you:
about:config − set browser.ml.linkPreview.enabled to true

I'm installing and tweaking #Immich, a really neat "Google Photos" like app that runs locally. It uses ML to do face detection and other neat features. I've been tuning my server and needed a better way to measure performance, so I vibe wrote a helper script. If you run Immich, this might be useful to you:

GitHub - rubix1138/immich_eta: Script that queries the Immich API and calculates and ETA
Script that queries the Immich API and calculates and ETA - rubix1138/immich_eta

ML-KEM Mythbusting
"There have been some recent concerns about ML-KEM, NIST’s standard for encryption with Post-Quantum Cryptography, related standards of the IETF, and lots of conspiracy theories about malicious actors subverting the standardization process. "
#pqc #pqcrypto

Effect of Mixed Ratios of Mangosteen Peel (Garcinia mangostana L.) and Grass Jelly Leaf (Cyclea barbata L. Miers) Natural Dyes on the Performance of Dye-Sensitized Solar Cells (DSSC)
Eka Nurfani, Haifa Salsabila, Dwiky I. Bakhtiar, Wahyu S. Sipahutar, M Alvien Ghifari, Rishal Asri, Meqorry Yusfi, Tarmizi Taher, Aditya Rianjanu, Robi Kurniawan
https://

Effect of Mixed Ratios of Mangosteen Peel (Garcinia mangostana L.) and Grass Jelly Leaf (Cyclea barbata L. Miers) Natural Dyes on the Performance of Dye-Sensitized Solar Cells (DSSC)
Dye-sensitized solar cells (DSSCs) are promising low-cost and environmentally friendly photovoltaic devices, especially when utilizing natural sensitizers. This study explores the effect of concentration ratios of natural dyes extracted from mangosteen peel (MP) and grass jelly leaves (GJL) on the optical, morphological, and photovoltaic properties of DSSCs. Dye solutions were prepared at a fixed concentration of 0.6 g/mL, with the MP:GJL volume ratio varied at 3:1, 2:1, 1:1, 1:2, and 1:3. As a…

Warum meine Großmutter zurück in die Stadt zog und ich mein Nest nicht auf dem Land bauen will: kurze Wege und mehr Menschen heißt auch leichter sozialer Kontakt.
https://loma.ml/display/373ebf56-c2de9553-e191b82d366f91de
Ich finde die Initiative vom Land SH dennoc…

60 years ago #OTD #comet #IkeyaSeki reached perihelion, -10 mag., was visible in daylight and showed a spectacular tail in the following weeks: https://groups.io/g/comets-ml/message/32770 and https://www.astronomy.com/today-in-the-history-of-astronomy/sept-18-1965-discovery-of-comet-ikeya-seki/ and https://noirlab.edu/public/images/noao-01854/

Übermorgen bei unseren Nachbarn:
#UniGöttingen

Model ML, which aims to use AI agents to automate grunt work done by investment bankers like making pitch decks, raised $75M, after raising $12M earlier in 2025 (Aisha S Gani/Bloomberg)
https://www.bloomberg.com/news/articles/20

Another ML(#MarxismLeninism) blocked me when I posted this: https://social.linux.pizza/@midtsveen/115581081054226799
Love it or hate it, we can disagree on topics …

Marked safe from AI in Firefox 144 on Linux!
(about:config is your friend: search for ".ml.")
https://www.mozilla.org/en-US/firefox/144.0/whatsnew/?oldversion=143.0.4

An online service I’m subscribed to emailed me to say that I would be placed on the “Pro Legacy” plan at the same price of $29 annually in December.
But if upgrade to their $60/year Pro plan, I get access to AI/ML “innovations”. Uh, no, thank you!
#AI #ML

Dear Reader,

Today I'm delighted to announce a Take Control book about a fantastic new Mac email app called #MailMaven. Developed by our friends at SmallCubed (best known for their popular MailSuite plugin for Apple Mail), MailMaven is a powerful and flexible alternative to apps like Apple Mail, Outlook, and Thunderbird.
The Manual is free from :

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Learning to sample fibers for goodness-of-fit testing
Ivan Gvozdanovi\'c, Sonja Petrovi\'c

from my link log —
psychec: ML-style type inference for C.
https://github.com/ltcmelo/psychec
saved 2019-09-09 https://dotat.at/:/GN6G4.html

GitHub - ltcmelo/psychec: A compiler frontend for the C programming language
A compiler frontend for the C programming language - ltcmelo/psychec

Believe it or not, I've never actually submitted an academic paper for peer-reviewed publication before, and I'm a lil nail-bitey.
Is there anyone here who's active in ACL (the Association for Computational Linguistics) who wants to swap papers with me for a deep critique of methods/code/conclusions? I have a fun classic ML model that will entertain you, I swear.

Там вышла 4я часть иски по финалке
Сначала думал, что опять Ютуб древние архивы поднял, а потом увидел время публикации... Я поражен до глубины души
https://youtu.be/X2SDwyldfzU

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
Moinuddin Muhammad Imtiaz Bhuiyan, Kazi Ekramul Hoque, Rakibul Islam, Md. Mahbubur Rahman Tusher, Najmul Hassan, Yoichi Tomioka, Satoshi Nishimura, Jungpil Shin, Abu Saleh Musa Miah
https://arxiv.org/abs/2510.05835

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, incl…

Ethical Considerations Around Machine Learning-Engaged Online Participatory Research - poster from Zooniverse community at #FF2025 https://zenodo.org/records/17779992

The totality of the evidence supports the thesis that displacement of long-established dietary patterns by ultra-processed foods is a key driver of the escalating global burden of multiple diet-related chronic diseases.
Monteiro CA, Louzada ML, Steele-Martinez E, Cannon G, Andrade GC, Baker P, et al. (2025): Ultra-processed foods and human health: the main thesis and the evidence. The Lancet 0. DOI: 10.1016/S0140-6736(25)01565-X

I can't hide that AMD cosplaying as Nvidia was a nasty surprise to me. But I am just a lowly gamer. Mad respect to the Linux hackers for knowing that corporations are not their friends, and - respectfully to AMD - bringing the goods only to Linux 🙂 ->
Modder gets FSR Redstone ML-powered Frame Generation running on RDNA 3 GPUs - VideoCardz.com

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra
Vasuda Trehan, Kevin H. Knuth, M. J. Way
https://arxiv.org/abs/2510.08766

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra
The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model…

The {purrr} package is a powerfull way to replace loops. The {furrr} package takes this approach one step further by parallel execution: #rstats

Apply Mapping Functions in Parallel using Futures
Implementations of the family of map() functions from purrr that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

God, can someone please finally take #Firefox out of the hands of #Mozilla and their damn "AI" bros?
When they started adding all of this nonsense I went into my about:config to explicitly set "browser.ml.enable" to "false", but apparently even doesn't save you, as the new "features" will just not give a damn. 🫠

@… , we hadden het laatst over kaasfondue van schaap, hier heeft mijn websuper ineens Kaasfondue van geitenkaas - Kant en Klaar - Hofweb - de boerderij ken ik, die is, ondanks de suffe naam, goed. Ze doen er wel te weinig wijn in.

To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera, Roberto Pellungrini, Bruno Lepri, Andrea Passerini
https://arxiv.org/abs/2510.08314

To Ask or Not to Ask: Learning to Require Human Feedback
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machine Learning (ML) model to pass difficult cases to a human expert. However, LtD treats humans and ML models as mutually exclusive decision-makers, restricting the expert contribution to mere predictions. To address this limitation, we propose Learning to Ask (LtA), a new framework that handles both when and how to inco…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Replicable Clustering
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
Md Habibur Rahman, Md Sharif Hossen, Nathan H. Stephenson, Vijay K. Shah, Aloizio Da Silva
https://arxiv.org/abs/2510.09706

A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN
The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-in…

Computations and ML for surjective rational maps
Ilya Karzhemanov
https://arxiv.org/abs/2510.08093 https://arxiv.org/pdf/2510.08093

Computations and ML for surjective rational maps
The present note studies \emph{surjective rational endomorphisms} $f: \mathbb{P}^2 \dashrightarrow \mathbb{P}^2$ with \emph{cubic} terms and the indeterminacy locus $I_f \ne \emptyset$. We develop an experimental approach, based on some Python programming and Machine Learning, towards the classification of such maps; a couple of new explicit $f$ is constructed in this way. We also prove (via pure projective geometry) that a general non-regular cubic endomorphism $f$ of $\mathbb{P}^2$ is surject…

Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Benedikt B\"ock, Amar Kasibovic, Wolfgang Utschick
https://arxiv.org/abs/2510.12279

Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this work, we argue that a link-level perspective incorporates limiting assumptions, causing unwanted distributional shifts or necessitating impractical online training. An additional drawback is that this perspective leads to (near-)Gaussian channel characteristics. Thus, ML-based models, trained on link-level channel data,…

Software Defect Prediction using Autoencoder Transformer Model
Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli
https://arxiv.org/abs/2510.10840 https://

Software Defect Prediction using Autoencoder Transformer Model
An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequent…

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars
Rodrigo C\'ordova Rosado, Andy D. Goulding, Jenny E. Greene, Nickolas Kokron, Andrina Nicola, Michael A. Strauss, Ryan C. Hickox
https://arxiv.org/abs/2510.11780

Cross-correlation of Luminous Red Galaxies with ML-selected AGN in HSC-SSP III: HOD Parameters for Type I and Type II Quasars
Understanding the dark matter (DM) halo environment in which galaxies that host active galactic nuclei (AGN) reside is a window into the nature of supermassive black hole (SMBH) accretion. We apply halo occupation distribution (HOD) modeling tools to interpret the angular cross-correlation functions between $1.5\times10^6$ luminous red galaxies (LRGs) and our $\sim28,500$ Hyper Suprime-Cam + Wide-field Infrared Survey Explorer-selected (and $L_{6 μm}$-limited) AGN to infer the halo properties …

Generalized Mandelstam-Leibbrandt regularization
Jorge Alfaro
https://arxiv.org/abs/2510.01200 https://arxiv.org/pdf/2510.01200

Generalized Mandelstam-Leibbrandt regularization
Algebraic non-covariant gauges are used often in string theory, Chern-Simons theory, gravitation and gauge theories. Loop integrals, however, have spurious singularities that need to be regularized. The most popular and consistent regularization is the Mandelstam- Leibbrandt(ML) prescription. This paper extends the ML prescription outside the light cone. It shares all the properties of light-cone ML regularization: It preserves naive power counting and gauge invariance. Moreover, using di…

🇺🇦 #NowPlaying on KEXP's #90TEEN
Molina:
🎵 Organs
#Molina
https://corpusmolina.bandcamp.com/track/organs-feat-ml-buch
https://open.spotify.com/track/3XrEOFbHz26fgR8UTmPxmP

das #nvidia

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic
https://arxiv.org/abs/2510.08726

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as attention mechanisms. The paper introduces Neptune, a tensor compiler for advanced operator fusion for sequences of reduction operators. Neptune presents a new approach for advanced operator fusion,…

What’s disgusting is speaking to a Palestinian person in Gaza this way.
Maybe Mastodon server admins should check for basic humanity before allowing people on. Unless that’s not a necessary prerequisite for membership in your community, of course. https://lemmy.ca/comment/20336687

Automated Research Article Classification and Recommendation Using NLP and ML
Shadikur Rahman, Hasibul Karim Shanto, Umme Ayman Koana, Syed Muhammad Danish
https://arxiv.org/abs/2510.05495

Automated Research Article Classification and Recommendation Using NLP and ML
In the digital era, the exponential growth of scientific publications has made it increasingly difficult for researchers to efficiently identify and access relevant work. This paper presents an automated framework for research article classification and recommendation that leverages Natural Language Processing (NLP) techniques and machine learning. Using a large-scale arXiv.org dataset spanning more than three decades, we evaluate multiple feature extraction approaches (TF--IDF, Count Vectorize…

Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia, Sven Klaassen, Nils Skotara, Martin Spindler
https://arxiv.org/abs/2510.09109

Sensitivity Analysis for Causal ML: A Use Case at Booking.com
Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal effect estimates may become biased, undermining the validity of research findings. In these contexts, sensitivity analysis plays a crucial ro…

First the nucleus of #comet SWAN split - https://skyweek.wordpress.com/2025/11/02/allgemeines-live-blog-ab-dem-2-november-2025/#Nov06 ("Der Kern des Kometen C/2025 R2 (SWAN) ist zerbrochen ...") - and now C/2025 K1 (ATLAS) has suffered the same fate: a 2-meter-telescope image from https://www.astronomerstelegram.org/?read=17487 ("Multiple Fragmentation Observed"), also https://groups.io/g/comets-ml/topic/116216567 (several amateur observations) and https://www.astronomerstelegram.org/?read=17488 (Hubble detections of more fragments, sadly no pictures included).

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Learning Shared Representations from Unpaired Data
Amitai Yacobi, Nir Ben-Ari, Ronen Talmon, Uri Shaham

Soumith Chintala, who co-created the PyTorch ML framework at Meta and left the company this month, has joined Mira Murati's Thinking Machines Lab (Pranav Dixit/Business Insider)
https://www.businessinsider.com/meta-soumith-chinta…

A top Meta AI researcher has joined Mira Murati's hot new startup, Thinking Machines Lab
Thinking Machines Lab, led by Mira Murati, hires Meta AI veteran Soumith Chintala as it rapidly expands its team and prepares for major funding.

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Sparse Robust Classification via the Kernel Mean
Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- An Eulerian Perspective on Straight-Line Sampling
Panos Tsimpos, Youssef Marzouk

Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
Deeksha Hareesha Kulal, Chidozie Princewill Arannonu, Afsah Anwar, Nidhi Rastogi, Quamar Niyaz
https://arxiv.org/abs/2510.11915

Robust ML-based Detection of Conventional, LLM-Generated, and Adversarial Phishing Emails Using Advanced Text Preprocessing
Phishing remains a critical cybersecurity threat, especially with the advent of large language models (LLMs) capable of generating highly convincing malicious content. Unlike earlier phishing attempts which are identifiable by grammatical errors, misspellings, incorrect phrasing, and inconsistent formatting, LLM generated emails are grammatically sound, contextually relevant, and linguistically natural. These advancements make phishing emails increasingly difficult to distinguish from legitimat…

On 16WW Data Collections and Graphs - Open for research #dataset - https://m.earth.org.uk/note-on-data.html

On 16WW Data Collections and Graphs
Open for research #dataset

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- What Makes Looped Transformers Perform Better Than Non-Recursive Ones (Provably)
Zixuan Gong, Jiaye Teng, Yong Liu

Lightning talks include IIIF's AI/ML working group presented by the excellent Martin Kalfatovic https://www.conftool.org/fantastic-futures-2025/index.php?page=browseSessions&ismobile=true&form_session=26

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
Vahid Negahdari, Shirin Samadi Bahrami, Seyed Reza Moghadasi, Mohammad Reza Razvan

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Nges Brian Njungle, Eric Jahns, Luigi Mastromauro, Edwin P. Kayang, Milan Stojkov, Michel A. Kinsy
https://arxiv.org/abs/2510.09985

Prismo: A Decision Support System for Privacy-Preserving ML Framework Selection
Machine learning has become a crucial part of our lives, with applications spanning nearly every aspect of our daily activities. However, using personal information in machine learning applications has sparked significant security and privacy concerns about user data. To address these challenges, different privacy-preserving machine learning (PPML) frameworks have been developed to protect sensitive information in machine learning applications. These frameworks generally attempt to balance desi…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Multiparameter regularization and aggregation in the context of polynomial functional regression
Gizewski, Holzleitner, Mayer-Suess, Pereverzyev, Pereverzyev

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Energy-Driven Steering: Reducing False Refusals in Large Language Models
Jiang, Ou, Liu, Pang, Wan, Duan, Dong, Chang, Wang, Wu, Li

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Gaurab Chhetri, Shriyank Somvanshi, Pavan Hebli, Shamyo Brotee, Subasish Das
https://arxiv.org/abs/2510.10436

Post-Quantum Cryptography and Quantum-Safe Security: A Comprehensive Survey
Post-quantum cryptography (PQC) is moving from evaluation to deployment as NIST finalizes standards for ML-KEM, ML-DSA, and SLH-DSA. This survey maps the space from foundations to practice. We first develop a taxonomy across lattice-, code-, hash-, multivariate-, isogeny-, and MPC-in-the-Head families, summarizing security assumptions, cryptanalysis, and standardization status. We then compare performance and communication costs using representative, implementation-grounded measurements, and re…

LED Lighting: Mini Reviews - Real-world testing! - https://m.earth.org.uk/LED-lighting.html

LED Lighting: Mini Reviews
Real-world testing!

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Latency-Aware Contextual Bandit: Application to Cryo-EM Data Collection
Lai Wei, Ambuj Tewari, Michael A. Cianfrocco

Formal Models and Convergence Analysis for Context-Aware Security Verification
Ayush Chaudhary
https://arxiv.org/abs/2510.12440 https://arxiv.org/pdf/2510.…

Formal Models and Convergence Analysis for Context-Aware Security Verification
We present a formal framework for context-aware security verification that establishes provable guarantees for ML-enhanced adaptive systems. We introduce context-completeness - a new security property - and prove: (1) sample complexity bounds showing when adaptive verification succeeds, (2) information-theoretic limits relating context richness to detection capability, (3) convergence guarantees for ML-based payload generators, and (4) compositional soundness bounds. We further provide a formal…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning
Md. Saiful Bari Siddiqui, Md Mohaiminul Islam, Md. Golam Rabiul Alam

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- An Empirical Analysis of the Laplace and Neural Tangent Kernels
Ronaldas Paulius Lencevi\v{c}ius

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A Mixed-Methods Analysis of Repression and Mobilization in Bangladesh's July Revolution Using Mac...
Md. Saiful Bari Siddiqui, Anupam Debashis Roy

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Model-free generalized fiducial inference
Jonathan P Williams
https://…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Mixtures of Gaussian Process Experts with SMC$^2$
Teemu H\"ark\"onen, Sara Wade, Kody Law, Lassi Roininen

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Domain Generalization: A Tale of Two ERMs
Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade, Clayton Scott

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Group Policy Gradient
Junhua Chen, Zixi Zhang, Hantao Zhong, Rika Antonova

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
Zheng, Venkatasubramanian, Li, Braverman, Ke, Hou, Jin, Agrawal

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Gradient Descent with Large Step Sizes: Chaos and Fractal Convergence Region
Shuang Liang, Guido Mont\'ufar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Discrimination in machine learning algorithms
Roberta Pappad\`a, Francesco Pauli

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Orthogonal Procrustes problem preserves correlations in synthetic data
Oussama Ounissi, Nicklas J\"averg\r{a}rd, Adrian Muntean

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Convergence analysis of online algorithms for vector-valued kernel regression
Michael Griebel, Peter Oswald

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Baron, Oreshkin, Ma, Zhang, Torkkola, Mahoney, Wilson, Konstantinova

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Efficient Probabilistic Visualization of Local Divergence of 2D Vector Fields with Independent Ga...
Ouermi, Li, Moreland, Pugmire, Johnson, Athawale

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers
Dingling Yao, Filip Tronarp, Nathanael Bosch

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cogni...
Fahad Mostafa, Kannon Hossain, Hafiz Khan

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- BOOST: Bayesian Optimization with Optimal Kernel and Acquisition Function Selection Technique
Joon-Hyun Park, Mujin Cheon, Jeongsu Wi, Dong-Yeun Koh

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Asymptotic Classification Error for Heavy-Tailed Renewal Processes
Xinhui Rong, Victor Solo

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- ACE: Adapting sampling for Counterfactual Explanations
Margarita A. Guerrero, Cristian R. Rojas

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Anomaly detection by partitioning of multi-variate time series
Pierre Lotte, Andr\'e P\'eninou, Olivier Teste

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Group Averaging for Physics Applications: Accuracy Improvements at Zero Training Cost
Valentino F. Foit, David W. Hogg, Soledad Villar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Differentiable, Bit-shifting, and Scalable Quantization without training neural network from scratch
Zia Badar

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Dataset-Free Weight-Initialization on Restricted Boltzmann Machine
Muneki Yasuda, Ryosuke Maeno, Chako Takahashi

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- A metrological framework for uncertainty evaluation in machine learning classification models
Samuel Bilson, Maurice Cox, Anna Pustogvar, Andrew Thompson

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
Boris Kriuk
https://arxiv.org/abs/2510.02189 https://

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
Arctic warming threatens over 100 billion in permafrost-dependent infrastructure across Northern territories, yet existing risk assessment frameworks lack spatiotemporal validation, uncertainty quantification, and operational decision-support capabilities. We present a hybrid physics-machine learning framework integrating 2.9 million observations from 171,605 locations (2005-2021) combining permafrost fraction data with climate reanalysis. Our stacked ensemble model (Random Forest + Histogram G…