Tootfinder

NFL Wild Card Weekend player statistical leaders, props, picks: Bills' Allen, Packers' Jacobs could thrive

https://www.cbssports.com/nfl/news/nfl-wil

Automatically describe data and models as text using the {report} package. #rstats

An Oxford Internet Institute study of 445 AI benchmarks finds many tests lack clear aims and comparable statistical methods, potentially exaggerating AI claims (Jared Perlo/NBC News)
https://www.nbcnews.com/tech/tech-news/ai-c…

AI’s capabilities may be exaggerated by flawed tests, study says
Researchers behind a new study say that the methods used to evaluate AI systems’ capabilities routinely oversell AI performance and lack scientific rigor.

Week 9 Monday Night Football Statistical Review: Cardinals beat Cowboys behind Jacoby Brissett https://www.pff.com/news/nfl-week-9-monday-night-football-statistical-review-cardinals-cowboys-2025

Updated NFL Power Rankings: New 1-32 poll, plus the most shocking statistics of the 2025 season https://www.espn.com/nfl/story/_/id/47189899/nfl-week-15-power-rankings-poll-32-teams-2025-most-shocking-statistics

AFarePart: Accuracy-aware Fault-resilient Partitioner for DNN Edge Accelerators
Mukta Debnath (University of Calcutta, India), Krishnendu Guha (University College Cork, Ireland), Debasri Saha (University of Calcutta, India), Amlan Chakrabarti (University of Calcutta, India), Susmita Sur-Kolay (Indian Statistical Institute, India)
https://a…

Optical clocks with accuracy validated at the 19th digit
K. J. Arnold, M. D. K. Lee, Zhao Qi, Qichen Qin, Zhang Zhao, N. Jayjong, M. D. Barrett
https://arxiv.org/abs/2512.07346 https://arxiv.org/pdf/2512.07346 https://arxiv.org/html/2512.07346
arXiv:2512.07346v1 Announce Type: new
Abstract: We report a comprehensive evaluation of all known sources of systematic uncertainty for two independent $^{176}$Lu$^ $ single-ion optical references, finding total systematic uncertainty of $1.1\times10^{-19}$ and $1.4\times10^{-19}$ for the two individual systems and $9.6\times10^{-20}$ for the difference. Through direct comparison via correlation spectroscopy, we demonstrate a relative frequency agreement of $-2.4\pm(5.7)_\mathrm{stat}\pm(1.0)_\mathrm{sys}\times10^{-19}$, where `stat' and `sys' indicate the statistical and systematic uncertainty, respectively. The comparison uncertainty is statistically limited after approximately 200 hours of averaging with a measurement instability of $4.8\times10^{-16}(\tau/\mathrm{s})^{-1/2}$.
toXiv_bot_toot

Great seminar: Understanding Statistical Process Control by Donald Wheeler
https://www.youtube.com/watch?v=rP3i97Q6XjM&list=PLUeXRJ5a5UC1cDLxbZPs6Jg_d9m-nvIjO &index=2

Statistical milestones are in reach for Cowboys in Week 18 https://insidethestar.com/statistical-milestones-are-in-reach-for-cowboys-in-week-18

NFL stats and records, Week 14: Browns QB Shedeur Sanders enters rarefied rookie air https://www.nfl.com/news/nfl-stats-and-records-week-14-browns-qb-shedeur-sanders-enters-rarefied-rookie-air

I wrote a blog post about the often stated but never explained assumption that communities in graphs should always be connected.
This “connected cluster axiom” is inconsistent with statistical significance and null models that underlie the most widely employed methods.
https://skewed.de/lab/posts/connected-

LLMs never make mistakes or hallucinate, as this presupposes they actually know what they’re doing—they don’t: they have no concept of what words mean.
They don’t even deal with language, as they generate chains of big numbers based on statistical correlations.
The resulting transformation into human-readable text is always only a statistical approximation of what a real answer could maybe look like.
By sheer chance sometimes LLMs are even correct (usually for trivial things); however above a certain length of answer it is always wrong.

Do you need better performance than what the standard #tidyverse functions have? {collapse} might be worth a look: https://sebkrantz.github.io/collapse/

Revealing stimulus-dependent dynamics through statistical complexity
Edson V. de Paula, Rafael M. Jungmann, Antonio J. Fontenele, Leandro A. A. Aguiar, Pedro V. Carelli, Fernanda S. Matias, Mauro Copelli, Nivaldo A. P. de Vasconcelos
https://arxiv.org/abs/2512.05007

It would be great if there was one simple trick for winning elections. But 'just be more moderate' isn't it.
In fact, you can use the NYT's exact method to 'prove' a 'Progressive Advantage' of 1.4 pts.
This piece shows what's really going on: funded candidates do better than unfunded ones.
-- Adam Bonica

The New York Times’ “Moderation Advantage” Is a Statistical Illusion
After accounting for money and incumbency the supposed electoral bonus for moderate candidates vanishes entirely.

Attention Factors for Statistical Arbitrage
Elliot L. Epstein, Rose Wang, Jaewon Choi, Markus Pelger
https://arxiv.org/abs/2510.11616 https://arxiv.org/pdf…

Attention Factors for Statistical Arbitrage
Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings that allow for complex interactions. We identify time-series signals from the re…

Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
Jiaqi Li, Zhipeng Lou, Johannes Schmidt-Hieber, Wei Biao Wu
https://arxiv.org/abs/2510.12013 https://

Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) and its Ruppert-Polyak averaged variant (ASGD) lie at the heart of modern large-scale learning, yet their theoretical properties in high-dimensional settings are rarely understood. In this paper, we provide rigorous statistical guarantees for constant learning-rate SGD and ASGD in high-dimensional regimes. Our key innovation is to transfer powerful tools from high-dimensional time series to online learning. Specifically, by viewing SGD as a nonlinear autoregres…

I was just thinking about how the fact that #Musk named his AI "Grok" is evidence that he "reads sci-fi" in the same way he "plays video games." Like, he claims to do it but when it comes time to show the evidence it's clear he does not actually "grok" it.
Like... To grok something is to have a layer deeper than simply knowledge, but mathematically encoding statistical relationships between words is pretty obviously not even understanding much less qualifying as "groking" it. In the book, the ability to grok something is also the ability to annihilate that thing with a thought. Just pretending that an LLM actually *was* something that could become AGI (which it's not), this name would imply the AI would have the power to annihilate reality. That's bad. That's a bad name for an AI.
And why would a greedy fascist name something of his after something an anarchist communist space Jesus taught to the hippie cult he started? There are so many layers of facepalm to this. It's some kind of php-esque fractal of incompetence.
Like, there's no reason to talk about this but my brain does this to me sometimes and now it's your problem.

"Please refer to this as 'advanced statistical inference' to avoid triggering the Butlerians."

Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Puneet Velidi, Zhengxiao Wei, Shreena Nisha Kalaria, Yimeng Liu, C\'eline M. Laumont, Brad H. Nelson, Farouk S. Nathoo
https://arxiv.org/abs/2510.10358

Generalized Jeffreys's approximate objective Bayes factor: Model-selection consistency, finite-sample accuracy, and statistical evidence in 71,126 clinical trial findings
Concerns about the misuse and misinterpretation of p-values and statistical significance have motivated alternatives for quantifying evidence. We define a generalized form of Jeffreys's approximate objective Bayes factor (eJAB), a one-line calculation that is a function of the p-value, sample size, and parameter dimension. We establish conditions under which eJAB is model-selection consistent and verify them for ten statistical tests. We assess finite-sample accuracy by comparing eJAB with Mark…

The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. #rstats

GitHub - fastverse/fastverse: An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R - fastverse/fastverse

"The greatest statistical analysis is nothing if it can’t be implemented by people. But people learn in different ways. Some like good stories, others like pictures. Only a few like equations..."
https://management.curiouscatblog.net/

In the 1950s, the Air Force realized that planes were crashing because cockpits didn’t actually fit the pilots’ bodies. Wrong size = danger!! They commissioned a researcher to develop a new, more correct set of standard dimensions for the seat, yoke, etc.
That researcher, Gilbert S. Daniels, came up with 10 body measurements that matter to cockpit size. He gathered measurements of several thousand pilots. And the number of people who were at the average for all ten measurements? Zero. Not a single one.
“Average” proved to be a statistical construct, not a thing that actually exists as a person.
https://99percentinvisible.org/episode/on-average/
3/

Beyond the Use-and-then-Forget (UatF) Bound: Fixed Point Algorithms for Statistical Max-Min Power Control
Renato Luis Garrido Cavalcante, Noor Ul Ain, Lorenzo Miretti, Slawomir Stanczak
https://arxiv.org/abs/2510.11582

Beyond the Use-and-then-Forget (UatF) Bound: Fixed Point Algorithms for Statistical Max-Min Power Control
We introduce mathematical tools and fixed point algorithms for optimal statistical max-min power control in cellular and cell-less massive MIMO systems. Unlike previous studies that rely on the use-and-then-forget (UatF) lower bound on Shannon achievable (ergodic) rates, our proposed framework can deal with alternative bounds that explicitly consider perfect or imperfect channel state information (CSI) at the decoder. In doing so, we address limitations of UatF-based algorithms, which inherit t…

N-output Mechanism: Estimating Statistical Information from Numerical Data under Local Differential Privacy
Incheol Baek, Yon Dohn Chung
https://arxiv.org/abs/2510.11116 https:/…

N-output Mechanism: Estimating Statistical Information from Numerical Data under Local Differential Privacy
Local Differential Privacy (LDP) addresses significant privacy concerns in sensitive data collection. In this work, we focus on numerical data collection under LDP, targeting a significant gap in the literature: existing LDP mechanisms are optimized for either a very small ($|Ω| \in \{2, 3\}$) or infinite output spaces. However, no generalized method for constructing an optimal mechanism for an arbitrary output size $N$ exists. To fill this gap, we propose the \textbf{N-output mechan…

Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
Silvie Ill\'esov\'a, Tom\'a\v{s} Bezd\v{e}k, Vojt\v{e}ch Nov\'ak, Bruno Senjean, Martin Beseda
https://arxiv.org/abs/2510.08727

Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
This work investigates the performance of numerical optimization algorithms applied to the State-Averaged Orbital-Optimized Variational Quantum Eigensolver for the H2 molecule under various quantum noise conditions. The goal is to assess the stability, accuracy, and computational efficiency of commonly used gradient-based, gradient-free, and global optimization strategies within the Noisy Intermediate-Scale Quantum regime. We systematically compare six representative optimizers, BFGS, SLSQP, Ne…

TS-Agent: A Time Series Reasoning Agent with Iterative Statistical Insight Gathering
Penghang Liu, Elizabeth Fons, Svitlana Vyetrenko, Daniel Borrajo, Vamsi Potluru, Manuela Veloso
https://arxiv.org/abs/2510.07432

TS-Agent: A Time Series Reasoning Agent with Iterative Statistical Insight Gathering
Large language models (LLMs) have shown strong abilities in reasoning and problem solving, but recent studies reveal that they still struggle with time series reasoning tasks, where outputs are often affected by hallucination or knowledge leakage. In this work we propose TS-Agent, a time series reasoning agent that leverages LLMs strictly for what they excel at, i.e., gathering evidence and synthesizing it into conclusions through step-by-step reasoning, while delegating the extraction of stati…

(Anti)Gravitron: A Statistical Physics Perspective on Multidimensional Metrics of Polarizing Inequality
Rolando Gonzales Martinez
https://arxiv.org/abs/2510.09436 https://

(Anti)Gravitron: A Statistical Physics Perspective on Multidimensional Metrics of Polarizing Inequality
This paper introduces a novel framework for measuring multidimensional inequality based on a statistical physics reinterpretation of centrifugal and centripetal forces in rotating systems. Inspired by the mechanics of the Gravitron and extended via the conceptual AntiGravitron, this study proposes a new class of inequality metrics grounded in multivariate mixtures of Beta distributions. These composite metrics capture three key structural dimensions of polarizing inequality: the number and bala…

statistical collage
iterative pastiche
recursive potpourri

NFL stats and records, Week 18: Browns rookie class, Myles Garrett make history https://www.nfl.com/news/nfl-stats-and-records-week-18-browns-rookie-class-myles-garrett-make-history

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- A framework for the use of generative modelling in non-equilibrium statistical mechanics
Karl J Friston, Maxwell J D Ramstead, Dalton A R Sakthivadivel

RE: https://mastodon.social/@nixCraft/115554484108496189
I still cannot comprehend how anyone could honestly consider a statistical model created from training data to be “intelligent”. Don't you remember the times when people knowingly smiled at anyo…

There is enough data to start publishing reports of my statistical analysis of the Italian Volleyball Serie A1 championship.
https://davideaversa.it/experiment/volley/seriea1w2025.html

After reading a term paper that demonstrated nothing more than the "author's" brainless use of a brainless statistical model to brainlessly vomit brainless prose, I'm deeply grateful for the flawed result of human thinking that turned up second in the grading pile. It's not amazing but it's really not bad and, more importantly, it shows the student learned something. If nothing else, LLMs have given me a new appreciation for the imperfect but honest work we produce …

On Estimation of Angles of Arrival in Monostatic ISAC Without Instantaneous Transmit CSI
Ataher Sams, Simone Di Bari, Besma Smida, Natasha Devroye, Daniela Tuninetti, Giorgio Taricco
https://arxiv.org/abs/2510.08793

On Estimation of Angles of Arrival in Monostatic ISAC Without Instantaneous Transmit CSI
This paper explores the fundamental limits of Integrated Sensing and Communication (ISAC) in a more realistic setting compared to previous literature when the Base Staion (BS) has only statistical CSI of the communication user rather than full CSI. We analyze a monostatic setting where the BS performs multi-target Angle of Arrival (AoA) estimation while simultaneously communicating with one of the targets. We assume that the BS has statistical CSI about all AoAs, with less uncertainty in the Ao…

Hivemind:
I am looking for suitable reading on data for a second-year undergraduate module called "Liberal Arts and the Public Sphere". Any recommendations for monographs or essays on critical data literacy or the importance of data to the political economy are gratefully received. The students do not necessarily have advanced numeracy or statistical skills, so the reading must be fairly accessible.
#HE

Out now in PNAS: Statistical errors undermine claims about the evolution of #polysynthetic #languages by Alex Koplenig and me: #linguistics 🧶 coming up...

@… The important detail to keep in mind, in my opinion, is that it’s a large statistical model.
Answers you get aren’t “known” but rather “likely” based on the text trained upon.
That’s a really important nuance for anything fact based.

Statistical properties of Markov shifts (part I)
Yeor Hafouta
https://arxiv.org/abs/2510.07757 https://arxiv.org/pdf/2510.07757

Statistical properties of Markov shifts (part I)
We prove central limit theorems, Berry-Esseen type theorems, almost sure invariance principles, large deviations and Livsic type regularity for partial sums of the form $S_n=\sum_{j=0}^{n-1}f_j(...,X_{j-1},X_j,X_{j+1},...)$, where $(X_j)$ is an inhomogeneous Markov chain satisfying some mixing assumptions and $f_j$ is a sequence of sufficiently regular functions. Even though the case of non-stationary chains and time dependent functions $f_j$ is more challenging, our results seem to be new alre…

Modelling Territorial Dynamics through Statistical Mechanics: An Application to Resident Foreign Population
Pierpaolo Massoli
https://arxiv.org/abs/2510.10784 https://

Modelling Territorial Dynamics through Statistical Mechanics: An Application to Resident Foreign Population
This study introduces a statistical mechanics framework to analyze the territorial distribution of the Resident Foreign Population across Italian municipalities. The observed percentages of foreign residents are treated as the reference configuration of an interacting system of territorial units. The socio-economic structure of municipalities is summarized through interpretable composite indices and reduced via Principal Components Analysis to construct a univariate external field compatible wi…

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Disharee Bhowmick, Ranjith Ramanathan, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2510.09593

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Time series data often contain latent temporal structure, transitions between locally stationary regimes, repeated motifs, and bursts of variability, that are rarely leveraged in standard representation learning pipelines. Existing models typically operate on raw or fixed-window sequences, treating all time steps as equally informative, which leads to inefficiencies, poor robustness, and limited scalability in long or noisy sequences. We propose STaTS, a lightweight, unsupervised framework for …

Universal scale-free representations in human visual cortex https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013714 "revealing a universal high-dimensional spectrum of visual information that emerges despite individual differenc…

Universal scale-free representations in human visual cortex
Author summary The human cerebral cortex is thought to encode sensory information in population activity patterns, but the statistical structure of these population codes has yet to be characterized. By examining large-scale neuroimaging recordings of human vision using a spectral approach more common in physics than neuroscience, we reveal the universal scale-free structure of population codes in visual cortex, which is found in all subjects and at multiple stages of visual processing. Moreove…

Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noise
Debsurya De, Dmitriy Kunisky
https://arxiv.org/abs/2510.08541 https://

Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noise
Recent work has generalized several results concerning the well-understood spiked Wigner matrix model of a low-rank signal matrix corrupted by additive i.i.d. Gaussian noise to the inhomogeneous case, where the noise has a variance profile. In particular, for the special case where the variance profile has a block structure, a series of results identified an effective spectral algorithm for detecting and estimating the signal, identified the threshold signal strength required for that algorithm…

Statistical Shape Modelling of the Nasosinusal Anatomy
Michele Bertolini (POLIMI), Silvia Tonghini (POLIMI), Marco Rossoni (POLIMI), Marina Carulli (POLIMI), Susanne Weise (TU Dresden), Christian Jan Baldus (POLIMI), Giorgio Colombo (POLIMI), Monica Bordegoni (POLIMI)
https://arxiv.org/abs/2510.09713…

Statistical Shape Modelling of the Nasosinusal Anatomy
The human nose exhibits a huge variation in shape among individuals. All these variants alter the airflow through the nasal cavity and can impact how we smell odors. To acquire a better understanding of physiological and pathological functioning, it is important to study the effects of these modifications. SSM, or Statistical Shape Modelling, is a widely used methodology for considering morphological differences within a population. In the literature, only a few studies analyze nasal anatomy in…

hear me out, if you’re a government and want your country to do more science wouldn’t it be better to hire more scientists instead of hiring more statistical text randomizers

StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis
Siyuan Li, Aodu Wulianghai, Xi Lin, Guangyan Li, Xiang Chen, Jun Wu, Jianhua Li
https://arxiv.org/abs/2510.12608

StyleDecipher: Robust and Explainable Detection of LLM-Generated Texts with Stylistic Analysis
With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task for ensuring content authenticity and trust. Existing approaches rely on statistical discrepancies or model-specific heuristics to distinguish between LLM-generated and human-written text. However, these methods struggle in real-world scenarios due to limited generalization, vulnerability to paraphrasing, and lack of explainability, particularly w…

The Mechanical Yes-Man: Emancipatory AI Pedagogy in Higher Education
Linda Rocco
https://arxiv.org/abs/2510.10176 https://arxiv.org/pdf/2510.10176

The Mechanical Yes-Man: Emancipatory AI Pedagogy in Higher Education
The proliferation of Large Language Models in higher education presents a fundamental challenge to traditional pedagogical frameworks. Drawing on Jacques Rancière's theory of intellectual emancipation, this paper examines how generative AI risks becoming a "mechanical yes-man" that reinforces passivity rather than fostering intellectual autonomy. Generative AI's statistical logic and lack of causal reasoning, combined with frictionless information access, threatens to hollow out cognitive proc…

YSO Variability in the W51 Star-Forming Region
Mi-Ryang Kim, Jeong-Eun Lee, Contreras Pe\~na Carlos, Gregory Herczeg, Doug Johnstone, Miju Kang
https://arxiv.org/abs/2510.12212 …

YSO Variability in the W51 Star-Forming Region
Time-domain studies of mid-infrared and submillimeter variability have shown that at least half of protostars are variable. We present a statistical analysis of mid-infrared variability among young stellar objects (YSOs) in the distant, massive star-forming region W51 using NEOWISE data. From a catalog of 81 protostars, 527 disk objects, and 37,687 other sources including diskless pre-main sequence and evolved contaminants, we identified significant variability in the 3.4 um (W1) and 4.6 um (W2…

On The Roots of Independence Polynomial: Quantifying The Gap
Om Prakash, Vikram Sharma
https://arxiv.org/abs/2510.09197 https://arxiv.org/pdf/2510.09197

On The Roots of Independence Polynomial: Quantifying The Gap
The independence polynomial of a graph $G$ is the generating polynomial corresponding to its independent sets of different sizes. More formally, if $a_k(G)$ denotes the number of independent sets of $G$ of size $k$ then \[I(G,z) \as \sum_{k}^{} (-1)^k a_k(G) z^k.\] The study of evaluating $I(G,z)$ has several deep connections to problems in combinatorics, complexity theory and statistical physics. Consequently, the roots of the independence polynomial have been studied in detail. In particula…

Statistical Analysis of Target Parameter Estimation Using Passive Radar
Mats Viberg, Daniele Gerosa, Tomas McKelvey, Thomas Eriksson
https://arxiv.org/abs/2510.07948 https://

Statistical Analysis of Target Parameter Estimation Using Passive Radar
A passive radar system uses one or more so-called Illuminators of Opportunity (IO) to detect and localize targets. In such systems, a reference channel is often used at each receiving node to capture the transmitted IO signal, while targets are detected using the main surveillance channel. The purpose of the present contribution is to analyze a method for estimating the target parameters in such a system. Specifically, we quantify the additional error contribution due to not knowing the transmi…

GrifFinNet: A Graph-Relation Integrated Transformer for Financial Predictions
Chenlanhui Dai, Wenyan Wang, Yusi Fan, Yueying Wang, Lan Huang, Kewei Li, Fengfeng Zhou
https://arxiv.org/abs/2510.10387

GrifFinNet: A Graph-Relation Integrated Transformer for Financial Predictions
Predicting stock returns remains a central challenge in quantitative finance, transitioning from traditional statistical methods to contemporary deep learning techniques. However, many current models struggle with effectively capturing spatio-temporal dynamics and integrating multiple relational data sources. This study proposes GrifFinNet, a Graph-Relation Integrated Transformer for Financial Predictions, which combines multi-relational graph modeling with Transformer-based temporal encoding. …

NFL stats and records, Week 13: Bills QB Josh Allen continues to break records https://www.nfl.com/news/nfl-stats-and-records-week-13-bills-qb-josh-allen-continues-to-break-records

AQORA: A Learned Adaptive Query Optimizer for Spark SQL
Jiahao He, Yutao Cui, Cuiping Li, Jikang Jiang, Yuheng Hou, Hong Chen
https://arxiv.org/abs/2510.10580 https://

AQORA: A Learned Adaptive Query Optimizer for Spark SQL
Recent studies have identified two main approaches to improve query optimization: learned query optimization (LQO), which generates or selects better query plans before execution based on models trained in advance, and adaptive query processing (AQP), which adapts the query plan during execution based on statistical feedback collected at runtime. Although both approaches have shown promise, they also face critical limitations. LQO must commit to a fixed plan without access to actual cardinaliti…

User interface pretending to be human: fraud.
Statistical sentence generation described as “intelligence”: fraud.
Telling people, often children, to commit dangerous or criminal acts: all sorts of crimes.
Scientific papers generated, non-existing cases quoted at court, homework faked, artists ripped-off: fraud and copyright infringement.
Training based on unlicensed works: copyright infringement.
Data centers with tax incentives and special energy pricing: theft.
Promises to investors of AGI: fraud.
Government bailouts with taxpayer money: theft and fraud.
—
Media and politicians: This is innovation, don’t get left behind.

Cowboys defense much improved since bye week thanks to $21 million group https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/11/27/the-cowboys-defense-l…

Finally, what Xia & Lindell call a "separation problem" is, in our view, a feature of our approach and not a bug.
If, e.g., all languages in a family are polysynthetic (or none are), that’s not a statistical artefact – it’s the signal. The outcome is well associated with genealogy, showing that family membership captures someth genuinely informative about the process. When the model finds that family explains a large share of the variance, that's not a failure–it's evidence that phylogenetic structure dominates the pattern.
So while Xia & Lindell insist that "autocorrelation due to relationships and distance cannot be captured in family or regional-level analyses", we see that as an empirical question – and we treated it as one.
The real test is whether a mixed model that explicitly represents phylogeny and geography performs worse than their alternative, where the entire shared history of languages and environments is effectively collapsed into a single dimension (an eigenvector).
In other words: we model relationships – Xia & Lindell summarise them into one number per language.

Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response
Yiheng Chen, Lingyao Li, Zihui Ma, Qikai Hu, Yilun Zhu, Min Deng, Runlong Yu
https://arxiv.org/abs/2510.12061

Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response
Effective disaster response is essential for safeguarding lives and property. Existing statistical approaches often lack semantic context, generalize poorly across events, and offer limited interpretability. While Large language models (LLMs) provide few-shot generalization, they remain text-bound and blind to geography. To bridge this gap, we introduce a Geospatial Awareness Layer (GAL) that grounds LLM agents in structured earth data. Starting from raw wildfire detections, GAL automatically r…

High-Probability Bounds For Heterogeneous Local Differential Privacy
Maryam Aliakbarpour, Alireza Fallah, Swaha Roy, Ria Stevens
https://arxiv.org/abs/2510.11895 https://…

High-Probability Bounds For Heterogeneous Local Differential Privacy
We study statistical estimation under local differential privacy (LDP) when users may hold heterogeneous privacy levels and accuracy must be guaranteed with high probability. Departing from the common in-expectation analyses, and for one-dimensional and multi-dimensional mean estimation problems, we develop finite sample upper bounds in $\ell_2$-norm that hold with probability at least $1-β$. We complement these results with matching minimax lower bounds, establishing the optimality (up to con…

Crosslisted article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Error thresholds of toric codes with transversal logical gates
Yichen Xu, Yiqing Zhou, James P. Sethna, Eun-Ah Kim

How to Get Actual Privacy and Utility from Privacy Models: the k-Anonymity and Differential Privacy Families
Josep Domingo-Ferrer, David S\'anchez
https://arxiv.org/abs/2510.11299

How to Get Actual Privacy and Utility from Privacy Models: the k-Anonymity and Differential Privacy Families
Privacy models were introduced in privacy-preserving data publishing and statistical disclosure control with the promise to end the need for costly empirical assessment of disclosure risk. We examine how well this promise is kept by the main privacy models. We find they may fail to provide adequate protection guarantees because of problems in their definition or incur unacceptable trade-offs between privacy protection and utility preservation. Specifically, k-anonymity may not entirely exclude …

Experimental verification of multi-copy activation of genuine multipartite entanglement
Robert St\'arek, Tim Gollerthan, Olga Leskovjanov\'a, Michael Meth, Peter Tirler, Nicolai Friis, Martin Ringbauer, Ladislav Mi\v{s}ta Jr
https://arxiv.org/abs/2510.12457

Experimental verification of multi-copy activation of genuine multipartite entanglement
A central concept in quantum information processing is genuine multipartite entanglement (GME), a type of correlation beyond biseparability, that is, correlations that cannot be explained by statistical mixtures of partially separable states. GME is relevant for characterizing and benchmarking complex quantum systems, and it is an important resource for applications such as quantum communication. Remarkably, it has been found that GME can be activated from multiple copies of biseparable quantum…

NFL stats and records, Week 17: Patriots QB Drake Maye makes case for MVP https://www.nfl.com/news/nfl-stats-and-records-week-17-patriots-qb-drake-maye-makes-case-for-mvp-award

Remarkable that there's apparently no step between studies and official US government health advice.
It reminds me of the, mentally overwhelming, pattern in health magazines that suggest changes to diet every month often on the basis of a single university paper's statistical findings.

The 2025 Chiefs May Be the Most Complete Team in the NFL https://insidethestar.com/the-2025-chiefs-may-be-the-most-complete-team-in-the-nfl

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique
https://arxiv.org/abs/2510.12076

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anoma…

Spatio-Temporal Evolution of the March 2022 ICME Revealed by Multi-Point Observations of Forbush Decreases
Gaku Kinoshita, Beatriz Sanchez-Cano, Yoshizumi Miyoshi, Laura Rodoriguez-Garcia, Emilia Kilpua, Benoit Lavraud, Mathias Rojo, Marco Pinto, Yuki Harada, Go Murakami, Yoshifumi Saito, Shoichiro Yokota, Daniel Heyner, David Fischer, Nicolas Andre, Kazuo Yoshioka

Spatio-Temporal Evolution of the March 2022 ICME Revealed by Multi-Point Observations of Forbush Decreases
Interplanetary coronal mass ejections (ICMEs) cause Forbush Decreases (FDs) effects, which are local decreases in background galactic cosmic rays (GCR). Even though FDs can be observed with simple particle instruments, their amplitude and shape provide physical profiles of passing ICMEs. However, in some cases, previous statistical studies of the heliocentric distance dependence of FD changes associated with ICME propagation have found no strong correlation. We need the criteria for evaluating …

Unravelling inter-channel quantum interference in below-threshold nonsequential double ionization with statistical measures
S. Hashim, C. Figueira de Morisson Faria
https://arxiv.org/abs/2510.16135

Rate optimal learning of equilibria from data
Till Freihaut, Luca Viano, Emanuele Nevali, Volkan Cevher, Matthieu Geist, Giorgia Ramponi
https://arxiv.org/abs/2510.09325 https:/…

Rate optimal learning of equilibria from data
We close open theoretical gaps in Multi-Agent Imitation Learning (MAIL) by characterizing the limits of non-interactive MAIL and presenting the first interactive algorithm with near-optimal sample complexity. In the non-interactive setting, we prove a statistical lower bound that identifies the all-policy deviation concentrability coefficient as the fundamental complexity measure, and we show that Behavior Cloning (BC) is rate-optimal. For the interactive setting, we introduce a framework that …

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency without Model Sweeps
Do Tien Hai, Trung Nguyen Mai, TrungTin Nguyen, Nhat Ho, Binh T. Nguyen, Christopher Drovandi
https://arxiv.org/abs/2510.12744

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency without Model Sweeps
We develop a unified statistical framework for softmax-gated Gaussian mixture of experts (SGMoE) that addresses three long-standing obstacles in parameter estimation and model selection: (i) non-identifiability of gating parameters up to common translations, (ii) intrinsic gate-expert interactions that induce coupled differential relations in the likelihood, and (iii) the tight numerator-denominator coupling in the softmax-induced conditional density. Our approach introduces Voronoi-type loss f…

Targeted Pooled Latent-Space Steganalysis Applied to Generative Steganography, with a Fix
Etienne Levecque (LIST3N), Aur\'elien Noirault (CRIStAL), Tom\'a\v{s} Pevn\'y (CTU), Jan Butora (CRIStAL), Patrick Bas (CRIStAL), R\'emi Cogranne (LIST3N)
https://arxiv.org/abs/2510.12414

Targeted Pooled Latent-Space Steganalysis Applied to Generative Steganography, with a Fix
Steganographic schemes dedicated to generated images modify the seed vector in the latent space to embed a message, whereas most steganalysis methods attempt to detect the embedding in the image space. This paper proposes to perform steganalysis in the latent space by modeling the statistical distribution of the norm of the latent vector. Specifically, we analyze the practical security of a scheme proposed by Hu et. al. for latent diffusion models, which is both robust and practically undetecta…

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Boundary layers, transport and universal distribution in boundary driven active systems
Pritha Dolai, Arghya Das

NFL stats and records, Week 8: Bills' Josh Allen continues QB scoring trend; Myles Garrett delivers five-sack performance in Browns' loss https://www.nfl.com/news/nfl-stats-and-records-week-8-bills-josh-allen-con…

A Structural Theory of Quantum Metastability: Markov Properties and Area Laws
Thiago Bergamaschi, Chi-Fang Chen, Umesh Vazirani
https://arxiv.org/abs/2510.08538 https://

A Structural Theory of Quantum Metastability: Markov Properties and Area Laws
Statistical mechanics assumes that a quantum many-body system at low temperature can be effectively described by its Gibbs state. However, many complex quantum systems exist only as metastable states of dissipative open system dynamics, which appear stable and robust yet deviate substantially from true thermal equilibrium. In this work, we model metastable states as approximate stationary states of a quasi-local, (KMS)-detailed-balanced master equation representing Markovian system-bath interac…

On goodness-of-fit testing for volatility in McKean-Vlasov models
Akram Heidari, Mark Podolskij
https://arxiv.org/abs/2510.12607 https://arxiv.org/pdf/2510…

On goodness-of-fit testing for volatility in McKean-Vlasov models
This paper develops a statistical framework for goodness-of-fit testing of volatility functions in McKean-Vlasov stochastic differential equations, which describe large systems of interacting particles with distribution-dependent dynamics. While integrated volatility estimation in classical SDEs is now well established, formal model validation and goodness-of-fit testing for McKean-Vlasov systems remain largely unexplored, particularly in regimes with both large particle limits and high-frequen…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[17/17]:
- Attention Factors for Statistical Arbitrage
Elliot L. Epstein, Rose Wang, Jaewon Choi, Markus Pelger

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Abdullahi Mohammad, Bdah Eya, Bassant Selim
https://arxiv.org/abs/2510.12179 https://

A Deep Multi-Task Learning Approach to Impulsive Noise Parameter Estimation
Impulsive noise poses a significant challenge to the reliability of wireless communication systems, necessitating accurate estimation of its statistical parameters for effective mitigation. This paper introduces a multitask learning (MTL) framework based on a CNN-LSTM architecture enhanced with an attention mechanism for the joint estimation of impulsive noise parameters. The proposed model leverages a unified weighted-loss function to enable simultaneous learning of multiple parameters within …

NFL stats and records, Week 12: RB Jahmyr Gibbs closes in on Lions legend https://www.nfl.com/news/nfl-stats-and-records-week-12-rb-jahmyr-gibbs-closes-in-on-lions-legend

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Tsuyoshi Okita
https://arxiv.org/abs/2510.08295

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (C…

Crosslisted article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Combined Representation and Generation with Diffusive State Predictive Information Bottleneck
Richard John, Yunrui Qiu, Lukas Herron, Pratyush Tiwary

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
Shai Zucker, Xiong Wang, Fei Lu, Inbar Seroussi
https://arxiv.org/abs/2510.11789 https:…

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
We study the convergence rate of learning pairwise interactions in single-layer attention-style models, where tokens interact through a weight matrix and a non-linear activation function. We prove that the minimax rate is $M^{-\frac{2β}{2β+1}}$ with $M$ being the sample size, depending only on the smoothness $β$ of the activation, and crucially independent of token count, ambient dimension, or rank of the weight matrix. These results highlight a fundamental dimension-free statistical efficie…

Monitoring 3D Lattice Structures in Additive Manufacturing Using Topological Data Analysis
Yulin An, Xueqi Zhao, Enrique del Castillo
https://arxiv.org/abs/2510.11740 https://…

Monitoring 3D Lattice Structures in Additive Manufacturing Using Topological Data Analysis
We present a new method for the statistical process control of lattice structures using tools from Topological Data Analysis. Motivated by applications in additive manufacturing, such as aerospace components and biomedical implants, where hollow lattice geometries are critical, the proposed framework is based on monitoring the persistent homology properties of parts. Specifically, we focus on homological features of dimensions zero and one, corresponding to connected components and one-dimensio…

Toward Mechanistic Explanation of Deductive Reasoning in Language Models
Davide Maltoni, Matteo Ferrara
https://arxiv.org/abs/2510.09340 https://arxiv.org/…

Toward Mechanistic Explanation of Deductive Reasoning in Language Models
Recent large language models have demonstrated relevant capabilities in solving problems that require logical reasoning; however, the corresponding internal mechanisms remain largely unexplored. In this paper, we show that a small language model can solve a deductive reasoning task by learning the underlying rules (rather than operating as a statistical learner). A low-level explanation of its internal representations and computational circuits is then provided. Our findings reveal that inducti…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noise
Debsurya De, Dmitriy Kunisky

Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective
Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
https://arxiv.org/abs/2510.12763

Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective
Neurodegeneration, characterized by the progressive loss of neuronal structure or function, is commonly assessed in clinical practice through reductions in cortical thickness or brain volume, as visualized by structural MRI. While informative, these conventional approaches lack the statistical sophistication required to fully capture the spatially correlated and heterogeneous nature of neurodegeneration, which manifests both in healthy aging and in neurological disorders. To address these limit…

Crosslisted article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Generalised fractional Rabi problem
Alexander Lopez, S\'ebastien Fumeron, Malte Henkel, Trifce Sandev, Esther D. Guti\'errez

NFL stats and records, Week 16: Jaguars QB Trevor Lawrence's recent stretch on par with past MVPs https://www.nfl.com/news/nfl-stats-and-records-week-16-jaguars-qb-trevor-lawrence-s-recent-stretch-on-par-with-past-mvps

A unified Bayesian framework for adversarial robustness
Pablo G. Arce, Roi Naveiro, David R\'ios Insua
https://arxiv.org/abs/2510.09288 https://arxiv.o…

A unified Bayesian framework for adversarial robustness
The vulnerability of machine learning models to adversarial attacks remains a critical security challenge. Traditional defenses, such as adversarial training, typically robustify models by minimizing a worst-case loss. However, these deterministic approaches do not account for uncertainty in the adversary's attack. While stochastic defenses placing a probability distribution on the adversary exist, they often lack statistical rigor and fail to make explicit their underlying assumptions. To reso…

PAC Reasoning: Controlling the Performance Loss for Efficient Reasoning
Hao Zeng, Jianguo Huang, Bingyi Jing, Hongxin Wei, Bo An
https://arxiv.org/abs/2510.09133 https://…

PAC Reasoning: Controlling the Performance Loss for Efficient Reasoning
Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during deployment, highlighting a need for efficient inference. A popular direction of efficiency improvement is to switch the LRM between thinking and nonthinking modes dynamically. However, such approaches often introduce additional reasoning errors and lack statistical guarantees for the performance loss, which are critica…

Cowboys' Dak Prescott leads NFL in major stat entering final month of season https://www.si.com/nfl/cowboys/onsi/news/dallas-cowboys-dak-prescott-leads-nfl-major-stat-entering-final-month-of-season

Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Universal finite-size scaling in high-dimensional critical phenomena
Yucheng Liu, Jiwoon Park, Gordon Slade

Distribution-Free Prediction Sets for Regression under Target Shift
Menghan Yi, Yanlin Tang, Huixia Judy Wang
https://arxiv.org/abs/2510.10985 https://arxi…

Distribution-Free Prediction Sets for Regression under Target Shift
In real-world applications, the limited availability of labeled outcomes presents significant challenges for statistical inference due to high collection costs, technical barriers, and other constraints. In this work, we propose a method to construct efficient conformal prediction sets for new target outcomes by leveraging a source distribution that is distinct from the target but related through a distributional shift assumption and provides abundant labeled data. When the target data are full…

NFL stats and records, Week 11: Bills QB Josh Allen continues setting records https://www.nfl.com/news/nfl-stats-and-records-week-11-bills-qb-josh-allen-continues-setting-records

Robust Sampling for Active Statistical Inference
Puheng Li, Tijana Zrnic, Emmanuel Cand\`es
https://arxiv.org/abs/2511.08991 https://arxiv.org/pdf/2511.089…

Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
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Replaced article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Topological phases in discrete stochastic systems
Jaime Agudo-Canalejo, Evelyn Tang

Crosslisted article(s) found for cond-mat.stat-mech. https://arxiv.org/list/cond-mat.stat-mech/new
[1/1]:
- Emergent spacetime supersymmetry at 2D fractionalized quantum criticality
Zhengzhi Wu, Zhou-Quan Wan, Shao-Kai Jian, Hong Yao

NFL stats and records, Week 15: Bills' Josh Allen joins elite company with sixth season of 35-plus offensive TDs https://www.nfl.com/news/nfl-stats-and-records-week-15-bills-josh-allen-joins-elite-company-wi…

[2025-10-15 Wed (UTC), 7 new articles found for cond-mat.stat-mech Statistical Mechanics]
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[2025-10-14 Tue (UTC), 7 new articles found for cond-mat.stat-mech Statistical Mechanics]
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[2025-10-13 Mon (UTC), 6 new articles found for cond-mat.stat-mech Statistical Mechanics]
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