Tootfinder

from my link log —
ROCkN: Providing GPS-quality timing accuracy with portable optical atomic clocks.
https://www.darpa.mil/news-events/2022-01-20
saved 2023-10-05

Resource-sensitive but language-blind: Community size and not grammatical complexity better predicts the accuracy of Large Language Models in a novel Wug Test
Nikoleta Pantelidou, Evelina Leivada, Paolo Morosi
https://arxiv.org/abs/2510.12463

Resource-sensitive but language-blind: Community size and not grammatical complexity better predicts the accuracy of Large Language Models in a novel Wug Test
The linguistic abilities of Large Language Models are a matter of ongoing debate. This study contributes to this discussion by investigating model performance in a morphological generalization task that involves novel words. Using a multilingual adaptation of the Wug Test, six models were tested across four partially unrelated languages (Catalan, English, Greek, and Spanish) and compared with human speakers. The aim is to determine whether model accuracy approximates human competence and whethe…

Doc: Amazon's Q Business AI assistant struggled with accuracy and data processing in its first year, drawing complaints; Amazon says the document is outdated (Eugene Kim/Business Insider)
https://www.businessinsider.com/amazon-q-significan…

Amazon internal review found its AI tool Q fell 'significantly' behind rivals on accuracy in its first year
Amazon's AI productivity tool Q Business faced significant accuracy challenges in its first year, leading to customer complaints and service updates.

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 Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky, Mohamed Amine Ferrag, Tam\'as Bisztray
https://arxiv.org/abs/2510.10493

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicious content, and ensuring accountability. While AI-vs-human detection usually treats AI as a single category we show that individual LLMs le…

Average Kernel Sizes -- Computable Sharp Accuracy Bounds for Inverse Problems
Nina M. Gottschling, David Iagaru, Jakob Gawlikowski, Ioannis Sgouralis
https://arxiv.org/abs/2510.10229

Average Kernel Sizes -- Computable Sharp Accuracy Bounds for Inverse Problems
The reconstruction of an unknown quantity from noisy measurements is a mathematical problem relevant in most applied sciences, for example, in medical imaging, radar inverse scattering, or astronomy. This underlying mathematical problem is often an ill-posed (non-linear) reconstruction problem, referred to as an ill-posed inverse problem. To tackle such problems, there exist a myriad of methods to design approximate inverse maps, ranging from optimization-based approaches, such as compressed se…

Rethinking Knowledge Distillation: A Data Dependent Regulariser With a Negative Asymmetric Payoff
Israel Mason-Williams, Gabryel Mason-Williams, Helen Yannakoudakis
https://arxiv.org/abs/2510.12615

Rethinking Knowledge Distillation: A Data Dependent Regulariser With a Negative Asymmetric Payoff
Knowledge distillation is often considered a compression mechanism when judged on the resulting student's accuracy and loss, yet its functional impact is poorly understood. In this work, we quantify the compression capacity of knowledge distillation and the resulting knowledge transfer from a functional perspective, decoupling compression from architectural reduction, which provides an improved understanding of knowledge distillation. We employ hypothesis testing, controls, and random control d…

Tiny-R1V: Lightweight Multimodal Unified Reasoning Model via Model Merging
Qixiang Yin, Huanjin Yao, Jianghao Chen, Jiaxing Huang, Zhicheng Zhao, Fei Su
https://arxiv.org/abs/2510.08987

Tiny-R1V: Lightweight Multimodal Unified Reasoning Model via Model Merging
Although Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse tasks, they encounter numerous challenges in terms of reasoning efficiency, such as large model size, overthinking, and compromised accuracy in lightweight scenarios. However, research on the reasoning capabilities of lightweight MLLMs is quite lacking. To this end, we propose Tiny-R1V, a novel lightweight 3B model that achieves faster inference and higher accuracy via a two-stage optimiza…

Data or Language Supervision: What Makes CLIP Better than DINO?
Yiming Liu, Yuhui Zhang, Dhruba Ghosh, Ludwig Schmidt, Serena Yeung-Levy
https://arxiv.org/abs/2510.11835 https:/…

Data or Language Supervision: What Makes CLIP Better than DINO?
CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs), but it remains unclear whether this advantage stems from CLIP's language supervision or its much larger training data. To disentangle these factors, we pre-train CLIP and DINO under controlled settings -- using the same architecture, dataset, and training configuration -- achieving similar ImageNet accuracy. Embedding analysis shows that CLIP captures high-level semantics (e.g., object catego…

Designing Tools with Control Confidence
Ajith Anil Meera, Abian Torres, Pablo Lanillos
https://arxiv.org/abs/2510.12630 https://arxiv.org/pdf/2510.12630

Designing Tools with Control Confidence
Prehistoric humans invented stone tools for specialized tasks by not just maximizing the tool's immediate goal-completion accuracy, but also increasing their confidence in the tool for later use under similar settings. This factor contributed to the increased robustness of the tool, i.e., the least performance deviations under environmental uncertainties. However, the current autonomous tool design frameworks solely rely on performance optimization, without considering the agent's confidence in…

Faster State Preparation with Randomization
Yue Wang, Xiao-Ming Zhang, Xiao Yuan, Qi Zhao
https://arxiv.org/abs/2510.12247 https://arxiv.org/pdf/2510.12247…

Faster State Preparation with Randomization
Quantum state preparation remains a dominant cost in many quantum algorithms. We introduce a randomized protocol that fundamentally improves the accuracy-cost trade-off for states with hierarchical amplitude structures, where amplitudes decay exponentially or by a power law with exponent greater than one. Rather than commonly employed deterministically truncating small amplitudes, we prepare an ensemble of simple circuits: each retains all large amplitudes while amplifying a single small one. W…

The Cost of Simplicity: How Reducing EEG Electrodes Affects Source Localization and BCI Accuracy
Eva Guttmann-Flury, Yanyan Wei, Shan Zhao, Jian Zhao, Mohamad Sawan
https://arxiv.org/abs/2510.10770

The Cost of Simplicity: How Reducing EEG Electrodes Affects Source Localization and BCI Accuracy
Electrode density optimization in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) requires balancing practical usability against signal fidelity, particularly for source localization. Reducing electrodes enhances portability but its effects on neural source reconstruction quality and source connectivity - treated as proxies to BCI performance - remain understudied. We address this gap through systematic evaluation of 62-, 32-, and 16-channel configurations using a fixed, ful…

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…

Evaluating End-User Device Energy Models in Sustainability Reporting of Browser-Based Web Services
Maja H. Kirkeby, Timmie Lagermann
https://arxiv.org/abs/2510.12566 https://

Evaluating End-User Device Energy Models in Sustainability Reporting of Browser-Based Web Services
Sustainability reporting in web-based services increasingly relies on simplified energy and carbon models such as the Danish Agency of Digital Government's Digst framework and the United Kingdom-based DIMPACT model. Although these models are widely adopted, their accuracy and precision remain underexplored. This paper presents an empirical study evaluating how well such models reflect actual energy consumption during realistic user interactions with common website categories. Energy use was mea…

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Suyash Fulay, Jocelyn Zhu, Michiel Bakker
https://arxiv.org/abs/2510.12689 ht…

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Large language models (LLMs) have shown promising accuracy in predicting survey responses and policy preferences, which has increased interest in their potential to represent human interests in various domains. Most existing research has focused on behavioral cloning, effectively evaluating how well models reproduce individuals' expressed preferences. Drawing on theories of political representation, we highlight an underexplored design trade-off: whether AI systems should act as delegates, mirr…

FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Sijing Xie, Dingzhu Wen, Changsheng You, Qimei Chen, Mehdi Bennis, Kaibin Huang
https://arxiv.org/abs/2510.12078

FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further enhance generalization ability while reducing training costs, this paper proposes Federated LoRA with Dropout (FedLoDrop), a new framework that applies dropout to the rows and columns of the trainable matrix in Federated LoRA. A generalization error bound and convergence analysis under sparsity regularization are obta…

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods
Mohammad Noormohammadi, Atefeh Javadi, Mehdi Khakian Ghomi
https://arxiv.org/abs/2510.10296

Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods
Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine learning methods and high-accuracy data, we investigate the possibility of detecting multiple structures within 500 arcmin of ASCC 32. We f…

Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
Meihui Zhang, Liming Wang, Chi Zhang, Zhaojing Luo
https://arxiv.org/abs/2510.12642 https://…

Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
A growing trend in modern data analysis is the integration of data management with learning, guided by accuracy, latency, and cost requirements. In practice, applications draw data of different formats from many sources. In the meanwhile, the objectives and budgets change over time. Existing systems handle these applications across databases, analysis libraries, and tuning services. Such fragmentation leads to complex user interaction, limited adaptability, suboptimal performance, and poor exte…

An Empirical Study for Representations of Videos in Video Question Answering via MLLMs
Zhi Li, Yanan Wang, Hao Niu, Julio Vizcarra, Masato Taya
https://arxiv.org/abs/2510.12299 …

An Empirical Study for Representations of Videos in Video Question Answering via MLLMs
Multimodal large language models have recently achieved remarkable progress in video question answering (VideoQA) by jointly processing visual, textual, and audio information. However, it remains unclear which video representations are most effective for MLLMs, and how different modalities balance task accuracy against computational efficiency. In this work, we present a comprehensive empirical study of video representation methods for VideoQA with MLLMs. We systematically evaluate single modal…

A comprehensive look into the accuracy of SpEC binary black hole waveforms
Taylor Knapp, Katerina Chatziioannou, Keefe Mitman, Mark A. Scheel, Michael Boyle, Lawrence E. Kidder, Harald Pfeiffer
https://arxiv.org/abs/2510.06393

A comprehensive look into the accuracy of SpEC binary black hole waveforms
Numerical relativity simulations provide a full description of the dynamics of binary systems, including gravitational radiation. The waveforms produced by these simulations have a number of applications in gravitational-wave detection and inference. In this work, we revisit the accuracy of the waveforms produced by the Spectral Einstein Code. Motivated by the wide range of waveform applications, we propose and explore three accuracy metrics between simulation resolutions: (i) the generalized f…

"AI-powered oil spill prediction system can improve emergency response accuracy by up to 25%"
#AI #ArtificialIntelligence #Technology

AI-powered oil spill prediction system can improve emergency response accuracy by up to 25%
Oil spills can be among the most devastating environmental disasters, with the potential to severely damage marine ecosystems, disrupt coastal communities, and impose lasting economic damage. Traditional numerical models, such as MEDSLIK-II, simulate the movement and transformation of oil particles in seawater, but their accuracy has been limited by dependence on expert judgment for tuning critical physical parameters. This manual calibration process, while informed by experience, is not always…

Serial-Parallel Dual-Path Architecture for Speaking Style Recognition
Guojian Li, Qijie Shao, Zhixian Zhao, Shuiyuan Wang, Zhonghua Fu, Lei Xie
https://arxiv.org/abs/2510.11732 …

Serial-Parallel Dual-Path Architecture for Speaking Style Recognition
Speaking Style Recognition (SSR) identifies a speaker's speaking style characteristics from speech. Existing style recognition approaches primarily rely on linguistic information, with limited integration of acoustic information, which restricts recognition accuracy improvements. The fusion of acoustic and linguistic modalities offers significant potential to enhance recognition performance. In this paper, we propose a novel serial-parallel dual-path architecture for SSR that leverages acoustic…

An AI dose engine for fast carbon ion treatment planning
Anastasiia Quarz, Angelica De Gregorio, Gaia Franciosini, Angelo Schiavi, Zolt\'an Perk\'o, Lennart Volz, Vincenzo Patera, Marco Durante, Christian Graeff
https://arxiv.org/abs/2510.11271

An AI dose engine for fast carbon ion treatment planning
Monte Carlo (MC) simulations provide gold-standard accuracy for carbon ion therapy dose calculations but are computationally intensive. Analytical pencil beam algorithms offer speed but reduced accuracy in heterogeneous tissues. We developed the first AI-based dose engine capable of predicting absorbed dose, the alpha and beta parameters for relative biological effectiveness (RBE)- weighted optimisation in carbon ion therapy, delivering MC-level accuracy with drastically reduced computation tim…

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 4: Validation and Performance
Collaboration, Martinelli, Pezzotta, Sciotti, Blot, Bonici, Camera, Ca\~nas-Herrera, Cardone, Carrilho, Casas, Davini, Di Domizio, Farrens, Goh, Beauchamps, Ili\'c, Joudaki, Keil, Le Brun, Moretti, Pettorino, S\'anchez, Sakr, Tanidis, Tutusaus, Ajani, Crocce, Giocoli, Legrand, Lembo, Lesci, Girones, Nouri-Zonoz, Pamuk, Tsedrik, Bel, Carbone, Duncan, Kilbinger, Lacasa, Lattan…

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 4: Validation and Performance
The Euclid satellite will provide data on the clustering of galaxies and on the distortion of their measured shapes, which can be used to constrain and test the cosmological model. However, the increase in precision places strong requirements on the accuracy of the theoretical modelling for the observables and of the full analysis pipeline. In this paper, we investigate the accuracy of the calculations performed by the Cosmology Likelihood for Observables in Euclid (CLOE), a software able to ha…

It feels to me like that’s Mo’s last game. The way players were trying to feed him for the fairytale ending?
His vision is still elite, but his speed is gone (Dunk beat him in a dead sprint from that lovely Mac Allister ball into space) and his passing and shooting accuracy have diminished as well.
I will always love what he did for #LFC, his class (gracefully weighing his miscalculation la…

Replaced article(s) found for math.PR. https://arxiv.org/list/math.PR/new
[1/1]:
- Accuracy criterion for mean field approximations of Markov processes on hypergraphs
Illes Horvath, Daniel Keliger

Just suspended/defederated the zhub.link domain that appears to be a white supremacist/hate speech instance whose admin is going around harassing Palestinians and allies on the fediverse and is/was apparently also hosting an account disseminating CSAM (https://hear-me.social/@admin/11266673170955…

Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
Qingwen Li, Xiaohang Zhao, Xiao Han, Hailiang Huang, Lanjuan Liu
https://arxiv.org/abs/2510.11745 ht…

Think as a Doctor: An Interpretable AI Approach for ICU Mortality Prediction
Intensive Care Unit (ICU) mortality prediction, which estimates a patient's mortality status at discharge using EHRs collected early in an ICU admission, is vital in critical care. For this task, predictive accuracy alone is insufficient; interpretability is equally essential for building clinical trust and meeting regulatory standards, a topic that has attracted significant attention in information system research. Accordingly, an ideal solution should enable intrinsic interpretability and ali…

Chinese ModernBERT with Whole-Word Masking
Zeyu Zhao, Ningtao Wang, Xing Fu, Yu Cheng
https://arxiv.org/abs/2510.12285 https://arxiv.org/pdf/2510.12285

Chinese ModernBERT with Whole-Word Masking
Encoder-only Transformers have advanced along three axes -- architecture, data, and systems -- yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvements have not fully transferred to Chinese, where tokenization and morphology differ markedly from English. We introduce Chinese ModernBERT, a from-scratch Chinese encoder that couples: (i) a hardware-aware 32k BPE vocabulary tailored to frequent Chinese affixes/compounds, lowering the embedding budget; (ii) whole-word …

GS-Verse: Mesh-based Gaussian Splatting for Physics-aware Interaction in Virtual Reality
Anastasiya Pechko, Piotr Borycki, Joanna Waczy\'nska, Daniel Barczyk, Agata Szyma\'nska, S{\l}awomir Tadeja, Przemys{\l}aw Spurek
https://arxiv.org/abs/2510.11878

GS-Verse: Mesh-based Gaussian Splatting for Physics-aware Interaction in Virtual Reality
As the demand for immersive 3D content grows, the need for intuitive and efficient interaction methods becomes paramount. Current techniques for physically manipulating 3D content within Virtual Reality (VR) often face significant limitations, including reliance on engineering-intensive processes and simplified geometric representations, such as tetrahedral cages, which can compromise visual fidelity and physical accuracy. In this paper, we introduce \our{} (\textbf{G}aussian \textbf{S}platting…

Surprisingly useful device for #WiFi debugging: robot vacuum.
Roomba, Dreame (and probably others) make a map of WiFi signal strength.

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt
https://arxiv.org/abs/2510.10718

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies -- Mean Spatial-Lag Autocorrelation and Spa…

Iterative Data Curation with Theoretical Guarantees
V\"ain\"o Yrj\"an\"ainen Johan Jonasson, M{\aa}ns Magnusson
https://arxiv.org/abs/2510.11428 https://

Iterative Data Curation with Theoretical Guarantees
In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-quality research, downstream applications, and model training. This results in the problem of how to curate or improve data accuracy in such large and growing data, especially when the data is too large for manual curation to be feasible. This paper presents a unified procedure for iterative and continuous improvement …

Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs
Jo\~ao Paulo Cardoso de Lima, Marc Dietrich, Jeronimo Castrillon, Asif Ali Khan
https://arxiv.org/abs/2510.11192 h…

Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs
Structured sparsity enables deploying large language models (LLMs) on resource-constrained systems. Approaches like dense-to-sparse fine-tuning are particularly compelling, achieving remarkable structured sparsity by reducing the model size by over 6.7x, while still maintaining acceptable accuracy. Despite this reduction, LLM inference, especially the decode stage being inherently memory-bound, is extremely expensive on conventional Von-Neumann architectures. Compute-in-memory (CIM) architectur…

Digital adiabatic evolution is universally accurate
Yangyu Lu, Yifei Huang, Dong An, Qi Zhao, Dingshun Lv, Xiao Yuan
https://arxiv.org/abs/2510.12237 https://

Digital adiabatic evolution is universally accurate
Adiabatic evolution is a central paradigm in quantum physics. Digital simulations of adiabatic processes are generally viewed as costly, since algorithmic errors typically accumulate over the long evolution time, requiring exceptionally deep circuits to maintain accuracy. This work demonstrates that digital adiabatic evolution is intrinsically accurate and robust to simulation errors. We analyze two Hamiltonian simulation methods -- Trotterization and generalized quantum signal processing -- an…

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
Nisith Dissanayake (University of Moratuwa), Uthayasanker Thayasivam (University of Moratuwa)
https://arxiv.org/abs/2510.12455

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems (IDS) often struggle to maintain high accuracy across both frequent and rare attacks, leading to increased false negatives for minority classes. To address this, we propose a hybrid anomaly detection framework that integrates specialized deep learning models with an ensemble meta-c…

Wikipedia’s rejoinder to Ted Cruz is entirely fair and accurate, but don’t kid yourself that Cruz is interested in fairness or accuracy, he’s just pushing back against the tendency of factual reporting to exhibit a liberal bias.
#wikipedia

Ted Cruz doesn’t seem to understand Wikipedia, lawyer for Wikimedia says
Wikipedia host’s lawyer wants to help Ted Cruz understand how the platform works.

Faver: Boosting LLM-based RTL Generation with Function Abstracted Verifiable Middleware
Jianan Mu, Mingyu Shi, Yining Wang, Tianmeng Yang, Bin Sun, Xing Hu, Jing Ye, Huawei Li
https://arxiv.org/abs/2510.08664

Faver: Boosting LLM-based RTL Generation with Function Abstracted Verifiable Middleware
LLM-based RTL generation is an interesting research direction, as it holds the potential to liberate the least automated stage in the current chip design. However, due to the substantial semantic gap between high-level specifications and RTL, coupled with limited training data, existing models struggle with generation accuracy. Drawing on human experience, design with verification helps improving accuracy. However, as the RTL testbench data are even more scarce, it is not friendly for LLMs. Alt…

Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception
Muxing Huang, Zibin Chen, Weiliang Xu, Zilan Li, Yuanzhi Zhou, Guoyuan Zhou, Wenjing Chen, Xinming Li
https://arxiv.org/abs/2510.12528

Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception
Tactile perception is crucial for embodied intelligent robots to recognize objects. Vision-based tactile sensors extract object physical attributes multidimensionally using high spatial resolution; however, this process generates abundant redundant information. Furthermore, single-dimensional extraction, lacking effective fusion, fails to fully characterize object attributes. These challenges hinder the improvement of recognition accuracy. To address this issue, this study introduces a two-stre…

MIP-Based Tumor Segmentation: A Radiologist-Inspired Approach
Romario Zarik, Nahum Kiryati, Michael Green, Liran Domachevsky, Arnaldo Mayer
https://arxiv.org/abs/2510.09326 http…

MIP-Based Tumor Segmentation: A Radiologist-Inspired Approach
PET/CT imaging is the gold standard for tumor detection, offering high accuracy in identifying local and metastatic lesions. Radiologists often begin assessment with rotational Multi-Angle Maximum Intensity Projections (MIPs) from PET, confirming findings with volumetric slices. This workflow is time-consuming, especially in metastatic cases. Despite their clinical utility, MIPs are underutilized in automated tumor segmentation, where 3D volumetric data remains the norm. We propose an alternati…

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
Rong Jin, Guangyao Wang, Xingsheng Sun
https://arxiv.org/abs/2510.09703 https://

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
High-fidelity simulations are essential for understanding and predicting the behavior of materials under high-velocity impact (HVI) in both fundamental research and practical applications. However, their accuracy relies on material models and parameters that are traditionally obtained through manual fitting to multiple time- and labor-intensive experiments. This study presents an ensemble-based data assimilation (DA) framework to automatically and simultaneously calibrate plasticity, fracture, …

Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
Katarina Dyreby, Francisco Caldas, Cl\'audia Soares
https://arxiv.org/abs/2510.11242

Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts and the Low Earth Orbit (LEO) space environment. This study critically evaluates the accuracy and reliability of publicly available ephemeris data for a LEO mega-constellation - Starlink. The goal of this work is twofold: (i) compare and analyze the quality of the data against high-precision numerical propagation. (ii) …

A GPU-Accelerated Matrix-Free FAS Multigrid Solver for Navier-Stokes Equations with Memory-Efficient Implementations
Jiale Meng, Shuqi Tang, Steven M. Wise, Zhenlin Guo
https://arxiv.org/abs/2510.11152

A GPU-Accelerated Matrix-Free FAS Multigrid Solver for Navier-Stokes Equations with Memory-Efficient Implementations
We develop a matrix-free Full Approximation Storage (FAS) multigrid solver based on staggered finite differences and implemented on GPU in MATLAB. To enhance performance, intermediate variables are reused, and an X-shape Multi-Color Gauss-Seidel (X-MCGS) smoother is introduced, which eliminates conditional branching by partitioning the grid into four submatrices. Restriction and prolongation operators are also GPU-accelerated. Convergence tests verify robustness and accuracy, while benchmarks s…

I am enthusiastic about GenAI , it's possible applications and added value in specific usescases, but please let's not start using CoPilot in Excel with these accuracy numbers...😆 Imagine that workers would actually start using this and not checking their work. It could destroy productivity...

GPT-5 Model Corrected GPT-4V's Chart Reading Errors, Not Prompting
Kaichun Yang, Jian Chen
https://arxiv.org/abs/2510.06782 https://arxiv.org/pdf/2510.…

GPT-5 Model Corrected GPT-4V's Chart Reading Errors, Not Prompting
We present a quantitative evaluation to understand the effect of zero-shot large-language model (LLMs) and prompting uses on chart reading tasks. We asked LLMs to answer 107 visualization questions to compare inference accuracies between the agentic GPT-5 and multimodal GPT-4V, for difficult image instances, where GPT-4V failed to produce correct answers. Our results show that model architecture dominates the inference accuracy: GPT5 largely improved accuracy, while prompt variants yielded only…

Leveraging Cellular Automata for Real-Time Wildfire Spread Modeling in California
Connor Weinhouse, Jameson Augustin
https://arxiv.org/abs/2510.09708 https://

Leveraging Cellular Automata for Real-Time Wildfire Spread Modeling in California
Wildfires are becoming increasingly frequent and devastating, and therefore the technology to combat them must adapt accordingly. Modern predictive models have failed to balance predictive accuracy and operational viability, resulting in consistently delayed or misinformed fire suppression and public safety efforts. The present study addresses this gap by developing and validating a predictive model based on cellular automata (CA) that incorporates key environmental variables, including vegetat…

Analytical and numerical investigation of heat transfer of porous fin in a local thermal non-equilibrium state
Payam Jalili, Salar Ghadiri Alamdari, Bahram Jalili, Amirali Shateri, Davood Domiri Ganji
https://arxiv.org/abs/2510.11157

Analytical and numerical investigation of heat transfer of porous fin in a local thermal non-equilibrium state
This research employs a local thermal non-equilibrium (LTNE) model to analyze the heat transfer phenomenon through a porous fin, considering natural convection and radiation effects. The infiltration velocity within the porous medium is evaluated using the Darcy model, and buoyancy effects are accounted for using the Boussinesq approximation. The Akbari-Ganji method (AGM) is applied to address the governing energy equations. The accuracy of the proposed solution is verified by comparing it with…

Operator-Consistent Physics-Informed Learning for Wafer Thermal Reconstruction in Lithography
Ze Tao, Yuxi Jin, Ke Xu, Haoran Xu, Hanxuan Wang, Fujun Liu
https://arxiv.org/abs/2510.09207

Operator-Consistent Physics-Informed Learning for Wafer Thermal Reconstruction in Lithography
Thermal field reconstruction in post-exposure bake (PEB) is critical for advanced lithography, but current methods lack consistent accuracy and physical alignment. To bridge this gap, we introduce a physics-informed neural model that unifies coordinates, fields, and differential operators on a single computation graph using gated liquid layers. Applied to a 2D PEB scenario with internal heat generation and convective boundaries, the model formulates residuals via differential forms and a loss f…

The study involved leading laboratories across multiple countries testing identical samples of gut microbiome bacteria. Results revealed startling inconsistencies, with accuracy measures varying dramatically between laboratories – despite analysing the same samples.
MHRA-led study reveals major inconsistencies in global microbiome research

MHRA-led study reveals major inconsistencies in global microbiome research
International collaboration establishes new quality standards to improve reliability of gut health studies – improving accuracy to provide better diagnosis and treatment.

The age and metallicity dependence of the near-infrared absolute magnitude and colour of red clump stars
Hiroki Onozato, Yoshifusa Ita, Yoshikazu Nakada
https://arxiv.org/abs/2510.09168

The age and metallicity dependence of the near-infrared absolute magnitude and colour of red clump stars
Understanding the age and metallicity dependence of the absolute magnitude and colour of red clump (RC) stars is crucial for validating the accuracy of stellar evolution models and enhancing their reliability as a standard candle. However, this dependence has previously been investigated in the near-infrared across multiple bands only for -1.05 $\leq$ [Fe/H] $\leq$ 0.40, a range accessible through the star clusters in the Large Magellanic Cloud. Therefore, we used star clusters in the Small Mag…

Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
Mingqian Guan, Komei Fujita, Naoya Sueishi, Shota Yasui
https://arxiv.org/abs/2510.10527

Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identification; however, IPW-transformed outcomes are known to be noisy, even when true propensity scores are used. To address this issue, we introduce a noise reduction procedure and estimate a linear CATE model using Lasso, achieving both accuracy and interpretability. We theoretically show that denoising reduces the predictio…

ACE-G: Improving Generalization of Scene Coordinate Regression Through Query Pre-Training
Leonard Bruns, Axel Barroso-Laguna, Tommaso Cavallari, \'Aron Monszpart, Sowmya Munukutla, Victor Adrian Prisacariu, Eric Brachmann
https://arxiv.org/abs/2510.11605

ACE-G: Improving Generalization of Scene Coordinate Regression Through Query Pre-Training
Scene coordinate regression (SCR) has established itself as a promising learning-based approach to visual relocalization. After mere minutes of scene-specific training, SCR models estimate camera poses of query images with high accuracy. Still, SCR methods fall short of the generalization capabilities of more classical feature-matching approaches. When imaging conditions of query images, such as lighting or viewpoint, are too different from the training views, SCR models fail. Failing to genera…

Quasinormal modes from numerical relativity with Bayesian inference
Richard Dyer, Christopher J. Moore
https://arxiv.org/abs/2510.11783 https://arxiv.org/p…

Quasinormal modes from numerical relativity with Bayesian inference
Numerical relativity (NR) enables the study of physics in strong and dynamical gravitational fields and provides predictions for the gravitational-wave signals produced by merging black holes. Despite the impressive accuracy of modern codes, the resulting waveforms inevitably contain numerical uncertainties. Quantifying these uncertainties is important, especially for studies probing subdominant or nonlinear effects around the merger and ringdown. This paper describes a flexible Gaussian-proces…

HOPPET v2.0.0 release note
Alexander Karlberg, Paolo Nason, Gavin Salam, Giulia Zanderighi, Fr\'ed\'eric Dreyer
https://arxiv.org/abs/2510.09310 https://

HOPPET v2.0.0 release note
We document the three main new features in release 2.0.0 of the HOPPET parton distribution function evolution code, specifically support for N$^3$LO QCD evolution in the variable flavour number scheme, for the determination of hadronic structure functions for massless quarks up to N$^3$LO, and for QED evolution to an accuracy phenomenologically equivalent to NNLO QCD. Additionally we describe a new Python interface, CMake build option, functionality to save a hoppet table as an LHAPDF grid and …

QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
Adil Kabylda, Sergio Su\'arez-Dou, Nils Davoine, Florian N. Br\"unig, Alexandre Tkatchenko
https://arxiv.org/abs/2510.09939

QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computational efficiency of mechanistic potentials. However, the development of reliable MLFFs for biomolecular systems remains constrained by the scarcity of high-quality, chemically diverse QM datasets that span all of the major classes of biomolecules expressed in living cells. Crucially, such a comprehensive dataset must be c…

Scalable accuracy gains from postselection in quantum error correcting codes
Hongkun Chen, Daohong Xu, Grace M. Sommers, David A. Huse, Jeff D. Thompson, Sarang Gopalakrishnan
https://arxiv.org/abs/2510.05222

Scalable accuracy gains from postselection in quantum error correcting codes
Decoding stabilizer codes such as the surface and toric codes involves evaluating free-energy differences in a disordered statistical mechanics model, in which the randomness comes from the observed pattern of error syndromes. We study the statistical distribution of logical failure rates across observed syndromes in the toric code, and show that, within the coding phase, logical failures are predominantly caused by exponentially unlikely syndromes. Therefore, postselecting on not seeing these …

Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
David Debot, Giuseppe Marra
https://arxiv.org/abs/2510.05670 https://

Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
Concept Bottleneck Models (CBNMs) are deep learning models that provide interpretability by enforcing a bottleneck layer where predictions are based exclusively on human-understandable concepts. However, this constraint also restricts information flow and often results in reduced predictive accuracy. Concept Sidechannel Models (CSMs) address this limitation by introducing a sidechannel that bypasses the bottleneck and carry additional task-relevant information. While this improves accuracy, it …

The effect of magnetic fields on vertex reconstructed muon-spin spectroscopy
Pascal Isenring, Zaher Salman
https://arxiv.org/abs/2510.10094 https://arxiv.o…

The effect of magnetic fields on vertex reconstructed muon-spin spectroscopy
The use of a Si pixel-based particle tracking scheme in muSR will, among others, allow measurements using a ten-fold increased stopped muons rate and samples ten times smaller than currently possible. Here we present simulation results to assess the effects of magnetic fields on two spectrometer configurations using a two-layered tracking scheme for the incoming and outgoing particles. At a low magnetic field of up to ~50 mT, the tracking and reconstruction accuracy is only minimally influenced…

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
Amir Bahador Javadi, Philip Pong
https://arxiv.org/abs/2510.09411 https://

Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Using sparse identification of nonlinear dynamics and deep symbolic regression, models were generated from synthetic data simulating key disturbances in active power, reactive power, and voltage references. Deep symbolic regression demonstrated more accuracy in capturing complex system dynamics, though it required subst…

Dr.LLM: Dynamic Layer Routing in LLMs
Ahmed Heakl, Martin Gubri, Salman Khan, Sangdoo Yun, Seong Joon Oh
https://arxiv.org/abs/2510.12773 https://arxiv.org…

Dr.LLM: Dynamic Layer Routing in LLMs
Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and insufficient flexibility for harder ones that need deeper reasoning. Adaptive-depth methods can improve efficiency, but prior approaches rely on costly inference-time search, architectural changes, or large-scale retraining, and in practice often degrade accuracy despite efficiency gains. We introduce Dr.LLM, Dynamic routing of Layers for LLMs, a retrofitt…

New nickle release today. This adds complex numbers, adds more math functions and fixes some existing ones. I'm using all of this to generate test vectors for picolibc math functions. More news about that when I manage to get the tests through CI.
Here's the nickle bits:
https://github.com/keith-…

Fertility startup Inito, which offers an at-home health diagnostics platform, raised a $29M Series B and plans to use AI-designed antibodies to make new tests (Aisha Malik/TechCrunch)
https://techcrunch.com/2025/12/10/fert

Fertility startup Inito wants to use AI-designed antibodies to expand at-home health tests | TechCrunch
Inito has raised $29 million in new funding to invest in and develop AI-engineered antibodies that will help it offer new types of tests and improve the accuracy of existing ones.

Opinions can be Incorrect! In our Opinion. On the accuracy principle in data protection law
Dara Hallinan, Frederik Zuiderveen Borgesius
https://arxiv.org/abs/2509.23848 https:/…

Opinions can be Incorrect! In our Opinion. On the accuracy principle in data protection law
The GDPR contains an accuracy principle, as most data privacy laws in the world do. In principle, data controllers must ensure that personal data they use are accurate. Some have argued that the accuracy principle does not apply to personal data in the form of opinions about data subjects. We argue, however, from a positive law perspective, that the accuracy principle does apply to opinions. We further argue, from a normative perspective, that the accuracy principle should apply to opinions.

A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges
Yuwen Cui, Guangjing Wang, Khanh Vu, Kai Wei, Kehan Shen, Zhengyuan Jiang, Xiao Han, Ning Wang, Zhuo Lu, Yao Liu
https://arxiv.org/abs/2510.11804

A Comprehensive Survey of Website Fingerprinting Attacks and Defenses in Tor: Advances and Open Challenges
The Tor network provides users with strong anonymity by routing their internet traffic through multiple relays. While Tor encrypts traffic and hides IP addresses, it remains vulnerable to traffic analysis attacks such as the website fingerprinting (WF) attack, achieving increasingly high fingerprinting accuracy even under open-world conditions. In response, researchers have proposed a variety of defenses, ranging from adaptive padding, traffic regularization, and traffic morphing to adversarial…

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Samuel Hildebrand (Louisiana State University), Curtis Taylor (Oak Ridge National Lab), Sean Oesch (Oak Ridge National Lab), James M Ghawaly Jr (Louisiana State University), Amir Sadovnik (Oak Ridge National Lab), Ryan Shivers (Oak Ridge National Lab), Brandon Schreiber (Oak Ridge National Lab), Kevin Kurian (University of Florida)

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evaluate RAG pipelines as a whole, evaluating a pipeline's ability to ingest, retrieve, and reason about several modalities of information, differentiating it from existing benchmarks that focus on particular aspects such as retrieval. We present (1) a small, human-created dataset of 93 questions designed to evaluate a pipe…

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Qiya Yang, Xiaoxi Liang, Zeping Xiao, Yingjie Deng, Yalong Wang, Yongqi Liu, Han Li
https://arxiv.org/abs/2510.10127

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Reranking, as the final stage of recommender systems, demands real-time inference, accuracy, and diversity. It plays a crucial role in determining the final exposure, directly influencing user experience. Recently, generative reranking has gained increasing attention for its strong ability to model complex dependencies among items. However, most existing methods suffer from the "likelihood trap", where high-likelihood sequences are often perceived as low-quality by humans. These models tend to …

Proprioceptive Misestimation of Hand Speed
Caitlin Callaghan, David J Reinkensmeyer
https://arxiv.org/abs/2510.11664 https://arxiv.org/pdf/2510.11664

Proprioceptive Misestimation of Hand Speed
The accuracy with which the human proprioceptive system estimates hand speed is not well understood. To investigate this, we designed an experiment using hobby-grade mechatronics parts and integrated it as a laboratory exercise in a large remote laboratory course. In a simple joint position reproduction task, participants (N = 191) grasped a servomotor-driven shaft with one hand as it followed a randomized trajectory composed of sinusoidal submovements. They simultaneously attempted to reproduc…

Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
Kuan-Yi Lee, Tsung-En Lin, Hung-Yi Lee
https://arxiv.org/abs/2510.11454 https://

Audio-Maestro: Enhancing Large Audio-Language Models with Tool-Augmented Reasoning
Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solely on end-to-end reasoning, limiting interpretability and accuracy for tasks that require structured knowledge or specialized signal analysis. In this work, we present Audio-Maestro -- a tool-augmented audio reasoning framework that enables audio-language models to autonomously call external tools and integrate their timestamped outputs into the reasoning p…

Robust Functional Logistic Regression
Berkay Akturk, Ufuk Beyaztas, Han Lin Shang
https://arxiv.org/abs/2510.12048 https://arxiv.org/pdf/2510.12048

Robust Functional Logistic Regression
Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robust estimation procedure for functional logistic regression, in which the observations of the functional predictor are projected onto a set …

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Farzaneh Zirak, Farhana Choudhury, Renata Borovica-Gajic
https://arxiv.org/abs/2510.11011

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Data prefetching--loading data into the cache before it is requested--is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning-based approaches, especially those leveraging data semantics, achieve higher accuracy for complex access patterns. However, these methods often struggle with today's dynamic, ever-growing datasets and require frequent, timely fine-tuning. Privacy constraints may also restrict a…

Spectral Graph Clustering under Differential Privacy: Balancing Privacy, Accuracy, and Efficiency
Mohamed Seif, Antti Koskela, H. Vincent Poor, Andrea J. Goldsmith
https://arxiv.org/abs/2510.07136

Spectral Graph Clustering under Differential Privacy: Balancing Privacy, Accuracy, and Efficiency
We study the problem of spectral graph clustering under edge differential privacy (DP). Specifically, we develop three mechanisms: (i) graph perturbation via randomized edge flipping combined with adjacency matrix shuffling, which enforces edge privacy while preserving key spectral properties of the graph. Importantly, shuffling considerably amplifies the guarantees: whereas flipping edges with a fixed probability alone provides only a constant epsilon edge DP guarantee as the number of nodes g…

94.1% accuracy is definitely the exception to the rule for me, but the moves looked clear and obvious. I had wondered about whether patience against the pinned queen was accurate but reasoned it had to be.
Opponent allowing the pin on the queen was their undoing, obviously, but they still played with 82.5% accuracy. In most of my games, I'd be delighted to score that high.
#chess

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

Calibrated Dynamic Modeling for Force and Payload Estimation in Hydraulic Machinery
Lennart Werner, Pol Eyschen, Sean Costello, Pierluigi Micarelli, Marco Hutter
https://arxiv.org/abs/2510.11574

Calibrated Dynamic Modeling for Force and Payload Estimation in Hydraulic Machinery
Accurate real-time estimation of end effector interaction forces in hydraulic excavators is a key enabler for advanced automation in heavy machinery. Accurate knowledge of these forces allows improved, precise grading and digging maneuvers. To address these challenges, we introduce a high-accuracy, retrofittable 2D force- and payload estimation algorithm that does not impose additional requirements on the operator regarding trajectory, acceleration or the use of the slew joint. The approach is …

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Bowen Du, Qi Li, Mingwei Ge, Xintao Li, Yongqian Liu
https://arxiv.org/abs/2510.11392

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Wind farm parameterizations are crucial for quantifying the wind-farm atmosphere interaction, where wind turbines are typically modeled as elevated momentum sinks and sources of turbulence kinetic energy (TKE). These quantities must be properly distributed to the mesoscale grid. Existing parameterizations use the single-column method. However, this method can easily lead to the errors of the spatial distribution of the sink and source, thereby impacting the accuracy of mesoscale flow simulation…

Refining open cluster parameters with Gaia XP metallicities
M. Nizovkina, S. S. Larsen, A. G. A. Brown, A. Helmi
https://arxiv.org/abs/2510.10385 https://a…

Refining open cluster parameters with Gaia XP metallicities
The precision of cluster parameter determination has significantly improved with the availability of homogeneous photometric Gaia data, however, challenges such as age-metallicity degeneracy and lack of spectroscopic observations remain. In this paper we investigate whether metallicities derived from low-resolution Gaia XP spectra can be effectively used to break degeneracies and improve the accuracy of OC parameter determinations. We analysed 20 OCs using isochrone fitting methods on Gaia DR…

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method
Yuwei Geng, Junqi Yin, Eric C. Cyr, Guannan Zhang, Lili Ju
https://arxiv.org/abs/2510.07672

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method
While recent advances in deep learning have shown promising efficiency gains in solving time- dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time- dependent PDEs is to use the learned solution as the coarse propagator in the Parareal method and a traditional numerical method as the fine solver. However, successful integration of deep lear…

Hallucination Filtering in Radiology Vision-Language Models Using Discrete Semantic Entropy
Patrick Wienholt, Sophie Caselitz, Robert Siepmann, Philipp Bruners, Keno Bressem, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
https://arxiv.org/abs/2510.09256

Hallucination Filtering in Radiology Vision-Language Models Using Discrete Semantic Entropy
To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs) in radiologic image based visual question answering (VQA). This retrospective study evaluated DSE using two publicly available, de-identified datasets: (i) the VQA-Med 2019 benchmark (500 images with clinical questions and short-text answers) and (ii) a diagnostic radiology dataset (206 cases: 60 computed tomography …

Cosmology Likelihood for Observables in \Euclid (CLOE). 1. Theoretical recipe
Collaboration, Cardone, Joudaki, Blot, Bonici, Camera, Ca\~nas-Herrera, Carrilho, Casas, Davini, Di Domizio, Farrens, Goh, Beauchamps, Ili\'c, Keil, Le Brun, Martinelli, Moretti, Pettorino, Pezzotta, S\'anchez, Sakr, Sciotti, Tanidis, Tutusaus, Ajani, Crocce, Giocoli, Legrand, Lembo, Lesci, Girones, Nouri-Zonoz, Pamuk, Tsedrik, Bel, Carbone, Duncan, Kilbinger, Lacasa, Lattanzi, Sapone, Sellentin, Tayl…

Cosmology Likelihood for Observables in \Euclid (CLOE). 1. Theoretical recipe
As the statistical precision of cosmological measurements increases, the accuracy of the theoretical description of these measurements needs to increase correspondingly in order to infer the underlying cosmology that governs the Universe. To this end, we have created the Cosmology Likelihood for Observables in Euclid (CLOE), which is a novel cosmological parameter inference pipeline developed within the Euclid Consortium to translate measurements and covariances into cosmological parameter cons…

Cocoon: A System Architecture for Differentially Private Training with Correlated Noises
Donghwan Kim, Xin Gu, Jinho Baek, Timothy Lo, Younghoon Min, Kwangsik Shin, Jongryool Kim, Jongse Park, Kiwan Maeng
https://arxiv.org/abs/2510.07304

Cocoon: A System Architecture for Differentially Private Training with Correlated Noises
Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differential privacy (DP), such as DP-SGD, have been gaining attention as a solution. However, DP-SGD adds a noise at each training iteration, which degrades the accuracy of the trained model. To improve accuracy, a new family of approaches adds carefully designed correlated noises, so that noises cancel out each other across iterations. We performed an extensive…

GroundGazer: Camera-based indoor localization of mobile robots with millimeter accuracy at low cost
Sven Hinderer, Jakob H\"usken, Bohan Sun, Bin Yang
https://arxiv.org/abs/2509.17346

GroundGazer: Camera-based indoor localization of mobile robots with millimeter accuracy at low cost
Highly accurate indoor localization systems with mm positioning accuracy are currently very expensive. They include range finders (such as LiDAR), tachymeters, and motion capture systems relying on multiple high-end cameras. In this work, we introduce a high-accuracy, planar indoor localization system named GroundGazer (GG) for autonomous mobile robots (AMRs). GG estimates the AMR's position with mm and its heading with sub-degree accuracy. The system requires only a monocular (fisheye) camera,…

Chronologically Consistent Generative AI
Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
https://arxiv.org/abs/2510.11677 https://arxiv.org/pdf/2510.11677

Chronologically Consistent Generative AI
We introduce a family of chronologically consistent, instruction-following large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the s…

An inexact semismooth Newton-Krylov method for semilinear elliptic optimal control problem
Shiqi Chen, Xuesong Chen
https://arxiv.org/abs/2511.10058 https://arxiv.org/pdf/2511.10058 https://arxiv.org/html/2511.10058
arXiv:2511.10058v1 Announce Type: new
Abstract: An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem is reformulated into a nonlinear equation through variational inequality principles and discretized using a second-order finite difference scheme. By leveraging slanting differentiability, the algorithm constructs semismooth Newton directions and employs GMRES method to inexactly solve the Newton equations, significantly reducing computational overhead. A dynamic nonmonotonic line search strategy is introduced to adjust stepsizes adaptively, ensuring global convergence while overcoming local stagnation. Theoretical analysis demonstrates that the algorithm achieves superlinear convergence near optimal solutions when the residual control parameter $\eta_k$ approaches to 0. Numerical experiments validate the method's accuracy and efficiency in solving semilinear elliptic optimal control problems, corroborating theoretical insights.
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Towards Long-Term User Welfare in Recommender Systems via Creator-Oriented Information Revelation
Xu Zhao, Xiaopeng Ye, Chen Xu, Weiran Shen, Jun Xu
https://arxiv.org/abs/2510.10511

Towards Long-Term User Welfare in Recommender Systems via Creator-Oriented Information Revelation
Improving the long-term user welfare (e.g., sustained user engagement) has become a central objective of recommender systems (RS). In real-world platforms, the creation behaviors of content creators plays a crucial role in shaping long-term welfare beyond short-term recommendation accuracy, making the effective steering of creator behavior essential to foster a healthier RS ecosystem. Existing works typically rely on re-ranking algorithms that heuristically adjust item exposure to steer creator…

Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
Om Dobariya, Akhil Kumar
https://arxiv.org/abs/2510.04950 https://ar…

Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
The wording of natural language prompts has been shown to influence the performance of large language models (LLMs), yet the role of politeness and tone remains underexplored. In this study, we investigate how varying levels of prompt politeness affect model accuracy on multiple-choice questions. We created a dataset of 50 base questions spanning mathematics, science, and history, each rewritten into five tone variants: Very Polite, Polite, Neutral, Rude, and Very Rude, yielding 250 unique prom…

RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution
Aofan Liu, Haoxuan Li, Bin Wang, Ao Yang, Hui Li
https://arxiv.org/abs/2510.08665 htt…

RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution
Code generation models based on large language models (LLMs) have gained wide adoption, but challenges remain in ensuring safety, accuracy, and controllability, especially for complex tasks. Existing methods often lack dynamic integration of external tools, transparent reasoning, and user control over safety. To address these issues, we propose a controllable code generation framework utilizing the ReAct paradigm for multi-agent task execution. This framework is a multi-agent system designed to…

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…

Caltech says it built the world's largest neutral-atom quantum computer, with 6,100 qubits, 13 second coherence, 10x longer than the past, and 99.98% accuracy (Jason Nelson/Decrypt)
https://decrypt.co/341716/caltech-builds-worlds-largest-neutral-atom…

Caltech Builds World’s Largest Neutral-Atom Quantum Computer
Researchers kept more than 6,000 qubits coherent while operating with 99.98% accuracy. Here's why that matters for quantum computing.

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly, Jean-Emmanuel Deschaud
https://arxiv.org/abs/2510.06876

HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexity of neighbor searching and 3D convolutions. Projection-based methods are faster but lose critical geometric information during the 2D pro…

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins, Zhaoming Xie
https://arxiv.org/abs/2510.08754

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and pred…

COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Gaoxiang Luo, Aryan Deshwal
https://arxiv.org/abs/2510.01178 https://…

COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Selecting an optimal set of exemplars is critical for good performance of in-context learning. However, prior exemplar search methods narrowly optimize for predictive accuracy, critically neglecting model calibration--a key determinant of trustworthiness and safe deployment. In this paper, we formulate exemplar selection as a multi-objective optimization problem, explicitly targeting both the maximization of predictive accuracy and the minimization of expected calibration error. We solve this p…

Accuracy-First R\'enyi Differential Privacy and Post-Processing Immunity
Ossi R\"ais\"a, Antti Koskela, Antti Honkela
https://arxiv.org/abs/2509.22213 https://

Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
The accuracy-first perspective of differential privacy addresses an important shortcoming by allowing a data analyst to adaptively adjust the quantitative privacy bound instead of sticking to a predetermined bound. Existing works on the accuracy-first perspective have neglected an important property of differential privacy known as post-processing immunity, which ensures that an adversary is not able to weaken the privacy guarantee by post-processing. We address this gap by determining which ex…

HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Yipu Zhang, Chaofang Ma, Jinming Ge, Lin Jiang, Jiang Xu, Wei Zhang
https://arxiv.org/abs/2510.09010

HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Neural Radiance Field (NeRF) has emerged as a promising 3D reconstruction method, delivering high-quality results for AR/VR applications. While quantization methods and hardware accelerators have been proposed to enhance NeRF's computational efficiency, existing approaches face crucial limitations. Current quantization methods operate without considering hardware architecture, resulting in sub-optimal solutions within the vast design space encompassing accuracy, latency, and model size. Additio…

Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
Giacomo Dimarco, Federica Ferrarese, Lorenzo Pareschi
https://arxiv.org/abs/2510.09192

Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
In this work, we propose a data augmentation strategy aimed at improving the training phase of neural networks and, consequently, the accuracy of their predictions. Our approach relies on generating synthetic data through a suitable compartmental model combined with the incorporation of uncertainty. The available data are then used to calibrate the model, which is further integrated with deep learning techniques to produce additional synthetic data for training. The results show that neural net…

S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
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CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts
Jiuheng Lin, Cong Jiang, Zirui Wu, Jiarui Sun, Yansong Feng
https://arxiv.org/abs/2510.09278 https://

CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts
Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs). However, standard outcome-based reinforcement learning (RL) on MCQs is risky. While it may improve accuracy, we observe it often degrades reasoning quality such as logical consistency. Existing solutions to supervise reasoning, such as large-scale Process Reward Models (PRMs), are prohibitively expensive. To address this, we propose CLARity, a cost-effective RL framework that enhanc…

ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
Francesco Maria Molfese, Luca Moroni, Ciro Porcaro, Simone Conia, Roberto Navigli
https://arxiv.org/abs/2510.09351

ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
While Small Language Models (SLMs) have demonstrated promising performance on an increasingly wide array of commonsense reasoning benchmarks, current evaluation practices rely almost exclusively on the accuracy of their final answers, neglecting the validity of the reasoning processes that lead to those answers. To address this issue, we introduce ReTraceQA, a novel benchmark that introduces process-level evaluation for commonsense reasoning tasks. Our expert-annotated dataset reveals that in a…

A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages
Raoyuan Zhao, Yihong Liu, Hinrich Sch\"utze, Michael A. Hedderich
https://arxiv.org/abs/2510.09555

A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages
Large reasoning models (LRMs) increasingly rely on step-by-step Chain-of-Thought (CoT) reasoning to improve task performance, particularly in high-resource languages such as English. While recent work has examined final-answer accuracy in multilingual settings, the thinking traces themselves, i.e., the intermediate steps that lead to the final answer, remain underexplored. In this paper, we present the first comprehensive study of multilingual CoT reasoning, evaluating three key dimensions: per…

Intra-request branch orchestration for efficient LLM reasoning
Weifan Jiang, Rana Shahout, Yilun Du, Michael Mitzenmacher, Minlan Yu
https://arxiv.org/abs/2509.24957 https://

Intra-request branch orchestration for efficient LLM reasoning
Large Language Models (LLMs) increasingly rely on inference-time reasoning algorithms such as chain-of-thought and multi-branch reasoning to improve accuracy on complex tasks. These methods, however, substantially increase token usage and per-request latency. Prior work has largely focused on reducing token usage, often at the expense of accuracy, while overlooking other latency factors. We present DUCHESS, an LLM serving system that reduces cost and latency without sacrificing accuracy through…

Slm-mux: Orchestrating small language models for reasoning
Chenyu Wang, Zishen Wan, Hao Kang, Emma Chen, Zhiqiang Xie, Tushar Krishna, Vijay Janapa Reddi, Yilun Du
https://arxiv.org/abs/2510.05077

Slm-mux: Orchestrating small language models for reasoning
With the rapid development of language models, the number of small language models (SLMs) has grown significantly. Although they do not achieve state-of-the-art accuracy, they are more efficient and often excel at specific tasks. This raises a natural question: can multiple SLMs be orchestrated into a system where each contributes effectively, achieving higher accuracy than any individual model? Existing orchestration methods have primarily targeted frontier models (e.g., GPT-4) and perform sub…