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They are breaking things of huge value to everyone.
Trump weather balloon cuts have Michigan meteorologists debating forecasts | Interlochen Public Radio https://www.interlochenpublicradio.org/2025-12-20/trump-weat…

Trump weather balloon cuts have Michigan meteorologists debating forecasts
Workforce cuts made by the Trump administration at the National Oceanic and Atmospheric Administration are prompting meteorologists to debate the reliability of weather predictions.

🚀 Enable via new Plugins section at https://openrouter.ai/settings/plugins - opt-in toggle activates automatic healing layer for all structured output requests
💡 Key insight: A 2% defect rate dropping to 1% means cutting defects, bugs, and support tickets in half - reliability at the margins is wh…

"users who leave their SSDs unpowered for over a year are risking the integrity of their data. The reliability of QLC NAND has improved over the years, so you should probably consider 2–3 years of unpowered usage as the guardrails. Without power, the voltage stored in the NAND cells can be lost, either resulting in missing data or completely useless drives."

The unpowered SSDs in your drawer are slowly losing your data
SSDs aren't ideal for long-term data storage

Massachusetts is about to see major savings from clean energy. A new analysis shows the state's SMART 3.0 solar-plus-storage program could save ratepayers $313 million annually by 2030.
The key? Pushing out inefficient natural gas plants, cutting reliance on fossil fuels during winter, and slashing 1.6 million metric tons of CO2 per year.

Solar and storage program forecast to save all Massachusetts ratepayers $313 million per year
Solar plus storage can also offer winter reliability improvements and limit gas consumption, finds a report from Synapse Energy Economics and the Solar Energy Industries Association.

[OT] Dilbert? Salesforce steps back from AI: Executives reveal overconfidence in LLMs, pivot to deterministic automation https://opentools.ai/news/salesforce-steps-back-from-ai-executives-reveal-overconfidence-in-ll…

What happens when you pair solar panels with mini nuclear reactors? Chinese researchers just cracked the code.
Their new microgrid framework combines photovoltaics with small modular reactors, using AI to balance both in real time. The results are striking: 18.7% lower costs, 37.1% fewer emissions, and 98% reliability.
The secret? Smart coordination between battery storage and hydrogen production that adapts on the fly.

When solar meets next-gen nuclear
Scientists in China have proposed a novel scheduling framework for microgrids based on hybrid PV and a small modular nuclear reactors. The framework uses multi-objective distributionally robust optimization with a real-time reinforcement learning mechanism and is reportedly able to reduce operational costs by 18.7%.

Sources: Resolve AI, which is developing an autonomous site reliability engineering tool, raised a Series A at multiple valuation tiers, including at $1B (Marina Temkin/TechCrunch)
https://techcrunch.com/2025/12/19/ex-splunk-execs-…

Ex-Splunk execs' startup Resolve AI hits $1 billion valuation with Series A | TechCrunch
The round was led by Lightspeed Venture Partners, according to people familiar with the deal.

from my link log —
A distributed systems reliability glossary.
https://antithesis.com/resources/reliability_glossary/
saved 2025-12-03 https://…

A distributed systems reliability glossary
A list of key concepts for building and testing reliable distributed systems, with basic definitions and deep references.

Wow, you *really* produce bad #quality if your #EV (with much less moving parts) is worse than all the non-electric #vehicles of a list. 😲
Avoid buying

Dispersion-Aware Modeling Framework for Parallel Optical Computing
Ziqi Wei, Yuanjian Wan, Yuhu Cheng, Xiao Yu, Peng Xie
https://arxiv.org/abs/2511.18897 https://arxiv.org/pdf/2511.18897 https://arxiv.org/html/2511.18897
arXiv:2511.18897v1 Announce Type: new
Abstract: Optical computing represents a groundbreaking technology that leverages the unique properties of photons, with innate parallelism standing as its most compelling advantage. Parallel optical computing like cascaded Mach-Zehnder interferometers (MZIs) based offers powerful computational capabilities but also introduces new challenges, particularly concerning dispersion due to the introduction of new frequencies. In this work, we extend existing theories of cascaded MZI systems to develop a generalized model tailored for wavelength-multiplexed parallel optical computing. Our comprehensive model incorporates component dispersion characteristics into a wavelength-dependent transfer matrix framework and is experimentally validated. We propose a computationally efficient compensation strategy that reduces global dispersion error within a 40 nm range from 0.22 to 0.039 using edge-spectrum calibration. This work establishes a fundamental framework for dispersion-aware model and error correction in MZI-based parallel optical computing chips, advancing the reliability of multi-wavelength photonic processors.
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Want a used car that works?
You’d be wise to not get an older Tesla.
In Consumer Reports’ latest ranking for used cars,
the Elon Musk-run automaker came dead last in terms of reliability,
trailing by over forty points from the top spot on a scale between 0 and 100
https://futurism.co…

Old Teslas Are Falling Apart
On Consumer Reports' latest ranking of used car reliability, Tesla came in dead last with a rating not even half of the top placed brand.

EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels
Kunyu Peng, Di Wen, Kailun Yang, Jia Fu, Yufan Chen, Ruiping Liu, Jiamin Wu, Junwei Zheng, M. Saquib Sarfraz, Luc Van Gool, Danda Pani Paudel, Rainer Stiefelhagen
https://arxiv.org/abs/2510.12687

EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels
Open-Set Domain Generalization (OSDG) aims to enable deep learning models to recognize unseen categories in new domains, which is crucial for real-world applications. Label noise hinders open-set domain generalization by corrupting source-domain knowledge, making it harder to recognize known classes and reject unseen ones. While existing methods address OSDG under Noisy Labels (OSDG-NL) using hyperbolic prototype-guided meta-learning, they struggle to bridge domain gaps, especially with limited…

These AWS & Cloudflare mega-outages are honestly embarrassing as an industry. Eugh. What are we doing???
We have so many tools & processes for ensuring reliability, but somehow two vendors can each single-handledly wipe everything out anytime.

Critical States Identiffcation in Power System via Lattice Partition and Its Application in Reliability Assessment
Han Hu, Wenjie Wan, Feiyu Chen, Xiaoyu Liu, Bo Yu, Kequan Zhao
https://arxiv.org/abs/2510.09420

Critical States Identiffcation in Power System via Lattice Partition and Its Application in Reliability Assessment
With the increasing complexity of power systems,accurately identifying critical states (the states corresponding to minimal cut sets) and assessing system reliability have become crucial tasks. In this paper, a mathematical lattice structure is employed to represent and partition the state space of power system. Based on this structure, a novel recursive method is proposed to efffciently identify critical states by leveraging lattice partitioning and Optimal Power Flow(OPF) calculations. This m…

38 coastal, remote, and island communities are getting a lifeline for their fragile energy grids.
Through the Energy Technology Innovation Partnership Project, they're designing microgrids, exploring local renewable generation, and hardening systems against extreme weather. The goal: reliable, affordable power that can withstand the next storm.

38 Coastal, Remote, & Island Communities To Pursue Energy Reliability via Energy Technology Innovation Partnership Project - CleanTechnica
Support CleanTechnica's work through a Substack subscription or on Stripe. Latest Cohort Is Program’s Biggest Yet, With Broad Geographic Representation By Justin Daugherty, NLR Remote and island communities face unique energy challenges—but these challenges also present unique opportunities to increase energy affordability, reliability, and security. In their efforts to address ... [continued]

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out:

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages
Amir Hossein Yari, Kalmit Kulkarni, Ahmad Raza Khan, Fajri Koto
https://arxiv.org/abs/2510.07061

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages
While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated almost exclusively for English and other high-resource languages. This narrow focus leaves Indian languages, spoken by over 1.5 billion people, largely overlooked, casting doubt on the universality of current evaluation practices. To address this gap, we introduce ITEM, a large-scale benchmark that systematically evaluates the alignment of 26 automat…

Fiabilité: Tesla est la pire marque du marché, selon l'organisation étatsunienne de consommateurs "Consumer Reports".
https://www.consumerreports.org/cars/which-brands-make-the-best-used-cars-a2811658468/

Reliability of Single-Level Equality-Constrained Inverse Optimal Control
Filip Be\v{c}anovi\'c (University of Belgrade - School of Electrical Engineering), Kosta Jovanovi\'c (University of Belgrade - School of Electrical Engineering), Vincent Bonnet (LAAS-CNRS)
https://arxiv.org/abs/2510.08406

Reliability of Single-Level Equality-Constrained Inverse Optimal Control
Inverse optimal control (IOC) allows the retrieval of optimal cost function weights, or behavioral parameters, from human motion. The literature on IOC uses methods that are either based on a slow bilevel process or a fast but noise-sensitive minimization of optimality condition violation. Assuming equality-constrained optimal control models of human motion, this article presents a faster but robust approach to solving IOC using a single-level reformulation of the bilevel method and yields equi…

Algorithmic analysis of a complex reliability system subject to multiple events with a preventive maintenance strategy and a Bernoulli vacation policy through MMAPs
Juan Eloy Ruiz-Castro, Hugo Ala\'in Zapata-Ceballos
https://arxiv.org/abs/2510.11506

Algorithmic analysis of a complex reliability system subject to multiple events with a preventive maintenance strategy and a Bernoulli vacation policy through MMAPs
In this work, a single-unit multi-state system is considered. The system is subject to internal failures, as well as external shocks with multiple consequences. It also incorporates a preventive maintenance strategy and a Bernoulli vacation policy for the repairperson. It is algorithmically modeled in both continuous and discrete time using Marked Markovian Arrival Processes (MMAP). The system's operation/degradation level is divided into an indeterminate number of levels. Upon returning from a…

Giddy up.
✅ PowerToys 0.95 is here: new Light Switch utility, faster Command Palette, and Peek with Spacebar - Windows Command Line
https://devblogs.microsoft.com/commandline/powertoys-0-95-is-her…

PowerToys 0.95 is here: new Light Switch utility, faster Command Palette, and Peek with Spacebar
New month, new release! This one’s packed with quality-of-life improvements, performance boosts, and a bunch of long-standing community requests finally checked off the list — all while keeping the focus on fundamentals like speed and reliability! Get the update by checking for updates in PowerToys or heading to the release page. Let’s dive in! 🆕 […]

Assurance of Frontier AI Built for National Security
Matteo Pistillo, Charlotte Stix
https://arxiv.org/abs/2510.08792 https://arxiv.org/pdf/2510.08792

Assurance of Frontier AI Built for National Security
This memorandum presents four recommendations aimed at strengthening the principles of AI model reliability and AI model governability, as DoW, ODNI, NIST, and CAISI refine AI assurance frameworks under the AI Action Plan. Our focus concerns the open scientific problem of misalignment and its implications on AI model behavior. Specifically, misalignment and scheming capabilities can be a red flag indicating AI model insufficient reliability and governability. To address the national security th…

Walk the Talk: Is Your Log-based Software Reliability Maintenance System Really Reliable?
Minghua He, Tong Jia, Chiming Duan, Pei Xiao, Lingzhe Zhang, Kangjin Wang, Yifan Wu, Ying Li, Gang Huang
https://arxiv.org/abs/2509.24352

Walk the Talk: Is Your Log-based Software Reliability Maintenance System Really Reliable?
Log-based software reliability maintenance systems are crucial for sustaining stable customer experience. However, existing deep learning-based methods represent a black box for service providers, making it impossible for providers to understand how these methods detect anomalies, thereby hindering trust and deployment in real production environments. To address this issue, this paper defines a trustworthiness metric, diagnostic faithfulness, for models to gain service providers' trust, based o…

Corrigendum to "Degree-Based Approximations for Network Reliability Polynomials". Comment on J. Complex Networks 2025, 13, cnaf001
Xinhan Liu, Piet Van Mieghem
https://arxiv.org/abs/2510.06247

Corrigendum to "Degree-Based Approximations for Network Reliability Polynomials". Comment on J. Complex Networks 2025, 13, cnaf001
Our original paper \cite{VanMieghem2025} described the stochastic approximation $\overline{rel}_G(p)=\bigl[1-ϕ_D(1-p)\bigr]^{N}$ in \cite[eq. (2.2)]{VanMieghem2025} and the first-order approximation $(R_1)_G(p)=\prod_{i=1}^{N}\!\bigl[1-(1-p)^{d_i}\bigr]$ in \cite[eq. (4.1)]{VanMieghem2025} as upper bounds for the all-terminal reliability polynomial \(rel_G(p)\). The present corrigendum clarifies that the unique upper bound is \(\Pr[\hat D_{\min}\geq 1]\), which is difficult to compute exactly,…

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang, Min-Ling Zhang
https://arxiv.org/abs/2512.17788 https://arxiv.org/pdf/2512.17788 https://arxiv.org/html/2512.17788
arXiv:2512.17788v1 Announce Type: new
Abstract: Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to address the challenges of inexact supervision in both instance and label spaces. However, existing MIPL approaches often suffer from poor calibration, undermining classifier reliability. In this work, we propose a plug-and-play calibratable disambiguation loss (CDL) that simultaneously improves classification accuracy and calibration performance. The loss has two instantiations: the first one calibrates predictions based on probabilities from the candidate label set, while the second one integrates probabilities from both candidate and non-candidate label sets. The proposed CDL can be seamlessly incorporated into existing MIPL and PLL frameworks. We provide a theoretical analysis that establishes the lower bound and regularization properties of CDL, demonstrating its superiority over conventional disambiguation losses. Experimental results on benchmark and real-world datasets confirm that our CDL significantly enhances both classification and calibration performance.
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PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
https://arxiv.org/abs/2510.12409

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules apply. Travel agencies are eager to offload this error-prone task onto AI systems; however, deploying LLMs without verified reliability could result in significant financial losses and erode customer trust. PricingLogic comprises 300 natural-language questions based on booking requests derived from 42 real-world pricing p…

Proceedings of the International Workshop on Verification of Scientific Software
Stephen F. Siegel, Ganesh Gopalakrishnan
https://arxiv.org/abs/2510.12314 https://

Proceedings of the International Workshop on Verification of Scientific Software
This volume contains the proceedings of the Verification of Scientific Software (VSS 2025) workshop, held on 4 May 2025 at McMaster University, Canada, as part of ETAPS 2025. VSS brings together researchers in software verification and scientific computing to address challenges in ensuring the correctness and reliability of large-scale scientific codes. The program featured five peer-reviewed papers, three invited contributions, and a set of challenge problems, covering themes such as deductive…

Joyride: Rethinking Linux's network stack design for better performance, security, and reliability
Yanlin Du, Ruslan Nikolaev
https://arxiv.org/abs/2509.25015 https://

Joyride: Rethinking Linux's network stack design for better performance, security, and reliability
Contemporary distributed computing workloads, including scientific computation, data mining, and machine learning, increasingly demand OS networking with minimal latency as well as high throughput, security, and reliability. However, Linux's conventional TCP/IP stack becomes increasingly problematic for high-end NICs, particularly those operating at 100 Gbps and beyond. These limitations come mainly from overheads associated with kernel space processing, mode switching, and data copying in th…

The National Security Strategy of the US is worth reading. It clearly shows that the USA is no longer an ally of Europe and that NATO should be worried about the reliability of the US. It also shows how completely unhinged and extreme right the US government has become.
https://www.

RAG-Pull: Imperceptible Attacks on RAG Systems for Code Generation
Vasilije Stambolic, Aritra Dhar, Lukas Cavigelli
https://arxiv.org/abs/2510.11195 https://

RAG-Pull: Imperceptible Attacks on RAG Systems for Code Generation
Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminating the need for model retraining. It does so by adding external data into the LLM's context. We develop a new class of black-box attack, RAG-Pull, that inserts hidden UTF characters into queries or external code repositories, redirecting retrieval toward malicious code, thereby breaking the models' safety alignment. We observe that query and code perturbat…

Quantifying spike train synchrony and directionality: Measures and Applications
Thomas Kreuz
https://arxiv.org/abs/2510.07140 https://arxiv.org/pdf/2510.07…

Quantifying spike train synchrony and directionality: Measures and Applications
By introducing the twin concepts of reliability and precision along with the corresponding measures, Mainen and Sejnowski's seminal 1995 paper "Reliability of spike timing in neocortical neurons" (Mainen and Sejnowski, 1995) paved the way for a new kind of quantitative spike train analysis. In subsequent years a host of new methods was introduced that measured both the synchrony among neuronal spike trains and the directional component, e.g. how activity propogates between neurons. This develop…

New FDA turmoil throws agency's reliability into question (Axios)
https://www.axios.com/2025/11/04/fda-staff-exits-george-tidmarsh-hhs
http://www.memeorandum.com/251104/p122#a251104p122

OpenAI says GPT-5.2 Thinking hallucinates less than GPT-5.1 and has improved reliability for agentic AI needs; pre-release testers include Notion, Box, Shopify (Hayden Field/The Verge)
https://www.theverge.com/ai-artificial-intelligence/842529/open…

OpenAI calls GPT-5.2 the best model yet for professionals
OpenAI has launched GPT-5.2, which it calls its best AI model yet for professional use.

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
Meixia Lin, Meijiao Shi, Yunhai Xiao, Qian Zhang
https://arxiv.org/abs/2510.11546 http…

Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods. To improve robustness, we adopt a non-smooth Wilcoxon score based rank objective and incorporate structured group sparsity regularization, a natural generalization of the lasso, yielding a group lasso regularized rank regression method. By extending the tuning-free parameter selection scheme originally developed for the lasso, we introduce …

Between Knowledge and Care: Evaluating Generative AI-Based IUI in Type 2 Diabetes Management Through Patient and Physician Perspectives
Yibo Meng, Ruiqi Chen, Zhiming Liu, Xiaolan Ding, Yan Guan
https://arxiv.org/abs/2510.10048

Between Knowledge and Care: Evaluating Generative AI-Based IUI in Type 2 Diabetes Management Through Patient and Physician Perspectives
Generative AI systems are increasingly adopted by patients seeking everyday health guidance, yet their reliability and clinical appropriateness remain uncertain. Taking Type 2 Diabetes Mellitus (T2DM) as a representative chronic condition, this paper presents a two-part mixed-methods study that examines how patients and physicians in China evaluate the quality and usability of AI-generated health information. Study~1 analyzes 784 authentic patient questions to identify seven core categories of …

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 …

Proactive and Reactive Autoscaling Techniques for Edge Computing
Suhrid Gupta, Muhammed Tawfiqul Islam, Rajkumar Buyya
https://arxiv.org/abs/2510.10166 https://

Proactive and Reactive Autoscaling Techniques for Edge Computing
Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as performance, reliability, and availability metrics. While cloud computing offers the large data storage and computation resources necessary to handle peak demands, a hybrid cloud and edge environment is required to ensure SLA compliance. Several auto-scaling a…

log scales like "number of nines" are fun when you start to get to poor levels of performance.
... in a room full of people who would have laughed at them if they had said "let's get up to three nines of reliability",
a team I work with took a KPI to "get loss rates down below 20%" and received wise nods from most of the engineers in the room.
...but that's the equivalent of "let's get up to 0.5 nines"
that's a …

Reliability Sensitivity with Response Gradient
Siu-Kui Au, Zi-Jun Cao
https://arxiv.org/abs/2510.09315 https://arxiv.org/pdf/2510.09315

Reliability Sensitivity with Response Gradient
Engineering risk is concerned with the likelihood of failure and the scenarios when it occurs. The sensitivity of failure probability to change in system parameters is relevant to risk-informed decision making. Computing sensitivity is at least one level more difficult than the probability itself, which is already challenged by a large number of input random variables, rare events and implicit nonlinear `black-box' response. Finite difference with Monte Carlo probability estimates is spurious, …

Part of what makes the Steamdeck great is its stand-by reliability.
Because most desktop/console games are designed for long play sessions, they are difficult to consume on the go. Valve, by making standby seamless, allows for these games to be paused and then continued seamlessly.

Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
Max Malyi, Jonathan Shek, Andre Biscaya
https://arxiv.org/abs/2509.22366 h…

Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
A wealth of operational intelligence is locked within the unstructured free-text of wind turbine maintenance logs, a resource largely inaccessible to traditional quantitative reliability analysis. While machine learning has been applied to this data, existing approaches typically stop at classification, categorising text into predefined labels. This paper addresses the gap in leveraging modern large language models (LLMs) for more complex reasoning tasks. We introduce an exploratory framework t…

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.

Power Reserve Capacity from Virtual Power Plants with Reliability and Cost Guarantees
Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini
https://arxiv.org/abs/2510.04815 htt…

Power Reserve Capacity from Virtual Power Plants with Reliability and Cost Guarantees
The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provi…

Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
Hansol Park, Hoseong Ahn, Junwon Moon, Yejin Lee, Kyuhong Shim
https://arxiv.org/abs/2510.08581

Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
Hallucinations in vision-language models have been extensively studied using benchmarks that probe reliability in image-text settings. In contrast, the effect of spoken queries on multimodal hallucinations remains largely unexplored, despite the growing role of voice-driven interfaces. In this work, we investigate how spoken input influences hallucinations in multimodal large language models. We present RePOPE-Spk, an audio-augmented extension of the RePOPE benchmark, where queries are provided…

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…

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Jaesun Kim, Jinmu You, Yutack Park, Yunsung Lim, Yujin Kang, Jisu Kim, Haekwan Jeon, Deokgi Hong, Seung Yul Lee, Saerom Choi, Yongdeok Kim, Jae W. Lee, Seungwu Han
https://arxiv.org/abs/2510.11241

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to narrow datasets or computational protocols, limiting their reliability across chemical and functional domains. We introduce a transferable multi-domain training strategy that jointly optimizes universal and task-specific parameters through selective regularization, coupled with a domain-bridging set (DBS) that aligns …

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) …

The Landscape of problematic papers in the field of non-coding RNA
Ying Lou, Zhengyi Zhou, Guosheng Wang, Zhesi Shen, Menghui Li
https://arxiv.org/abs/2509.24511 https://…

The Landscape of problematic papers in the field of non-coding RNA
In recent years, the surge in retractions has been accompanied by numerous papers receiving comments that raise concerns about their reliability. The prevalence of problematic papers undermines the reliability of scientific research and threatens the foundation of evidence-based medicine. In this study,we focus on the field of non-coding RNA(ncRNA) as a case study to explore the typical characteristics of problematic papers from various perspectives, aiming to provide insights for addressing la…

Visible Light Communication for Vehicular Networks: A Tutorial
Pedro E. G\'oria Silva, Eduardo S. Lima, Jules M. Moualeu, Mohamed Korium, Pedro H. J. Nardelli
https://arxiv.org/abs/2510.11123

Visible Light Communication for Vehicular Networks: A Tutorial
The advent of the fifth-generation technology promises to bring about more vertical applications and emerging services that include vehicular networks and intelligent transportation systems (ITSs). To achieve their vision of real-time and safetyapplications, vehicular networks rely on short-range to medium-range communications. One emerging technology that aims to provide reliability and high-data rate in short-range communications is the visible light communications (VLC). Due to its remarkabl…

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories
Muhammad Ayub Sabir, Junbiao Pang, Jiaqi Wu, Fatima Ashraf
https://arxiv.org/abs/2510.12686

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories
Abnormal stop detection (ASD) in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance. However, two key challenges hinder ASD effectiveness: sparse GPS trajectories, which obscure short or unauthorized stops, and limited labeled data, which restricts supervised learning. Existing methods often assume dense sampling or regular movement patterns, limiting their applicability. To address data sparsity, we propose a Sparsity-Aw…

Embedding-Aware Noise Modeling of Quantum Annealing
Seon-Geun Jeong, Mai Dinh Cong, Dae-Il Noh, Quoc-Viet Pham, Won-Joo Hwang
https://arxiv.org/abs/2510.04594 https://

Embedding-Aware Noise Modeling of Quantum Annealing
Quantum annealing provides a practical realization of adiabatic quantum computation and has emerged as a promising approach for solving large-scale combinatorial optimization problems. However, current devices remain constrained by sparse hardware connectivity, which requires embedding logical variables into chains of physical qubits. This embedding overhead limits scalability and reduces reliability as longer chains are more prone to noise-induced errors. In this work, building on the known st…

Stand Up, NAO! Increasing the Reliability of Stand-Up Motions Through Error Compensation in Position Control
Philip Reichenberg, Tim Laue
https://arxiv.org/abs/2510.02129 https:…

Stand Up, NAO! Increasing the Reliability of Stand-Up Motions Through Error Compensation in Position Control
Stand-up motions are an indispensable part of humanoid robot soccer. A robot incapable of standing up by itself is removed from the game for some time. In this paper, we present our stand-up motions for the NAO robot. Our approach dates back to 2019 and has been evaluated and slightly expanded over the past six years. We claim that the main reason for failed stand-up attempts are large errors in the executed joint positions. By addressing such problems by either executing special motions to fre…

MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking
Tianhao Li, Tingfa Xu, Ying Wang, Haolin Qin, Xu Lin, Jianan Li
https://arxiv.org/abs/2510.12565

MMOT: The First Challenging Benchmark for Drone-based Multispectral Multi-Object Tracking
Drone-based multi-object tracking is essential yet highly challenging due to small targets, severe occlusions, and cluttered backgrounds. Existing RGB-based tracking algorithms heavily depend on spatial appearance cues such as color and texture, which often degrade in aerial views, compromising reliability. Multispectral imagery, capturing pixel-level spectral reflectance, provides crucial cues that enhance object discriminability under degraded spatial conditions. However, the lack of dedicate…

TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation
Yincen Qu, Huan Xiao, Feng Li, Hui Zhou, Xiangying Dai
https://arxiv.org/abs/2510.09011

TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation
Travel planning is a valuable yet complex task that poses significant challenges even for advanced large language models (LLMs). While recent benchmarks have advanced in evaluating LLMs' planning capabilities, they often fall short in evaluating feasibility, reliability, and engagement of travel plans. We introduce a comprehensive benchmark for travel planning that unifies fine-grained criteria into a single reward, enabling direct comparison of plan quality and seamless integration with reinfo…

Defects4C: Benchmarking Large Language Model Repair Capability with C/C Bugs
Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Klong, Yi Li
https://arxiv.org/abs/2510.11059

Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this iss…

📈 Build dashboards: Visualize input/output token usage, sessions & conversations, total costs in USD, terminal type distribution (#VSCode, Apple Terminal), requests per user & tool type usage (Read, Edit, LS, Bash)
🎯 Real insights: Measure ROI & productivity gains, spot performance bottlenecks & reliability issues, track adoption trends & user trust via accept/reject …

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Prarthana P. Kartholy, Thandi M. Labor, Neil N. Panchal, Sean H. Wang, Hillary N. Owusu
https://arxiv.org/abs/2510.09636

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Selecting a college major is a difficult decision for many incoming freshmen. Traditional academic advising is often hindered by long wait times, intimidating environments, and limited personalization. AI Chatbots present an opportunity to address these challenges. However, AI systems also have the potential to generate biased responses, prejudices related to race, gender, socioeconomic status, and disability. These biases risk turning away potential students and undermining reliability of AI s…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalizat...
Peng, Wen, Yang, Fu, Chen, Liu, Wu, Zheng, Sarfraz, Van Gool, Paudel, Stiefelhagen

Shallow Robustness, Deep Vulnerabilities: Multi-Turn Evaluation of Medical LLMs
Blazej Manczak, Eric Lin, Francisco Eiras, James O' Neill, Vaikkunth Mugunthan
https://arxiv.org/abs/2510.12255

Shallow Robustness, Deep Vulnerabilities: Multi-Turn Evaluation of Medical LLMs
Large language models (LLMs) are rapidly transitioning into medical clinical use, yet their reliability under realistic, multi-turn interactions remains poorly understood. Existing evaluation frameworks typically assess single-turn question answering under idealized conditions, overlooking the complexities of medical consultations where conflicting input, misleading context, and authority influence are common. We introduce MedQA-Followup, a framework for systematically evaluating multi-turn rob…

Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems
Haoran He
https://arxiv.org/abs/2510.11891 https://ar…

Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems
This paper proposes a machine learning-assisted channel estimation approach for massive MIMO systems, leveraging DNNs to outperform traditional LS and MMSE methods. In 5G and beyond, accurate channel estimation mitigates pilot contamination and high mobility issues that harm system reliability. The proposed DNN architecture includes multi-layer perceptrons with ReLU activation, 3 hidden layers (256, 128, 64 neurons respectively), uses Adam optimizer (learning rate 1e-4) and MSE loss function. I…

I never could have been literate in Chinese. Not that I could not read those 2 characters as different, but I’d never be able to write characters with that fine a distinction with any sort of reliability. American cursive was bad enough. https://mastodon.social/@mcc/115651531673910237

Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making
Ziv Ben-Zion, Zohar Elyoseph, Tobias Spiller, Teddy Lazebnik
https://arxiv.org/abs/2510.06222

Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making
Large language models (LLMs) are rapidly evolving from text generators to autonomous agents, raising urgent questions about their reliability in real-world contexts. Stress and anxiety are well known to bias human decision-making, particularly in consumer choices. Here, we tested whether LLM agents exhibit analogous vulnerabilities. Three advanced models (ChatGPT-5, Gemini 2.5, Claude 3.5-Sonnet) performed a grocery shopping task under budget constraints (24, 54, 108 USD), before and after expo…

IntersectioNDE: Learning Complex Urban Traffic Dynamics based on Interaction Decoupling Strategy
Enli Lin, Ziyuan Yang, Qiujing Lu, Jianming Hu, Shuo Feng
https://arxiv.org/abs/2510.11534

IntersectioNDE: Learning Complex Urban Traffic Dynamics based on Interaction Decoupling Strategy
Realistic traffic simulation is critical for ensuring the safety and reliability of autonomous vehicles (AVs), especially in complex and diverse urban traffic environments. However, existing data-driven simulators face two key challenges: a limited focus on modeling dense, heterogeneous interactions at urban intersections - which are prevalent, crucial, and practically significant in countries like China, featuring diverse agents including motorized vehicles (MVs), non-motorized vehicles (NMVs)…

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov, Narine Kokhlikyan, Tom Goldstein, Kamalika Chaudhuri, Chuan Guo
https://arxiv.org/abs/2510.04885

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction Hierarchy and SecAlign, have shown notable robustness against static attacks. However, to more thoroughly evaluate the robustness of these defenses, it is arguably necessary to employ strong attacks such as automated red-teaming. To this end, we introduce RL-Hammer, a simple recipe for training attacker models that automatically learn to perform stro…

LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
Linghan Huang, Peizhou Zhao, Huaming Chen
https://arxiv.org/abs/2510.10179 https://

LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
The rapid development of large language models (LLMs) has revolutionized software testing, particularly fuzz testing, by automating the generation of diverse and effective test inputs. This advancement holds great promise for improving software reliability. Meanwhile, the introduction of MOJO, a high-performance AI programming language blending Python's usability with the efficiency of C and C++, presents new opportunities to enhance AI model scalability and programmability. However, as a new l…

Auto-Prompt Ensemble for LLM Judge
Jiajie Li, Huayi Zhang, Peng Lin, Jinjun Xiong, Wei Xu
https://arxiv.org/abs/2510.06538 https://arxiv.org/pdf/2510.06538…

Auto-Prompt Ensemble for LLM Judge
We present a novel framework that improves the reliability of LLM judges by selectively augmenting LLM with auxiliary evaluation dimensions. Existing LLM judges often miss crucial evaluation dimensions because they fail to recognize the implicit standards underlying human assessments. To address this challenge, we propose the Auto-Prompt Ensemble (APE), an adaptive framework that automatically learns evaluation dimensions from its failure cases. APE incorporates a confidence-based ensemble mech…

Automated Neural Architecture Design for Industrial Defect Detection
Yuxi Liu, Yunfeng Ma, Yi Tang, Min Liu, Shuai Jiang, Yaonan Wang
https://arxiv.org/abs/2510.06669 https://…

Automated Neural Architecture Design for Industrial Defect Detection
Industrial surface defect detection (SDD) is critical for ensuring product quality and manufacturing reliability. Due to the diverse shapes and sizes of surface defects, SDD faces two main challenges: intraclass difference and interclass similarity. Existing methods primarily utilize manually designed models, which require extensive trial and error and often struggle to address both challenges effectively. To overcome this, we propose AutoNAD, an automated neural architecture design framework f…

Beating Harmful Stereotypes Through Facts: RAG-based Counter-speech Generation
Greta Damo, Elena Cabrio, Serena Villata
https://arxiv.org/abs/2510.12316 https://

Beating Harmful Stereotypes Through Facts: RAG-based Counter-speech Generation
Counter-speech generation is at the core of many expert activities, such as fact-checking and hate speech, to counter harmful content. Yet, existing work treats counter-speech generation as pure text generation task, mainly based on Large Language Models or NGO experts. These approaches show severe drawbacks due to the limited reliability and coherence in the generated countering text, and in scalability, respectively. To close this gap, we introduce a novel framework to model counter-speech ge…

What Do Temporal Graph Learning Models Learn?
Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier
https://arxiv.org/abs/2510.09416 https://arxiv.org/pdf…

What Do Temporal Graph Learning Models Learn?
Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models. However, recent work has raised concerns about the reliability of benchmark results, noting issues with commonly used evaluation protocols and the surprising competitiveness of simple heuristics. This contrast raises the question of which properties of the underlying graphs temporal graph learning models actually use to f…

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Jialin Zheng, Zhong Liu, Xiaonan Lu
https://arxiv.org/abs/2510.09826

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To solve this, this letter proposes a latent-feature-informed neural ordinary differential equation (LFI-NODE) modeling method that can achieve lightweight stability evaluation directly from trajectory data.} LFI-NODE parameterizes the entire system ODE with a single continuous-time neural network, allowing each new sample…

Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning
Hassaan Hashmi, Spyridon Pougkakiotis, Dionysis Kalogerias
https://arxiv.org/abs/2509.26311 ht…

Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning
We consider the problem of maximizing weighted sum rate in a multiple-input single-output (MISO) downlink wireless network with emphasis on user rate reliability. We introduce a novel risk-aggregated formulation of the complex WSR maximization problem, which utilizes the Conditional Value-at-Risk (CVaR) as a functional for enforcing rate (ultra)-reliability over channel fading uncertainty/risk. We establish a WMMSE-like equivalence between the proposed precoding problem and a weighted risk-aver…

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
Shan Jiang, Chenguang Zhu, Sarfraz Khurshid
https://arxiv.org/abs/2510.10066 https://arx…

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
JavaScript obfuscators are widely deployed to protect intellectual property and resist reverse engineering, yet their correctness has been largely overlooked compared to performance and resilience. Existing evaluations typically measure resistance to deobfuscation, leaving the critical question of whether obfuscators preserve program semantics unanswered. Incorrect transformations can silently alter functionality, compromise reliability, and erode security-undermining the very purpose of obfusc…

"It feels like hard work trying to talk to it": Understanding Older Adults' Experiences of Encountering and Repairing Conversational Breakdowns with AI Systems
Niharika Mathur, Tamara Zubatiy, Agata Rozga, Elizabeth Mynatt
https://arxiv.org/abs/2510.06690

"It feels like hard work trying to talk to it": Understanding Older Adults' Experiences of Encountering and Repairing Conversational Breakdowns with AI Systems
Designing Conversational AI systems to support older adults requires more than usability and reliability, it also necessitates robustness in handling conversational breakdowns. In this study, we investigate how older adults navigate and repair such breakdowns while interacting with a voice-based AI system deployed in their homes for medication management. Through a 20-week in-home deployment with 7 older adult participant dyads, we analyzed 844 recoded interactions to identify conversational br…

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs
Shuai Zhao, Xinyi Wu, Shiqian Zhao, Xiaobao Wu, Zhongliang Guo, Yanhao Jia, Anh Tuan Luu
https://arxiv.org/abs/2510.04503

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs
During fine-tuning, large language models (LLMs) are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings. In this study, we propose Poison-to-Poison (P2P), a general and effective backdoor defense algorithm. P2P injects benign triggers with safe alternative labels into a subset of training samples and fi…

ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
Binrong Zhu, Guiran Liu, Nina Jiang
https://arxiv.org/abs/2510.07456

ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning
The application of advanced generative artificial intelligence in education is often constrained by the lack of real-time adaptability, personalization, and reliability of the content. To address these challenges, we propose ExpertAgent - an intelligent agent framework designed for personalized education that provides reliable knowledge and enables highly adaptive learning experiences. Therefore, we developed ExpertAgent, an innovative learning agent that provides users with a proactive and per…

SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin
https://arxiv.org/abs/2510.06596 https://

SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
The performance of machine learning models depends heavily on training data. The scarcity of large-scale, well-annotated datasets poses significant challenges in creating robust models. To address this, synthetic data generated through simulations and generative models has emerged as a promising solution, enhancing dataset diversity and improving the performance, reliability, and resilience of models. However, evaluating the quality of this generated data requires an effective metric. This pape…

Can We Reliably Rank Model Performance across Domains without Labeled Data?
Veronica Rammouz, Aaron Gonzalez, Carlos Cruzportillo, Adrian Tan, Nicole Beebe, Anthony Rios
https://arxiv.org/abs/2510.09519

Can We Reliably Rank Model Performance across Domains without Labeled Data?
Estimating model performance without labels is an important goal for understanding how NLP models generalize. While prior work has proposed measures based on dataset similarity or predicted correctness, it remains unclear when these estimates produce reliable performance rankings across domains. In this paper, we analyze the factors that affect ranking reliability using a two-step evaluation setup with four base classifiers and several large language models as error predictors. Experiments on t…

Aggregate Modeling of Air-Conditioner Loads Under Packet-based Control with Both On and Off Grid Access Requests
Mohammad Hassan, Mads R. Almassalkhi
https://arxiv.org/abs/2510.10651

Aggregate Modeling of Air-Conditioner Loads Under Packet-based Control with Both On and Off Grid Access Requests
Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibility by sending a request to the DER coordinator to turn-ON or turn-OFF. Much work has focused on modeling and analyzing aggregations of DE…

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Shiyuan Yin, Chenjia Bai, Zihao Zhang, Junwei Jin, Xinxin Zhang, Chi Zhang, Xuelong Li
https://arxiv.org/abs/2510.08044

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistem…

Out-of-Distribution Detection from Small Training Sets using Bayesian Neural Network Classifiers
Kevin Raina, Tanya Schmah
https://arxiv.org/abs/2510.06025 https://

Out-of-Distribution Detection from Small Training Sets using Bayesian Neural Network Classifiers
Out-of-Distribution (OOD) detection is critical to AI reliability and safety, yet in many practical settings, only a limited amount of training data is available. Bayesian Neural Networks (BNNs) are a promising class of model on which to base OOD detection, because they explicitly represent epistemic (i.e. model) uncertainty. In the small training data regime, BNNs are especially valuable because they can incorporate prior model information. We introduce a new family of Bayesian posthoc OOD sco…

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Wen Ye, Zhaocheng Liu, Yuwei Gui, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
https://arxiv.org/abs/2510.07217

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods opera…

Integrating Domain Knowledge into Process Discovery Using Large Language Models
Ali Norouzifar, Humam Kourani, Marcus Dees, Wil van der Aalst
https://arxiv.org/abs/2510.07161 ht…

Integrating Domain Knowledge into Process Discovery Using Large Language Models
Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvement. However, models derived solely from event data may not accurately reflect the real process, as event logs are often incomplete or affected by noise, and domain knowledge, an important complementary resource, is typically disregarded. As a result, the discovered models may lack reliability for downstream tasks. We…

Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu
https://arxiv.org/abs/2510.05147 https://arxiv.org/pd…

Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Ensuring reliability in modern software systems requires rigorous pre-production testing across highly heterogeneous and evolving environments. Because exhaustive evaluation is infeasible, practitioners must decide how to allocate limited testing resources across configurations where failure probabilities may drift over time. Existing combinatorial optimization approaches are static, ad hoc, and poorly suited to such non-stationary settings. We introduce a novel reinforcement learning (RL) fram…

UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene
Christian Maurer, Snehal Jauhri, Sophie Lueth, Georgia Chalvatzaki
https://arxiv.org/abs/2510.06754

UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene
Comprehensive visual, geometric, and semantic understanding of a 3D scene is crucial for successful execution of robotic tasks, especially in unstructured and complex environments. Additionally, to make robust decisions, it is necessary for the robot to evaluate the reliability of perceived information. While recent advances in 3D neural feature fields have enabled robots to leverage features from pretrained foundation models for tasks such as language-guided manipulation and navigation, existi…

Adaptive Tool Generation with Models as Tools and Reinforcement Learning
Chenpeng Wang, Xiaojie Cheng, Chunye Wang, Linfeng Yang, Lei Zhang
https://arxiv.org/abs/2510.06825 http…

Adaptive Tool Generation with Models as Tools and Reinforcement Learning
Tool-augmented language models have demonstrated strong capabilities, but their reliance on live API access creates scalability and reliability challenges during training and deployment. We propose MTR, a simulation-first training framework for tool-augmented reasoning. Instead of relying on live APIs, MTR learns from complete ReAct traces with schema-validated, simulated observations. Our approach operates through a multi-agent architecture where a ToolMaker generates task-specific, OpenAI-com…

Introspection in Learned Semantic Scene Graph Localisation
Manshika Charvi Bissessur, Efimia Panagiotaki, Daniele De Martini
https://arxiv.org/abs/2510.07053 https://

Introspection in Learned Semantic Scene Graph Localisation
This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. …

Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Markus Reuter, Tobias Lingenberg, R\=uta Liepi\c{n}a, Francesca Lagioia, Marco Lippi, Giovanni Sartor, Andrea Passerini, Burcu Sayin
https://arxiv.org/abs/2510.06999

Towards Reliable Retrieval in RAG Systems for Large Legal Datasets
Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically dependent on the accuracy of the retrieval step. This is particularly challenging in the legal domain, where large databases of structurally similar documents often cause retrieval systems to fail. In this paper, we address this challenge by first identifying and quantifying a critical failure mode we term Document-Leve…

Constrained Natural Language Action Planning for Resilient Embodied Systems
Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker, Aurora Schmidt, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath
https://arxiv.org/abs/2510.06357

Constrained Natural Language Action Planning for Resilient Embodied Systems
Replicating human-level intelligence in the execution of embodied tasks remains challenging due to the unconstrained nature of real-world environments. Novel use of large language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but hallucinations limit their reliability, and thus, viability beyond a research context. Additionally, the prompt engineering required to achieve adequate system performance lacks transparency, a…

lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
Haoxin Wang, Xiaolong Tu, Hongyu Ke, Huirong Chai, Dawei Chen, Kyungtae Han
https://arxiv.org/abs/2510.06126

lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
Large Language Models (LLMs) are increasingly integrated into everyday applications, but their prevalent cloud-based deployment raises growing concerns around data privacy and long-term sustainability. Running LLMs locally on mobile and edge devices (on-device LLMs) offers the promise of enhanced privacy, reliability, and reduced communication costs. However, realizing this vision remains challenging due to substantial memory and compute demands, as well as limited visibility into performance-e…

Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring
Han-Jay Shu, Wei-Ning Chiu, Shun-Ting Chang, Meng-Ping Huang, Takeshi Tohyama, Ahram Han, Po-Chih Kuo
https://arxiv.org/abs/2510.01683

Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring
Deep learning models achieve strong performance in chest radiograph (CXR) interpretation, yet fairness and reliability concerns persist. Models often show uneven accuracy across patient subgroups, leading to hidden failures not reflected in aggregate metrics. Existing error detection approaches -- based on confidence calibration or out-of-distribution (OOD) detection -- struggle with subtle within-distribution errors, while image- and representation-level consistency-based methods remain undere…

UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification
Yiannis Charalambous, Claudionor N. Coelho Jr, Luis Lamb, Lucas C. Cordeiro
https://arxiv.org/abs/2510.05441

UnitTenX: Generating Tests for Legacy Packages with AI Agents Powered by Formal Verification
This paper introduces UnitTenX, a state-of-the-art open-source AI multi-agent system designed to generate unit tests for legacy code, enhancing test coverage and critical value testing. UnitTenX leverages a combination of AI agents, formal methods, and Large Language Models (LLMs) to automate test generation, addressing the challenges posed by complex and legacy codebases. Despite the limitations of LLMs in bug detection, UnitTenX offers a robust framework for improving software reliability and…

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Shuliang Liu, Zhipeng Xu, Zhenghao Liu, Yukun Yan, Minghe Yu, Yu Gu, Chong Chen, Huiyuan Xie, Ge Yu
https://arxiv.org/abs/2510.08145

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LLMs with human judgments, providing accurate and reliable assessments. However, LLM-based judgment models often exhibit judgment preference bias during the evaluation phase, tending to favor responses generated by themselves, undermining the reliability of their judgments. This paper introduces the Group-Based Polling …

Learning Stability Certificate for Robotics in Real-World Environments
Zhe Shen
https://arxiv.org/abs/2510.03123 https://arxiv.org/pdf/2510.03123

Learning Stability Certificate for Robotics in Real-World Environments
Stability certificates play a critical role in ensuring the safety and reliability of robotic systems. However, deriving these certificates for complex, unknown systems has traditionally required explicit knowledge of system dynamics, often making it a daunting task. This work introduces a novel framework that learns a Lyapunov function directly from trajectory data, enabling the certification of stability for autonomous systems without needing detailed system models. By parameterizing the Lyap…

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Muhammad Usama, Haris Koutsopoulos
https://arxiv.org/abs/2510.03121

Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway…

Evaluating Large Language Models for IUCN Red List Species Information
Shinya Uryu
https://arxiv.org/abs/2510.02830 https://arxiv.org/pdf/2510.02830…

Evaluating Large Language Models for IUCN Red List Species Information
Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for s…

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Soohaeng Yoo Willow, Tae Hyeon Park, Gi Beom Sim, Sung Wook Moon, Seung Kyu Min, D. ChangMo Yang, Hyun Woo Kim, Juho Lee, Chang Woo Myung
https://arxiv.org/abs/2510.03046

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current MLPs struggle with uncertainty quantification, limiting their reliability for active learning, calibration, and out-of-distribution (OOD) detection. We address these challenges by developing Bayesian E(3) equivariant MLPs with iterative restratification of many-body message passing. Our approach introduces the joint ene…

PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
Wanjia Zhao, Qinwei Ma, Jingzhe Shi, Shirley Wu, Jiaqi Han, Yijia Xiao, Si-Yuan Chen, Xiao Luo, Ludwig Schmidt, James Zou
https://arxiv.org/abs/2510.03185

PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
Benchmarks for competition-style reasoning have advanced evaluation in mathematics and programming, yet physics remains comparatively explored. Most existing physics benchmarks evaluate only final answers, which fail to capture reasoning processes, while recent stepwise methods rely on heuristic LLM-as-judge scoring or restrictive linear assumptions, limiting reliability and diagnostic validity. We introduce PRISM-Physics, a process-level evaluation framework and benchmark for complex physics r…

Knowledge-Graph Based RAG System Evaluation Framework
Sicheng Dong, Vahid Zolfaghari, Nenad Petrovic, Alois Knoll
https://arxiv.org/abs/2510.02549 https://…

Knowledge-Graph Based RAG System Evaluation Framework
Large language models (LLMs) has become a significant research focus and is utilized in various fields, such as text generation and dialog systems. One of the most essential applications of LLM is Retrieval Augmented Generation (RAG), which greatly enhances generated content's reliability and relevance. However, evaluating RAG systems remains a challenging task. Traditional evaluation metrics struggle to effectively capture the key features of modern LLM-generated content that often exhibits hi…

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger, Sepp Hochreiter
https://arxiv.org/abs/2510.02279

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Hallucinations are a common issue that undermine the reliability of large language models (LLMs). Recent studies have identified a specific subset of hallucinations, known as confabulations, which arise due to predictive uncertainty of LLMs. To detect confabulations, various methods for estimating predictive uncertainty in natural language generation (NLG) have been developed. These methods are typically evaluated by correlating uncertainty estimates with the correctness of generated text, with…

Learning to Reason for Hallucination Span Detection
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula, Kundan Krishna, Hadi Pouransari, Cheng-Yu Hsieh, Cem Koc, Joseph Yitan Cheng, Oncel Tuzel, Raviteja Vemulapalli
https://arxiv.org/abs/2510.02173

Learning to Reason for Hallucination Span Detection
Large language models (LLMs) often generate hallucinations -- unsupported content that undermines reliability. While most prior works frame hallucination detection as a binary task, many real-world applications require identifying hallucinated spans, which is a multi-step decision making process. This naturally raises the question of whether explicit reasoning can help the complex task of detecting hallucination spans. To answer this question, we first evaluate pretrained models with and withou…