Tootfinder

Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
Daking Rai, Samuel Miller, Kevin Moran, Ziyu Yao
https://arxiv.org/abs/2507.00322

Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanced parentheses. In this study, we investigate the underlying mechanisms behind the persistence of these errors across LMs of varying sizes (124M-7B) to both understand and mitigate the errors. Our study reveals that LMs rely on a number of components (attention heads and FF neurons) that independently make their own predictions. While some components reli…

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
Yunrui Yu, Hang Su, Cheng-zhong Xu, Zhizhong Su, Jun Zhu
https://arxiv.org/abs/2507.22428

Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
Gradient-based adversarial attacks using the Cross-Entropy (CE) loss often suffer from overestimation due to relative errors in gradient computation induced by floating-point arithmetic. This paper provides a rigorous theoretical analysis of these errors, conducting the first comprehensive study of floating-point computation errors in gradient-based attacks across four distinct scenarios: (i) unsuccessful untargeted attacks, (ii) successful untargeted attacks, (iii) unsuccessful targeted attack…

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images
Sara Yavari, Rahul Nitin Pandya, Jacob Furst
https://arxiv.org/abs/2507.00983

DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images
Accurate segmentation of brain tumors in MRI scans is essential for reliable clinical diagnosis and effective treatment planning. Recently, diffusion models have demonstrated remarkable effectiveness in image generation and segmentation tasks. This paper introduces a novel approach to corrective segmentation based on diffusion models. We propose DMCIE (Diffusion Model with Concatenation of Inputs and Errors), a novel framework for accurate brain tumor segmentation in multi-modal MRI scans. We e…

How to tell a vibe coder of lying when they say they check their code.
People who will admit to using LLMs to write code will usually claim that they "carefully check" the output since we all know that LLM code has a lot of errors in it. This is insufficient to address several problems that LLMs cause, including labor issues, digital commons stress/pollution, license violation, and environmental issues, but at least it's they are checking their code carefully we shouldn't assume that it's any worse quality-wise than human-authored code, right?
Well, from principles alone we can expect it to be worse, since checking code the AI wrote is a much more boring task than writing code yourself, so anyone who has ever studied human-computer interaction even a little bit can predict people will quickly slack off, stating to trust the AI way too much, because it's less work. I'm a different domain, the journalist who published an entire "summer reading list" full of nonexistent titles is a great example of this. I'm sure he also intended to carefully check the AI output, but then got lazy. Clearly he did not have a good grasp of the likely failure modes of the tool he was using.
But for vibe coders, there's one easy tell we can look for, at least in some cases: coding in Python without type hints. To be clear, this doesn't apply to novice coders, who might not be aware that type hints are an option. But any serious Python software engineer, whether they used type hints before or not, would know that they're an option. And if you know they're an option, you also know they're an excellent tool for catching code defects, with a very low effort:reward ratio, especially if we assume an LLM generates them. Of the cases where adding types requires any thought at all, 95% of them offer chances to improve your code design and make it more robust. Knowing about but not using type hints in Python is a great sign that you don't care very much about code quality. That's totally fine in many cases: I've got a few demos or jam games in Python with no type hints, and it's okay that they're buggy. I was never going to debug them to a polished level anyways. But if we're talking about a vibe coder who claims that they're taking extra care to check for the (frequent) LLM-induced errors, that's not the situation.
Note that this shouldn't be read as an endorsement of vibe coding for demos or other rough-is-acceptable code: the other ethical issues I skipped past at the start still make it unethical to use in all but a few cases (for example, I have my students use it for a single assignment so they can see for themselves how it's not all it's cracked up to be, and even then they have an option to observe a pre-recorded prompt session instead).

The Impact of Spectroscopic Redshift Errors on Cosmological Measurements
Shengyu He, Jiaxi Yu, Antoine Rocher, Daniel Forero-S\'anchez, Jean-Paul Kneib, Cheng Zhao, Etienne Burtin
https://arxiv.org/abs/2508.21182

The Impact of Spectroscopic Redshift Errors on Cosmological Measurements
Spectroscopic redshift errors, including redshift uncertainty and catastrophic failures, can bias cosmological measurements from galaxy redshift surveys at sub-percent level. We investigate their impact on full-shape clustering analysis using contaminated mock catalogs. We find that redshift uncertainty introduces a damping effect on the power spectrum. This damping is scale-dependent and absorbed by counterterms in the clustering model, keeping parameter biases below $5\%$ for the DESI survey.…

The Trump administration rescinds VOA layoff notices due to document errors that could have delayed or nullified the layoffs, plans to attempt the layoffs again (Minho Kim/New York Times)
https://www.nytimes.com/2025/06/27/us/politics/trump-voa-layoffs…

Trump Administration Rescinds Voice of America Layoffs After Errors in Notices
The errors in the layoff notices could have derailed President Trump’s effort to dismantle the news organization. The administration said it would attempt the layoffs again.

Not quite a piece of CHERI-cake: Are new digital security by design architectures usable?
Maysara Alhindi, Joseph Hallett
https://arxiv.org/abs/2506.23682 …

Not quite a piece of CHERI-cake: Are new digital security by design architectures usable?
A digital security-by-design computer architecture, like CHERI, lets you program without fear of buffer overflows or other memory safety errors, but CHERI also rewrites some of the assumptions about how C works and how fundamental types (such as pointers) are implemented in hardware. We conducted a usability study to examine how developers react to the changes required by CHERI when porting software to run on it. We find that developers struggle with CHERI's display of warnings and errors and a…

«It’s an issue at the intersection of several critical problems with the modern internet: Google’s search monopoly, rampant porn piracy, a DMCA takedown process vulnerable to errors and abuse, and now the automation of all of the above in order to operate at scale.»
https://www.404media.co/how-onlyfans-piracy-is-ruining-the-internet-for-everyone/

H2SGEMM: Emulating FP32 GEMM on Ascend NPUs using FP16 Units with Precision Recovery and Cache-Aware Optimization
Weicheng Xue, Baisong Xu, Kai Yang, Yongxiang Liu, Dengdeng Fan, Pengxiang Xu, Yonghong Tian
https://arxiv.org/abs/2507.23387

H2SGEMM: Emulating FP32 GEMM on Ascend NPUs using FP16 Units with Precision Recovery and Cache-Aware Optimization
Low-precision matrix engines, such as FP16 cube, offer high throughput but lack support for full-precision computation. In this work, we propose H2SGEMM, a high-performance algorithm for emulating FP32 general matrix-matrix multiplication (GEMM) using only FP16 computation units on a representative AI accelerator. The method decomposes each FP32 operand into two FP16 values and compensates for numerical errors through a tunable scaling strategy. A detailed analysis of numerical errors, includin…

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Sarah Pungitore, Shashank Yadav, David Maughan, Vignesh Subbian
https://arxiv.org/abs/2507.23146

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
Objective: Although computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications, it is time-intensive because of manual data review. We previously assessed the ability of LLMs to perform computational phenotyping tasks using computable phenotypes for ARF respiratory support therapies. They successfully performed concept classification and classification of single-therapy phenotypes, but underperformed on multiple-therapy phenotype…

Starting with August 25th 2025, #Google shuts down yet another very popular service: #URLshortener goo.gl
Billions of links will go dead.
Background:

Weekend Reads
* URI redirection patterns https://arxiv.org/abs/2507.22019
* AI and college grad jobs https://www.

Not Here, Go There: Analyzing Redirection Patterns on the Web
URI redirections are integral to web management, supporting structural changes, SEO optimization, and security. However, their complexities affect usability, SEO performance, and digital preservation. This study analyzed 11 million unique redirecting URIs, following redirections up to 10 hops per URI, to uncover patterns and implications of redirection practices. Our findings revealed that 50% of the URIs terminated successfully, while 50% resulted in errors, including 0.06% exceeding 10 hops. …

LLM-Based Repair of Static Nullability Errors
Nima Karimipour, Michael Pradel, Martin Kellogg, Manu Sridharan
https://arxiv.org/abs/2507.20674 https://arxi…

LLM-Based Repair of Static Nullability Errors
Modern Java projects increasingly adopt static analysis tools that prevent null-pointer exceptions by treating nullness as a type property. However, integrating such tools into large, existing codebases remains a significant challenge. While annotation inference can eliminate many errors automatically, a subset of residual errors -- typically a mix of real bugs and false positives -- often persist and can only be resolved via code changes. Manually addressing these errors is tedious and error-p…

Threshold behavior of a social norm in response to error proneness
Quang Anh Le, Seung Ki Baek
https://arxiv.org/abs/2506.23907 https://

Threshold behavior of a social norm in response to error proneness
A social norm defines what is good and what is bad in social contexts, as well as what to do based on such assessments. A stable social norm should be maintained against errors committed by its players. In addition, individuals may have different probabilities of errors in following the norm, and a social norm would be unstable if it benefited those who do not follow the norm carefully. In this work, we show that Simple Standing, which has been known to resist errors and mutants successfully, a…

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
Junping Wang, Bicheng Wang, Yibo Xuea, Yuan Xie
https://arxiv.org/abs/2506.23926

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
Resilience non-equilibrium measurement, the ability to maintain fundamental functionality amidst failures and errors, is crucial for scientific management and engineering applications of industrial chain. The problem is particularly challenging when the number or types of multiple co-evolution of resilience (for example, randomly placed) are extremely chaos. Existing end-to-end deep learning ordinarily do not generalize well to unseen full-feld reconstruction of spatiotemporal co-evolution stru…

#NewRule – if you write an app or service that uses a #directory to save important files, and you generate #errors in your logs about the files in said directory, but don't document what, if anything the user can do about it…

New Rule: Thou Shalt Document Any Magic File Your Code Complains Or Warns The User About
From the And ANOTHER thing... department.. ok, #NewRule - if you write an app or service that uses a #directory to save important file...

QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Junyu Zhou, Yuhao Liu, Shize Che, Anupam Mitra, Efekan K\"okc\"u, Ermal Rrapaj, Costin Iancu, Gushu Li
https://arxiv.org/abs/2506.22958

QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, conventional compilation methods for analog simulators are typically labor-intensive, prone to errors, and computationally demanding. This paper introduces QTurbo, a powerful analog quantum simulation compiler designed to significantly enhance compilation efficiency and optimize hardware execution time. By generating precise…

Crunchyroll ran subtitles filled with errors and references to ChatGPT on one of its shows; the streamer had said it had no plans to use AI in programming (Charles Pulliam-Moore/The Verge)
https://www.theverge.com/ai-artific…

Crunchyroll ran embarrassingly bad ChatGPT subtitles on its new anime series
Crunchyroll subscribers were treated to some unexpected AI slop while watching Necronomico and the Cosmic Horror Show.

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
Y. Du
https://arxiv.org/abs/2506.22496

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
Large Language Models (LLMs) exhibit systematic risk-taking behaviors analogous to those observed in gambling psychology, including overconfidence bias, loss-chasing tendencies, and probability misjudgment. Drawing from behavioral economics and prospect theory, we identify and formalize these "gambling-like" patterns where models sacrifice accuracy for high-reward outputs, exhibit escalating risk-taking after errors, and systematically miscalibrate uncertainty. We propose the Risk-Aware Respons…

I love it when Mentour Pilot covers a crash that just has so many levels of negligence, rule breaking and errors:
https://www.youtube.com/watch?v=WzeQYxeQZOI

Causal Link Discovery with Unequal Edge Error Tolerance
Joni Shaska, Urbashi Mitra
https://arxiv.org/abs/2507.21570 https://arxiv.org/pdf/2507.21570…

Causal Link Discovery with Unequal Edge Error Tolerance
This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where different types of edge errors may have different importance, current state-of-the-art causal discovery algorithms do not differentiate between the types of edge errors, nor provide any finite-sample guarantees on the edge errors. Hence, this framework seeks to minimize one type of error while keeping the other below a use…

You Sound a Little Tense: L2 Tailored Clear TTS Using Durational Vowel Properties
Paige Tutt\"os\'i, H. Henny Yeung, Yue Wang, Jean-Julien Aucouturier, Angelica Lim
https://arxiv.org/abs/2506.23367

You Sound a Little Tense: L2 Tailored Clear TTS Using Durational Vowel Properties
We present the first text-to-speech (TTS) system tailored to second language (L2) speakers. We use duration differences between American English tense (longer) and lax (shorter) vowels to create a "clarity mode" for Matcha-TTS. Our perception studies showed that French-L1, English-L2 listeners had fewer (at least 9.15%) transcription errors when using our clarity mode, and found it more encouraging and respectful than overall slowed down speech. Remarkably, listeners were not aware of these eff…

Rich #Errors in #Kotlin
https://www.youtube.com/watch?v=IUrA3mDSWZQ

A Central Differential Flux with High-Order Dissipation for Robust Simulations of Transcritical Flows
Bonan Xu, Chang Sun, Peixu Guo
https://arxiv.org/abs/2508.21599 https://

A Central Differential Flux with High-Order Dissipation for Robust Simulations of Transcritical Flows
The simulation of transcritical flows remains challenging due to strong thermodynamic nonlinearities that induce spurious pressure oscillations in conventional schemes.While primitive-variable formulations offer improved robustness under such conditions, they are always limited by energy conservation errors and the absence of systematic high-order treatments for numerical fluxes. In this paper, we introduce the Central Differential flux with High-Order Dissipation (CDHD), a novel numerical flux…

Feature Integration Spaces: Joint Training Reveals Dual Encoding in Neural Network Representations
Omar Claflin
https://arxiv.org/abs/2507.00269 https://…

Feature Integration Spaces: Joint Training Reveals Dual Encoding in Neural Network Representations
Current sparse autoencoder (SAE) approaches to neural network interpretability assume that activations can be decomposed through linear superposition into sparse, interpretable features. Despite high reconstruction fidelity, SAEs consistently fail to eliminate polysemanticity and exhibit pathological behavioral errors. We propose that neural networks encode information in two complementary spaces compressed into the same substrate: feature identity and feature integration. To test this dual enc…

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic ...
Arthur Grundner, Tom Beucler, Julien Savre, Axel Lauer, Manuel Schlund, Veronik…

Efficient Decoding of Insertion and Deletion Errors for Helberg Codes
Anthony Segrest, Hieu D. Nguyen
https://arxiv.org/abs/2508.18699 https://arxiv.org/pd…

Efficient Decoding of Insertion and Deletion Errors for Helberg Codes
We present the first known efficient decoding algorithm for correcting multiple insertion-deletion errors in Helberg codes and their non-binary generalizations, extending a known algorithm for correcting multiple deletion errors.

Oh dear. This is the new economy columnist in the Morning Star.
Spot the errors.
Public investment-led growth is the key to solving the British crisis | Morning Star
https://morningstaronline.co.uk/article/public-investment-led-growth…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Seg...
Sara Yavari, Rahul Nitin Pandya, Jacob Furst

Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes
Erin Hartman, Chad Hazlett, Arisa Sadeghpour
https://arxiv.org/abs/2507.19607

Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes
Weighting procedures are used in observational causal inference to adjust for covariate imbalance within the sample. Common practice for inference is to estimate robust standard errors from a weighted regression of outcome on treatment. However, it is well known that weighting can inflate variance estimates, sometimes significantly, leading to standard errors and confidence intervals that are overly conservative. We instead examine and recommend the use of robust standard errors from a weighted…

Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework
Nils Dycke, Iryna Gurevych
https://arxiv.org/abs/2508.21422 htt…

Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework
Large Language Models (LLMs) have great potential to accelerate and support scholarly peer review and are increasingly used as fully automatic review generators (ARGs). However, potential biases and systematic errors may pose significant risks to scientific integrity; understanding the specific capabilities and limitations of state-of-the-art ARGs is essential. We focus on a core reviewing skill that underpins high-quality peer review: detecting faulty research logic. This involves evaluating t…

Optimal Dual Frame Pairs: A Synergy with Graph Theory
Shankhadeep Mondal, Ram Narayan Mohapatra
https://arxiv.org/abs/2507.23249 https://arxiv.org/pdf/2507…

Optimal Dual Frame Pairs: A Synergy with Graph Theory
This paper investigates the optimization of dual frame pairs in the context of erasure problems in data transmission, using a graph theoretical approach. Frames are essential for mitigating errors and signal loss due to their redundancy properties. We address the use of spectral radius and operator norm for error measurements, presenting conditions for the optimality of dual pairs for one and two erasures. Our study shows that a tight frame generated by connected graphs and its canonical dual p…

Optimal Motion Scaling for Delayed Telesurgery
Jason Lim, Florian Richter, Zih-Yun Chiu, Jaeyon Lee, Ethan Quist, Nathan Fisher, Jonathan Chambers, Steven Hong, Michael C. Yip
https://arxiv.org/abs/2506.21689

Optimal Motion Scaling for Delayed Telesurgery
Robotic teleoperation over long communication distances poses challenges due to delays in commands and feedback from network latency. One simple yet effective strategy to reduce errors and increase performance under delay is to downscale the relative motion between the operating surgeon and the robot. The question remains as to what is the optimal scaling factor, and how this value changes depending on the level of latency as well as operator tendencies. We present user studies investigating th…

XABPs: Towards eXplainable Autonomous Business Processes
Peter Fettke, Fabiana Fournier, Lior Limonad, Andreas Metzger, Stefanie Rinderle-Ma, Barbara Weber
https://arxiv.org/abs/2507.23269

XABPs: Towards eXplainable Autonomous Business Processes
Autonomous business processes (ABPs), i.e., self-executing workflows leveraging AI/ML, have the potential to improve operational efficiency, reduce errors, lower costs, improve response times, and free human workers for more strategic and creative work. However, ABPs may raise specific concerns including decreased stakeholder trust, difficulties in debugging, hindered accountability, risk of bias, and issues with regulatory compliance. We argue for eXplainable ABPs (XABPs) to address these conc…

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/4]:
- Fundamental thresholds for computational and erasure errors via the coherent information
Luis Colmenarez, Seyong Kim, Markus M\"uller

Suppression of errors in collectively coded information
Martin J. Falk, Leon Zhou, Yoshiya J. Matsubara, Kabir Husain, Jack W. Szostak, Arvind Murugan
https://arxiv.org/abs/2508.21806

Suppression of errors in collectively coded information
Modern life largely transmits genetic information from mother to daughter through the duplication of single physically intact molecules that encode information. However, copying an extended molecule requires highly processive copying machinery and high fidelity that scales with the genome size to avoid the error catastrophe. Here, we explore these fidelity requirements in an alternative architecture, the virtual circular genome, in which no one physical molecule encodes the full genetic informa…

An alternative method of adjusting for multiple comparison in medical research
Jiale Li, Zimu Wei
https://arxiv.org/abs/2507.22679 https://arxiv.org/pdf/25…

An alternative method of adjusting for multiple comparison in medical research
Background Most methods of adjusting for multiplicity focus primarily on controlling type I errors and rarely consider type II errors. We propose a new method that considers controlling for false-positive findings while ensuring sufficient statistical power. Methods We proposed a new method for multiple corrections called (Beta-exponential Adjustment, BEA) that considered the statistical power to control for type I errors while also considering the probability of type II errors. We conducted …

from my link log —
Dynamic witnesses for static type errors in OCaml, or, ill-typed programs usually go wrong.
https://arxiv.org/abs/1606.07557
saved 2025-05-31

Dynamic Witnesses for Static Type Errors (or, Ill-Typed Programs Usually Go Wrong)
Static type errors are a common stumbling block for newcomers to typed functional languages. We present a dynamic approach to explaining type errors by generating counterexample witness inputs that illustrate how an ill-typed program goes wrong. First, given an ill-typed function, we symbolically execute the body to synthesize witness values that make the program go wrong. We prove that our procedure synthesizes general witnesses in that if a witness is found, then for all inhabited input types…

The Myth of Causal Necessity: Misspecified Models in Mean-Variance Optimization
Alejandro Rodriguez Dominguez
https://arxiv.org/abs/2507.23138 https://arxi…

The Myth of Causal Necessity: Misspecified Models in Mean-Variance Optimization
This paper challenges the claim that causal factor modeling is a necessary condition for investment efficiency. Through formal analysis and empirical counterexamples, we show that predictive models, even when structurally misspecified, can produce directionally accurate signals, valid mean-variance frontiers, and positive Sharpe ratios. Contrary to the assertion that causal omission leads to systematic inefficiency, we demonstrate that calibration errors, not lack of causal structure, are the p…

Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification
Xing Shen, Justin Szeto, Mingyang Li, Hengguan Huang, Tal Arbel
https://arxiv.org/abs/2506.23298

Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification
Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these models into real-world clinical practice requires an in-depth analysis of the accuracies of their predictions, and their associated calibration errors, particularly across different demographic subgroups. In this work, we present the first investigation into the calibration biases and demographic unfairness of MLLMs'…

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Nasim Shirvani-Mahdavi, Devin Wingfield, Amin Ghasemi, Chengkai Li
https://arxiv.org/abs/2507.23740 …

Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique labeling conventions of each KG, can make them difficult for humans to understand. In this paper, we exp…

How HP's poor strategic and systematic thinking by HP destroyed WebOS and $1.2 billion, a platform before it's time that deserved better.
https://philmckinney.substack.com/p/i-convinced-hps-board-to-buy-palm
(Fun fact: the lead desig…

I Convinced HP's Board to Buy Palm for $1.2B. Then I Watched Them Kill It in 49 Days
The systematic thinking errors that kill breakthrough technology and the decision framework that prevents these disasters

Adaptive estimation for nonparametric circular regression with errors in variables
Tien Dat Nguyen, Thanh Mai Pham Ngoc
https://arxiv.org/abs/2508.18581 https://

Adaptive estimation for nonparametric circular regression with errors in variables
This paper investigates the nonparametric estimation of a circular regression function in an errors-in-variables framework. Two settings are studied, depending on whether the covariates are circular or linear. Adaptive estimators are constructed and their theoretical performance is assessed through convergence rates over Sobolev and Hölder smoothness classes. Numerical experiments on simulated and real datasets illustrate the practical relevance of the methodology.

Impact of eHMI on Pedestrians' Interactions with Level-5 Automated Driving Systems
Viktoria Marcus, Griffin Pitts, Sanaz Motamedi
https://arxiv.org/abs/2507.21303 https://…

Impact of eHMI on Pedestrians' Interactions with Level-5 Automated Driving Systems
Each year, over half of global traffic fatalities involve vulnerable road users (e.g. pedestrians), often due to human error. Level-5 automated driving systems (ADSs) could reduce driver errors contributing to pedestrian accidents, though effectiveness depends on clarity and understandability for other road users. External human-machine interfaces (eHMIs) have been proposed to facilitate pedestrian-ADS communication, though consensus on optimal eHMI features remains unclear. In an online survey…

Investigating the Influence of Asymmetric Errors on Retrievals of Exoplanet Transmission Spectra
Jack J. Davey, Kai Hou Yip, Quentin Changeat, Ingo P. Waldmann
https://arxiv.org/abs/2507.19223

Investigating the Influence of Asymmetric Errors on Retrievals of Exoplanet Transmission Spectra
In studies of exoplanet atmospheres using transmission spectroscopy, Bayesian retrievals are the most popular form of analysis. In these procedures it is common to adopt a Gaussian likelihood. However, this implicitly assumes that the upper and lower error bars on the spectral points are equal. With recent observations from the James Webb Space Telescope (JWST) offering higher quality of data, it is worth revisiting this assumption to understand the impact that an asymmetry between the error ba…

Tim Scott's video attacking CBO: Nine errors in 60 seconds (Glenn Kessler/Washington Post)
https://www.washingtonpost.com/politics/2025/06/16/tim-scott-cbo-nine-errors/
http://www.memeorandum.com/250616/p25#a250616p25

Arnoldi Singular Vector perturbations for machine learning weather prediction
Jens Winkler, Michael Denhard
https://arxiv.org/abs/2506.22450 https://

Arnoldi Singular Vector perturbations for machine learning weather prediction
Since weather forecasts are fundamentally uncertain, reliable decision making requires information on the likelihoods of future weather scenarios. We explore the sensitivity of machine learning weather prediction (MLWP) using the 24h Pangu Weather ML model of Huawei to errors in the initial conditions with a specific kind of Singular Vector (SV) perturbations. Our Arnoldi-SV (A-SV) method does not need linear nor adjoint model versions and is applicable to numerical weather prediction (NWP) as …

Anysphere launches Bugbot, an AI-powered tool that integrates with GitHub to detect coding errors introduced by humans or AI agents, for $40 per month per user (Lauren Goode/Wired)
https://www.wired.com/story/cursor-releases-new-ai-tool-for-debugging-code/

Cursor’s New Bugbot Is Designed to Save Vibe Coders From Themselves
One of the most popular platforms for AI-assisted programming says the next era of vibe coding is all about supercharging error detection.

Set-membership identification of continuous-time MIMO systems via Tustin discretization
Vito Cerone, Sophie M. Fosson, Simone Pirrera, Diego Regruto
https://arxiv.org/abs/2508.19348

Set-membership identification of continuous-time MIMO systems via Tustin discretization
In this paper, we deal with the identification of continuous-time systems from sampled data corrupted by unknown but bounded errors. A significant challenge in continuous-time identification is the estimation of the input and output data derivatives. In this paper, we propose a novel method based on set-membership techniques and Tustin discretization, which overcomes the derivative measurement problem and the presence of bounded errors affecting all the measured signals. First, we derive the pr…

Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets
Sarina Penquitt, Tobias Riedlinger, Timo Heller, Markus Reischl, Matthias Rottmann
https://arxiv.org/abs/2508.17930

Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets
Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and industry. The consequences of incorrectly annotated data include reduced model performance, biased benchmark results, and lower overall accuracy. Current state-of-the-art label error detection methods often focus on a single computer vision task and, consequently, a specific type of dataset, containing, for example, either …

Evaluating Disassembly Errors With Only Binaries
Lambang Akbar Wijayadi, Yuancheng Jiang, Roland H. C. Yap, Zhenkai Liang, Zhuohao Liu
https://arxiv.org/abs/2506.20109

Evaluating Disassembly Errors With Only Binaries
Disassemblers are crucial in the analysis and modification of binaries. Existing works showing disassembler errors largely rely on practical implementation without specific guarantees and assume source code and compiler toolchains to evaluate ground truth. However, the assumption of source code is contrary to typical binary scenarios where only the binary is available. In this work, we investigate an approach with minimal assumptions and a sound approach to disassembly error evaluation that doe…

CachyOS is all the rage.
CachyOS is indeed very fast, even on this underpowered Chromebook.
It is also kinda broken.
The installer is so close to unusable (as in, it's a GUI, but the mouse cursor doesn't curse and the display brightness is pegged at minimum and can't be controlled) that I very nearly gave up. Installing Arch on the CLI is far faster.
Wayland is irritating, the font rendering is still fucked (text literally jiggles around on screen!) and the s…

Ich benutze schon eine Weile das LanguageTool: https://languagetool.org/ für Thunderbird und habe gerade gemerkt, dass es für Firefox existiert und es ist echt cool. Es erkennt nicht nur Rechtschreibung, sondern auch Satzbau.
Vermutlich werden die wieder Daten sammeln oder so, aber es ist erstmal sehr nützlich…

Free AI Grammar Checker - LanguageTool
Instantly check grammar, punctuation, and spelling errors with LanguageTool's AI-powered grammar checker. Enhance your writing in over 30 languages with ease.

Toward Intelligent Electronic-Photonic Design Automation for Large-Scale Photonic Integrated Circuits: from Device Inverse Design to Physical Layout Generation
Hongjian Zhou, Pingchuan Ma, Jiaqi Gu
https://arxiv.org/abs/2507.22301

Toward Intelligent Electronic-Photonic Design Automation for Large-Scale Photonic Integrated Circuits: from Device Inverse Design to Physical Layout Generation
Photonic Integrated Circuits (PICs) offer tremendous advantages in bandwidth, parallelism, and energy efficiency, making them essential for emerging applications in artificial intelligence (AI), high-performance computing (HPC), sensing, and communications. However, the design of modern PICs, which now integrate hundreds to thousands of components, remains largely manual, resulting in inefficiency, poor scalability, and susceptibility to errors. To address these challenges, we propose PoLaRIS, …

Refining Czech GEC: Insights from a Multi-Experiment Approach
Petr Pechman, Milan Straka, Jana Strakov\'a, Jakub N\'aplava
https://arxiv.org/abs/2506.22402

Refining Czech GEC: Insights from a Multi-Experiment Approach
We present a grammar error correction (GEC) system that achieves state of the art for the Czech language. Our system is based on a neural network translation approach with the Transformer architecture, and its key feature is its real-time synthetic generation pipeline, which dynamically augments sentences with artificial errors by introducing both language-agnostic and Czech-specific errors. We conduct a comprehensive series of experiments, investigating the Czech GEC corpora as bases for synth…

On one of my #OpenWRT routers I can't update all packages because of low free space on /overlay partition.
According to sources like https://forum.openwrt.org/t/not-enough-space/1…

Alert - new email spam angle!

Tankies are the integer overflow errors of the left. They’re what happens when you go so far left that you wrap around to the right.
TL;DR: Don’t be a tankie.

Resolving an error with path-tracing and a 2-to-1 mapping in a work of Jang, So, and Marotta
Murali Meyer, Daniel Stoertz, Mike Wang
https://arxiv.org/abs/2508.18543 https://

Resolving an error with path-tracing and a 2-to-1 mapping in a work of Jang, So, and Marotta
We identify two nontrivial errors in the proof of the main results of the work \emph{generalized baby Mandelbrot sets adorned with halos in families of rational maps} by Jang, So, and Marotta. We correct these errors, showing that the main results remain true.

Non-periodic Fourier propagation algorithms for partial differential equations
Channa Hatharasinghe, Run Yan Teh, Jesse van Rhijn, Peter D. Drummond, Margaret D. Reid
https://arxiv.org/abs/2507.21757

Non-periodic Fourier propagation algorithms for partial differential equations
Spectral methods for solving partial differential equations (PDEs) and stochastic partial differential equations (SPDEs) often use Fourier or polynomial spectral expansions on either uniform and non-uniform grids. However, while very widely used, especially for slowly-varying solutions, non-uniform spatial grids can give larger spatial discretization errors if the solutions change rapidly in space. Here, we implement a Fourier method that employs fast trigonometric expansions on a uniform grid …

VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
Samuel Talkington, Daniel K. Molzahn
https://arxiv.org/abs/2507.20995 https://…

VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
This paper provides an educational case study regarding our experience in deploying ChatGPT Large Language Models (LLMs) in the Spring 2025 and Fall 2023 offerings of ECE 4320: Power System Analysis and Control at Georgia Tech. As part of course assessments, students were tasked with identifying, explaining, and correcting errors in the ChatGPT outputs corresponding to power factor correction problems. While most students successfully identified the errors in the outputs from the GPT-4 version …

from my link log —
Find memory errors in unsafe Rust in production with GWP-ASan and the Scudo hardened allocator.
https://blog.colinbreck.com/making-unsafe-rust-a-little-safer-find-memory-errors-in-production-wit…

Making Unsafe Rust a Little Safer: Find Memory Errors in Production with GWP-ASan
GWP-ASan finds rare memory errors in C and C++ called from Rust.

RANA: Robust Active Learning for Noisy Network Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang, Zhuofan Li, Can Zhao, Yanan Cao
https://arxiv.org/abs/2507.22434 https://

RANA: Robust Active Learning for Noisy Network Alignment
Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. …

On Error Rate Approximations for FSO Systems with Weak Turbulence and Pointing Errors
Carmen \'Alvarez Roa, Yunus Can G\"ultekin, Kaiquan Wu, Cornelis Willem Korevaar, Alex Alvarado
https://arxiv.org/abs/2506.19627

On Error Rate Approximations for FSO Systems with Weak Turbulence and Pointing Errors
Atmospheric attenuation, atmospheric turbulence, geometric spread, and pointing errors, degrade the performance of free-space optical transmission. In the weak turbulence regime, the probability density function describing the distribution of the channel fading coefficient that models these four effects is known in the literature. This function is an integral equation, which makes it difficult to find simple analytical expressions of important performance metrics such as the bit error rate (BER…

Tracing Errors, Constructing Fixes: Repository-Level Memory Error Repair via Typestate-Guided Context Retrieval
Xiao Cheng, Zhihao Guo, Huan Huo, Yulei Sui
https://arxiv.org/abs/2506.18394

Tracing Errors, Constructing Fixes: Repository-Level Memory Error Repair via Typestate-Guided Context Retrieval
Memory-related errors in C programming continue to pose significant challenges in software development, primarily due to the complexities of manual memory management inherent in the language. These errors frequently serve as vectors for severe vulnerabilities, while their repair requires extensive knowledge of program logic and C's memory model. Automated Program Repair (APR) has emerged as a critical research area to address these challenges. Traditional APR approaches rely on expert-designed …

X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
Zigang Geng, Yibing Wang, Yeyao Ma, Chen Li, Yongming Rao, Shuyang Gu, Zhao Zhong, Qinglin Lu, Han Hu, Xiaosong Zhang, Linus, Di Wang, Jie Jiang
https://arxiv.org/abs/2507.22058

X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
Numerous efforts have been made to extend the ``next token prediction'' paradigm to visual contents, aiming to create a unified approach for both image generation and understanding. Nevertheless, attempts to generate images through autoregressive modeling with discrete tokens have been plagued by issues such as low visual fidelity, distorted outputs, and failure to adhere to complex instructions when rendering intricate details. These shortcomings are likely attributed to cumulative errors duri…

Learn to Position -- A Novel Meta Method for Robotic Positioning
Dongkun Wang, Junkai Zhao, Yunfei Teng, Jieyang Peng, Wenjing Xue, Xiaoming Tao
https://arxiv.org/abs/2506.20445

Learn to Position -- A Novel Meta Method for Robotic Positioning
Absolute positioning accuracy is a vital specification for robots. Achieving high position precision can be challenging due to the presence of various sources of errors. Meanwhile, accurately depicting these errors is difficult due to their stochastic nature. Vision-based methods are commonly integrated to guide robotic positioning, but their performance can be highly impacted by inevitable occlusions or adverse lighting conditions. Drawing on the aforementioned considerations, a vision-free, m…

Euclid: Forecasts on $\Lambda$CDM consistency tests with growth rate data
I. Ocampo, D. Sapone, S. Nesseris, G. Alestas, J. Garc\'ia-Bellido, Z. Sakr, C. J. A. P. Martins, J. P. Mimoso, A. Carvalho, A. Da Silva, A. Blanchard, S. Casas, S. Camera, M. Martinelli, V. Pettorino, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, F. Bernardeau, A. Biviano, E. Branchini, M. Brescia, G. Ca\~nas-Herrera, V. Capobianco, C. Carbone, V. F. Ca…

Euclid: Forecasts on $Λ$CDM consistency tests with growth rate data
The large-scale structure (LSS) of the Universe is an important probe for deviations from the canonical cosmological constant $Λ$ and cold dark matter ($Λ$CDM) model. A statistically significant detection of any deviations would signify the presence of new physics or the breakdown of any number of the underlying assumptions of the standard cosmological model or possible systematic errors in the data. In this paper, we quantify the ability of the LSS data products of the spectroscopic survey o…

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations
Arun Govind Neelan, Ferdin Sagai Don Bosco, Naveen Sagar Jarugumalli, Suresh Balaji Vedarethinam
https://arxiv.org/abs/2506.22413

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations
In the realm of computational fluid dynamics, traditional numerical methods, which heavily rely on discretization, typically necessitate the formulation of partial differential equations (PDEs) in conservative form to accurately capture shocks and other discontinuities in compressible flows. Conversely, utilizing non-conservative forms often introduces significant errors near these discontinuities or results in smeared shocks. This dependency poses a considerable limitation, particularly as man…

Characterizing the Sensitivity to Individual Bit Flips in Client-Side Operations of the CKKS Scheme
Matias Mazzanti, Augusto Vega, Esteban Mocskos
https://arxiv.org/abs/2507.20891

Characterizing the Sensitivity to Individual Bit Flips in Client-Side Operations of the CKKS Scheme
Homomorphic Encryption (HE) enables computation on encrypted data without decryption, making it a cornerstone of privacy-preserving computation in untrusted environments. As HE sees growing adoption in sensitive applications such as secure machine learning and confidential data analysis ensuring its robustness against errors becomes critical. Faults (e.g., transmission errors, hardware malfunctions, or synchronization failures) can corrupt encrypted data and compromise the integrity of HE opera…

A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence
Rifat Ara Shams, Didar Zowghi, Muneera Bano
https://arxiv.org/abs/2506.18538

A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence
Ensuring diversity and inclusion (D&I) in artificial intelligence (AI) is crucial for mitigating biases and promoting equitable decision-making. However, existing AI risk assessment frameworks often overlook inclusivity, lacking standardized tools to measure an AI system's alignment with D&I principles. This paper introduces a structured AI inclusivity question bank, a comprehensive set of 253 questions designed to evaluate AI inclusivity across five pillars: Humans, Data, Process, System, and …

MindChat: Enhancing BCI Spelling with Large Language Models in Realistic Scenarios
JIaheng Wang, Yucun Zhong, Chengjie Huang, Lin Yao
https://arxiv.org/abs/2507.21435 https://…

MindChat: Enhancing BCI Spelling with Large Language Models in Realistic Scenarios
Brain-computer interface (BCI) spellers can render a new communication channel independent of peripheral nervous system, which are especially valuable for patients with severe motor disabilities. However, current BCI spellers often require users to type intended utterances letter-by-letter while spelling errors grow proportionally due to inaccurate electroencephalogram (EEG) decoding, largely impeding the efficiency and usability of BCIs in real-world communication. In this paper, we present Mi…

Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer
Yuansheng Li, Yunhao Zou, Linwei Chen, Ying Fu
https://arxiv.org/abs/2506.21880

Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer
Interferometric Hyperspectral Imaging (IHI) is a critical technique for large-scale remote sensing tasks due to its advantages in flux and spectral resolution. However, IHI is susceptible to complex errors arising from imaging steps, and its quality is limited by existing signal processing-based reconstruction algorithms. Two key challenges hinder performance enhancement: 1) the lack of training datasets. 2) the difficulty in eliminating IHI-specific degradation components through learning-base…

Symmetry in Multi-Qubit Correlated Noise Errors Enhances Surface Code Thresholds
SiYing Wang, Yue Yan, ZhiXin Xia, Xiang-Bin Wang
https://arxiv.org/abs/2506.15490

Symmetry in Multi-Qubit Correlated Noise Errors Enhances Surface Code Thresholds
Surface codes are promising for practical quantum error correction due to their high threshold and experimental feasibility. However, their performance under realistic noise conditions, particularly those involving correlated errors, requires further investigation. In this study, we investigate the impact of correlated errors on the error threshold. In particular, we focus on several distinct types of correlated errors that could potentially arise from next-nearest-neighbor (NNN) coupling in qu…

Reliability Analysis of Smart Contract Execution Architectures: A Comparative Simulation Study
\"Onder G\"urcan
https://arxiv.org/abs/2506.22180 …

Reliability Analysis of Smart Contract Execution Architectures: A Comparative Simulation Study
The industrial market continuously needs reliable solutions to secure autonomous systems. Especially as these systems become more complex and interconnected, reliable security solutions are becoming increasingly important. One promising solution to tackle this challenge is using smart contracts designed to meet contractual conditions, avoid malicious errors, secure exchanges, and minimize the need for reliable intermediaries. However, smart contracts are immutable. Moreover, there are different…

Efficient Decoding of Double-circulant and Wozencraft Codes from Square-root Errors
Oren Dubin, Noam Oz, Noga Ron-Zewi
https://arxiv.org/abs/2507.13548 htt…

Efficient Decoding of Double-circulant and Wozencraft Codes from Square-root Errors
We present efficient decoding algorithms from square-root errors for two known families of double-circulant codes: A construction based on Sidon sets (Bhargava, Taveres, and Shiva, \emph{IEEE IT 74}; Calderbank, \emph{IEEE IT 83}; Guruswami and Li, \emph{IEEE IT 2025}), and a construction based on cyclic codes (Chen, Peterson, and Weldon, \emph{Information and Control 1969}). We further observe that the work of Guruswami and Li implicitly gives a transformation from double-circulant codes of ce…

MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant
Akash Awasthi, Brandon V. Chang, Anh M. Vu, Ngan Le, Rishi Agrawal, Zhigang Deng, Carol Wu, Hien Van Nguyen
https://arxiv.org/abs/2506.17320

MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant
Radiology students often struggle to develop perceptual expertise due to limited expert mentorship time, leading to errors in visual search and diagnostic interpretation. These perceptual errors, such as missed fixations, short dwell times, or misinterpretations, are not adequately addressed by current AI systems, which focus on diagnostic accuracy but fail to explain how and why errors occur. To address this gap, we introduce MAARTA (Multi-Agentic Adaptive Radiology Teaching Assistant), a mult…

How an AI assistant developed by Brazilian nonprofit NoHarm is helping pharmacists in remote Amazon clinics process prescriptions more quickly and catch errors (Pedro Nakamura/Rest of World)
https://restofworld.org/2025/brazil-amazon-ai-healthcare-prescript…

AI is making health care safer in the remote Amazon
At overburdened clinics, pharmacists use AI to catch dangerous errors. It’s frontier tech meets frontier medicine — with global implications.

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic ...
Arthur Grundner, Tom Beucler, Julien Savre, Axel Lauer, Manuel Schlund, Veronik…

Posterior Cram\'er-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Benjamin J. B. Deutschmann, Xuhong Li, Florian Meyer, Erik Leitinger
https://arxiv.org/abs/2506.19957

Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well estab…

ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
Shiye Cao, Maia Stiber, Amama Mahmood, Maria Teresa Parreira, Wendy Ju, Micol Spitale, Hatice Gunes, Chien-Ming Huang
https://arxiv.org/abs/2507.13468

ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
The integration of large language models (LLMs) into conversational robots has made human-robot conversations more dynamic. Yet, LLM-powered conversational robots remain prone to errors, e.g., misunderstanding user intent, prematurely interrupting users, or failing to respond altogether. Detecting and addressing these failures is critical for preventing conversational breakdowns, avoiding task disruptions, and sustaining user trust. To tackle this problem, the ERR@HRI 2.0 Challenge provides a m…

Leveraging erasure errors in logical qubits with metastable $^{171}$Yb atoms
Bichen Zhang, Genyue Liu, Guillaume Bornet, Sebastian P. Horvath, Pai Peng, Shuo Ma, Shilin Huang, Shruti Puri, Jeff D. Thompson
https://arxiv.org/abs/2506.13724

Leveraging erasure errors in logical qubits with metastable $^{171}$Yb atoms
Implementing large-scale quantum algorithms with practical advantage will require fault-tolerance achieved through quantum error correction, but the associated overhead is a significant cost. The overhead can be reduced by engineering physical qubits with fewer errors, and by shaping the residual errors to be more easily correctable. In this work, we demonstrate quantum error correcting codes and logical qubit circuits in a metastable ${}^{171}$Yb qubit with a noise bias towards erasure errors,…

Classification errors distort findings in automated speech processing: examples and solutions from child-development research
Lucas Gautheron, Evan Kidd, Anton Malko, Marvin Lavechin, Alejandrina Cristia
https://arxiv.org/abs/2508.15637

Classification errors distort findings in automated speech processing: examples and solutions from child-development research
With the advent of wearable recorders, scientists are increasingly turning to automated methods of analysis of audio and video data in order to measure children's experience, behavior, and outcomes, with a sizable literature employing long-form audio-recordings to study language acquisition. While numerous articles report on the accuracy and reliability of the most popular automated classifiers, less has been written on the downstream effects of classification errors on measurements and statist…

Does DESI DR2 challenge $\Lambda$CDM paradigm ?
Himanshu Chaudhary, Salvatore Capozziello, Vipin Kumar Sharma, Ghulam Mustafa
https://arxiv.org/abs/2507.21607 https://

Does DESI DR2 challenge $Λ$CDM paradigm ?
Although the debate about the systematic errors of DESI DR1 is still open, recent DESI DR2 is consistent with DESI DR1 and further strengthens the results of DESI DR1. In this analysis, we present a $\sim 2.38 σ$ discrepancy between Planck $Λ$ CDM cosmology and the DESI DR2 Luminous Red Galaxy (LRG1) data at $z_{\text{eff}} = 0.51$, which predicts an unexpectedly large value for $Ω_m$, $Ω_m = 0.471^{+0.119}_{-0.065}$. We find that the $w_0 w_a$CDM model, using DESI DR2 data, suggests $w_0 >…

A Slice-Based Change Impact Analysis for Regression Test Case Prioritization of Object-Oriented Programs
S. Panda, D. Munjal, D. P. Mohapatra
https://arxiv.org/abs/2508.19056 ht…

A Slice-Based Change Impact Analysis for Regression Test Case Prioritization of Object-Oriented Programs
Test case prioritization focuses on finding a suitable order of execution of the test cases in a test suite to meet some performance goals like detecting faults early. It is likely that some test cases execute the program parts that are more prone to errors and will detect more errors if executed early during the testing process. Finding an optimal order of execution for the selected regression test cases saves time and cost of retesting. This paper presents a static approach to prioritizing th…

Oblivious Deletion Codes
Roni Con, Ray Li
https://arxiv.org/abs/2506.18878 https://arxiv.org/pdf/2506.18878

Oblivious Deletion Codes
We construct deletion error-correcting codes in the oblivious model, where errors are adversarial but oblivious to the encoder's randomness. Oblivious errors bridge the gap between the adversarial and random error models, and are motivated by applications like DNA storage, where the noise is caused by hard-to-model physical phenomena, but not by an adversary. (1) (Explicit oblivious) We construct $t$ oblivious deletion codes, with redundancy $\sim 2t\log n$, matching the existential bound for…

Positioning via Probabilistic Graphical Models in RIS-Aided Systems with Channel Estimation Errors
Leonardo Tercas, Markku Juntti
https://arxiv.org/abs/2508.18009 https://

Positioning via Probabilistic Graphical Models in RIS-Aided Systems with Channel Estimation Errors
We propose a 6D Bayesian-based localization framework to estimate the position and rotation angles of a mobile station (MS) within an indoor reconfigurable intelligent surface (RIS)-aided system. This framework relies on a probabilistic graphical model to represent the joint probability distribution of random variables through their conditional dependencies and employs the No-U-Turn Sampler (NUTS) to approximate the posterior distribution based on the estimated channel parameters. Our framework…

Retrieval Enhanced Feedback via In-context Neural Error-book
Jongyeop Hyun, Bumsoo Kim
https://arxiv.org/abs/2508.16313 https://arxiv.org/pdf/2508.16313

Retrieval Enhanced Feedback via In-context Neural Error-book
Recent advancements in Large Language Models (LLMs) have significantly improved reasoning capabilities, with in-context learning (ICL) emerging as a key technique for adaptation without retraining. While previous works have focused on leveraging correct examples, recent research highlights the importance of learning from errors to enhance performance. However, existing methods lack a structured framework for analyzing and mitigating errors, particularly in Multimodal Large Language Models (MLLM…

Enabling Equitable Access to Trustworthy Financial Reasoning
William Jurayj, Nils Holzenberger, Benjamin Van Durme
https://arxiv.org/abs/2508.21051 https://

Enabling Equitable Access to Trustworthy Financial Reasoning
According to the United States Internal Revenue Service, ''the average American spends $\$270$ and 13 hours filing their taxes''. Even beyond the U.S., tax filing requires complex reasoning, combining application of overlapping rules with numerical calculations. Because errors can incur costly penalties, any automated system must deliver high accuracy and auditability, making modern large language models (LLMs) poorly suited for this task. We propose an approach that integrates LLMs with a symb…

Computing Floating-Point Errors by Injecting Perturbations
Youshuai Tan, Zhanwei Zhang, Jinfu Chen, Zishuo Ding, Jifeng Xuan, Weiyi Shang
https://arxiv.org/abs/2507.08467

Computing Floating-Point Errors by Injecting Perturbations
Floating-point programs form the foundation of modern science and engineering, providing the essential computational framework for a wide range of applications, such as safety-critical systems, aerospace engineering, and financial analysis. Floating-point errors can lead to severe consequences. Although floating-point errors widely exist, only a subset of inputs may trigger significant errors in floating-point programs. Therefore, it is crucial to determine whether a given input could produce s…

Crosstalk Insensitive Trapped-Ion Entanglement through Coupling Matrix Engineering
Vikram Kashyap, Caleb Walton, Sara Mouradian
https://arxiv.org/abs/2508.20329 https://

Crosstalk Insensitive Trapped-Ion Entanglement through Coupling Matrix Engineering
Control crosstalk due to imperfect optical addressing in trapped-ion entangling gates results in unwanted entanglement between the target ions and their neighbors. These errors are highly non-local, making them particularly difficult to correct using error correcting codes. We introduce a method to design entangling gates that are insensitive to optical crosstalk by controlling the excitation of the ions' motional modes to engineer an effective qubit-qubit coupling matrix that entirely excludes…

FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
Likun Tan, Kuan-Wei Huang, Kevin Wu
https://arxiv.org/abs/2507.20930 https://

FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
Hallucinations in large language models pose a critical challenge for applications requiring factual reliability, particularly in high-stakes domains such as finance. This work presents an effective approach for detecting and editing factually incorrect content in model-generated responses based on the provided context. Given a user-defined domain-specific error taxonomy, we construct a synthetic dataset by inserting tagged errors into financial question-answering corpora and then fine-tune fou…

Protocol for Purifying Noisy Preparation and Measurements of Qubits
Jaemin Kim, Seungchan Seo, Jiyoung Yun, Benjamin Lienhard, Joonwoo Bae
https://arxiv.org/abs/2508.16136 https…

Protocol for Purifying Noisy Preparation and Measurements of Qubits
Noise affecting qubit preparation and measurements accounts for a significant fraction of errors in quantum information processing. This is especially critical in tasks like variational quantum algorithms, quantum error correction, and entanglement distribution through repeaters. In this work, we present a protocol to purify noisy SPAM, effectively suppressing these errors to an arbitrarily low level. For instance, in a realistic scenario where qubits contain error rates around $0.05$ in both p…

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
John Alderete, Macarious Kin Fung Hui, Aanchan Mohan
https://arxiv.org/abs/2508.13060

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of so…

Enhancing the Clique Local Decoder to Correct Length-2 Space Errors in the Surface Code
Zikang Jia, Shravan Veerapaneni, Gokul Subramanian Ravi
https://arxiv.org/abs/2507.11481

Enhancing the Clique Local Decoder to Correct Length-2 Space Errors in the Surface Code
The growing demand for fault-tolerant quantum computing drives the need for efficient, scalable Quantum Error Correction (QEC) strategies. Conventional decoders designed for worst-case error scenarios incur significant overhead, prompting the development of local decoders, that leverage the sparse and often trivial nature of many quantum errors, to support the conventional decoders. The previously proposed Clique decoder addresses this by handling isolated, length-1 space and time errors within…

Union-Intersection Union-Find for Decoding Depolarizing Errors in Topological Codes
Tzu-Hao Lin, Ching-Yi Lai
https://arxiv.org/abs/2506.14745 https://

Union-Intersection Union-Find for Decoding Depolarizing Errors in Topological Codes
In this paper, we introduce the Union-Intersection Union-Find (UIUF) algorithm for decoding depolarizing errors in topological codes, combining the strengths of iterative and standard Union-Find (UF) decoding. While iterative UF improves performance at moderate error rates, it lacks an error correction guarantee. To address this, we develop UIUF, which maintains the enhanced performance of iterative UF while ensuring error correction up to half the code distance. Through simulations under code …

Probing for Arithmetic Errors in Language Models
Yucheng Sun, Alessandro Stolfo, Mrinmaya Sachan
https://arxiv.org/abs/2507.12379 https://

Probing for Arithmetic Errors in Language Models
We investigate whether internal activations in language models can be used to detect arithmetic errors. Starting with a controlled setting of 3-digit addition, we show that simple probes can accurately decode both the model's predicted output and the correct answer from hidden states, regardless of whether the model's output is correct. Building on this, we train lightweight error detectors that predict model correctness with over 90% accuracy. We then extend our analysis to structured chain-of…

Compilation-informed probabilistic quantum error cancellation
Giancarlo Camilo, Thiago O. Maciel, Allan Tosta, Abdulla Alhajri, Thais de Lima Silva, Daniel Stilck Fran\c{c}a, Leandro Aolita
https://arxiv.org/abs/2508.20174

Compilation-informed probabilistic quantum error cancellation
The potential of quantum computers to outperform classical ones in practically useful tasks remains challenging in the near term due to scaling limitations and high error rates of current quantum hardware. While quantum error correction (QEC) offers a clear path towards fault tolerance, overcoming the scalability issues will take time. Early applications will likely rely on QEC combined with quantum error mitigation (QEM). We introduce a QEM scheme against both compilation errors and logical-ga…

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
Yiming Du, Yifan Xiang, Bin Liang, Dahua Lin, Kam-Fai Wong, Fei Tan
https://arxiv.org/abs/2508.19996

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early turns can propagate across subsequent turns, undermining coherence and response quality. Existing methods typically address data quality via static prefiltering, which decouples quality control from training and fails to mitigate turn-level error propagation. In this context, we propose ReSURE (Regularizing Supervisio…

Investigating the Performance of Adaptive Optics on Different Bases of Spatial Modes in Turbulent Channels
Rojan Abolhassani, Lukas Scarfe, Francesco Di Colandrea, Alessio D'Errico, Khabat Heshami, Ebrahim Karimi
https://arxiv.org/abs/2508.21015

Investigating the Performance of Adaptive Optics on Different Bases of Spatial Modes in Turbulent Channels
Quantum key distribution (QKD) allows secure key exchange based on the principles of quantum mechanics, with higher-dimensional photonic states offering enhanced channel capacity and resilience to noise. Free-space QKD is crucial for global networks where fibres are impractical, but atmospheric turbulence introduces severe states distortions, particularly for spatial modes. Adaptive optics (AO) provides a pathway to correct these errors, though its effectiveness depends on the encoding basis. H…