Tootfinder

I see that with a lot of criticism of generative "AI"—people state that obviously it's completely unreliable and untrustworthy for _their domain of expertise_ but they'll somehow gladly use it for other stuff.
I believe this cognitive dissonance has to do with how the chatbots pretend to be humans and trick us to assume agency when there is none.
Anyway, as I said otherwise it's great, you should read it: https://theoatmeal.com/comics/ai_art

Google Pixel Watch 4 review: a significant leap in Google's smartwatch lineup with a nice domed display, but Gemini is unreliable, and GPS maps are still wonky (Victoria Song/The Verge)
https://www.theverge.com/tech/795383/google-pixel-…

The Google Pixel Watch 4 is the Android watch to beat
The $349.99 Google Pixel Watch 4 adds several smart updates that add up to the best Android smartwatch you can buy.

Wow, sending post to the US right now is absurdly expensive and very unreliable... spending a lot of energy on NZPost - not sure it's all their fault - the US is utterly munted, but things seem helluva loose.

Quantile-Scaled Bayesian Optimization Using Rank-Only Feedback
Tunde Fahd Egunjobi
https://arxiv.org/abs/2510.03277 https://arxiv.org/pdf/2510.03277…

Quantile-Scaled Bayesian Optimization Using Rank-Only Feedback
Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, particularly in hyperparameter tuning. However, standard BO assumes access to precise objective values, which may be unavailable, noisy, or unreliable in real-world settings where only relative or rank-based feedback can be obtained. In this study, we propose Quantile-Scaled Bayesian Optimization (QS-BO), a principled rank-based optimization framework. QS-BO converts ranks into heteroscedastic Gaussian targe…

When correcting for regression to the mean is worse than no correction at all
Jos\'e F. Fontanari, Mauro Santos
https://arxiv.org/abs/2509.04718 https://

When correcting for regression to the mean is worse than no correction at all
The ubiquitous regression to the mean (RTM) effect complicates statistical inference in biological studies of change. We demonstrate that common RTM correction methods are flawed: the Berry et al. method popularized by Kelly & Price in The American Naturalist is unreliable for hypothesis testing, leading to both false positives and negatives, while the theoretically unbiased Blomqvist method has poor efficiency in limited sample sizes. Our findings show that the most robust approach to handling…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna, Yuanyuan Gao, Michael von Gablenz
https://arxiv.org/abs/2510.05566 http…

Domain-Shift-Aware Conformal Prediction for Large Language Models
Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real world applications. Conformal prediction provides finite-sample, distribution-free coverage guarantees, but standard conformal prediction breaks down under domain shift, often leading to under-coverage and unreliable prediction sets. We propose a new framework called Domain-Shift-Aware Conf…

Symbol Timing Synchronization and Signal Detection for Ambient Backscatter Communication
Yuxin Li, Guangyue Lu, Yinghui Ye, Zehui Xiong, Liqin Shi
https://arxiv.org/abs/2510.02981

Symbol Timing Synchronization and Signal Detection for Ambient Backscatter Communication
Ambient backscatter communication (AmBC) enables ambient Internet of Things (AIoT) devices to achieve ultra-low-power, low-cost, and massive connectivity. Most existing AmBC studies assume ideal synchronization between the backscatter device (BD) and the backscatter receiver (BR). However, in practice, symbol timing offset (STO) occurs due to both the propagation delay and the BR activation latency, which leads to unreliable symbol recovery at the BR. Moreover, the uncontrollable nature of the …

Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech
Hieu-Nghia Huynh-Nguyen, Huynh Nguyen Dang, Ngoc-Son Nguyen, Van Nguyen
https://arxiv.org/abs/2510.02848

Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech
Zero-shot Text-to-Speech (TTS) has recently advanced significantly, enabling models to synthesize speech from text using short, limited-context prompts. These prompts serve as voice exemplars, allowing the model to mimic speaker identity, prosody, and other traits without extensive speaker-specific data. Although recent approaches incorporating language models, diffusion, and flow matching have proven their effectiveness in zero-shot TTS, they still encounter challenges such as unreliable synth…

Tell Congress: NO Funding for Primate Breeding! #AnimalRights

URGENT: Tell Congress NO to Funding Primate Breeding!
Right now, Congress is working on the 2025 federal budget, and the Senate Appropriations Committee has allocated a staggering $30 million to expand primate breeding and research at National Primate Research Centers. This funding will support invasive, unethical experiments on primates—causing unimaginable suffering while advancing unreliable science that fails over 90% of the time in human clinical trials. 🔬 Despite these facts, the NIH asked for $10 million, but the Senate has tripled th…

oh no
Don’t use AI to tell you how to vote in election, says Dutch watchdog
https://www.theguardian.com/world/2025/oct/21/ai-chatbots-unreliable-biased-advice-voters-dutch-watchdog

:oleander_hawkmoth:
i am very unreliable. it is my curse. i say fuck the reliable. i am so unreliable i can't even be relied on to be unreliable 🌊
#randomcrap

Does #cloudflare have issues again? Currently #linkedin return a 500 on all requests with "cloudflare" ind the message. The same for orangeamps.com which I know uses cloudflare from the last cloudflare issues.
Maybe people should stop using such an unreliable service.

Meanwhile on the #Sun as seen by SDO HMI https://jsoc1.stanford.edu/data/hmi/images/latest/HMI_latest_Int_4096x4096.gif at 21:53 UTC today ... the big groups (1)4296 and 4294 at the bottom as well as 4299 at the top keep marching towards the center of the disk, but not much has happened since the X1.9 flare two days ago. (The Kp forecast in https://www.spacepager.eu/data-products/forecast-of-the-kp-index sees something interesting three nights from now, though, but such long-term views are notoriously unreliable.)

💢 Unreliable public charging stations deter many potential electric vehicle buyers
https://techxplore.com/news/2025-09-unreliable-stations-deter-potential-electric.html

Unreliable public charging stations deter many potential electric vehicle buyers
Public electric vehicle charging stations in America have a bad reputation. They're notorious for breaking down, charging at a snail's pace, refusing customer payment and leaving drivers stranded without juice.

OF COURSE!
LLMs do not have any knowledge They only have words. When "introspecting" they are actually just word-salading that like they do everything else. They can't SAY that so they make up plausible-seeming explanations.
BECAUSE THAT IS ALL THEY DO, EVER. https://mastodon.social/@arstechni…

Ars Technica (@arstechnica@mastodon.social)
Attached: 1 image LLMs show a “highly unreliable” capacity to describe their own internal processes Anthropic finds some LLM "self-awareness," but "failures of introspection remain the norm." https://arstechnica.com/ai/2025/11/llms-show-a-highly-unreliable-capacity-to-describe-their-own-internal-processes/?utm_brand=arstechnica&utm_social-type=owned&utm_source=mastodon&utm_medium=social

For decades, some big polluters were allowed to estimate their emissions using methods the government knew were often unreliable.

Real Pollution Levels: Air monitors at coke manufacturers, chemical plants and other industrial facilities showed far higher emissions than the estimates, records viewed by ProPublica show.

Trump Halts New Rules: The Trump administration has halted rules requiring more than 130 industrial plants to install air monitors and comply with new e…

The EPA Let Companies Estimate Their Own Pollution Levels. We Discovered Real Emissions Are Far Worse.
The Trump administration has put a stop to EPA rules that would have required more than 130 industrial facilities to install air monitors to measure pollution. Millions of people living near these plants face an increased risk of cancer.

Cross-Breed Pig Identification Using Auricular Vein Pattern Recognition: A Machine Learning Approach for Small-Scale Farming Applications
Emmanuel Nsengiyumvaa, Leonard Niyitegekaa, Eric Umuhoza
https://arxiv.org/abs/2510.02197

Cross-Breed Pig Identification Using Auricular Vein Pattern Recognition: A Machine Learning Approach for Small-Scale Farming Applications
Accurate livestock identification is a cornerstone of modern farming: it supports health monitoring, breeding programs, and productivity tracking. However, common pig identification methods, such as ear tags and microchips, are often unreliable, costly, target pure breeds, and thus impractical for small-scale farmers. To address this gap, we propose a noninvasive biometric identification approach that leverages uniqueness of the auricular vein patterns. To this end, we have collected 800 ear im…

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Yaxin Du, Yuanshuo Zhang, Xiyuan Yang, Yifan Zhou, Cheng Wang, Gongyi Zou, Xianghe Pang, Wenhao Wang, Menglan Chen, Shuo Tang, Zhiyu Li, Siheng Chen
https://arxiv.org/abs/2510.02271

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content is noisy and unreliable, and many real-world tasks require precise, domain-specific knowledge unavailable from the web. The emergence of the Model Context Protocol (MCP) now allows agents to interface with thousands of specialized tools, seemingly resolving this limitation. Yet it remains unclear whether agents can ef…

#TIL
> The macros describing this structure live in <asm/ioctl.h> and are
> _IO(type,nr) and {_IOR,_IOW,_IOWR}(type,nr,size). They use
> sizeof(size) so that size is a misnomer here: this third argument
> is a data type.
>
> Note that the size bits are very unreliable: in lots of cases they
> are wrong, either because of buggy macros using
> sizeof(sizeof(struct)), or because of legacy values.
#Linux

@… is going down the same ugly road that already made kmail unusable. Playful OS devs adding fancy features. And while messing with the code, it starts eating your mail and stops to be reliable. It will end as a perfect frontend that is unreliable 😞 That was the initial reason I moved from kmail to TB. It is getting difficult to find something reliable in…

Tree companies are really unreliable, even for a quote. It's been weeks since a tree fell and none seem to want to take my money.

From my perspective ->
1. So much construction. It is almost impossible to go anywhere without lane closures.
2. Buses are less than reliable and the last update nuked one route I took and it leaves me one and only one route to the transfer point near work.
3. LRT construction. Going to work isn't too bad, getting home I have to travel AWAY from home, transfer and hope the bus going home arrives. Alternative is to take three buses and that is double the time than …

DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete
https://arxiv.org/abs/2509.26562

DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However, their unpredictable and unreliable behaviors in real-world settings require new approaches to characterize and ensure their reliability. This paper introduces DeepProv, a novel and customizable system designed to capture and characterize the runtime behavior of DNNs during inference by using their underlying graph structure. Inspired by system a…

Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM
Yanwei Du, Jing-Chen Peng, Patricio A. Vela
https://arxiv.org/abs/2509.22910 https:…

Good Weights: Proactive, Adaptive Dead Reckoning Fusion for Continuous and Robust Visual SLAM
Given that Visual SLAM relies on appearance cues for localization and scene understanding, texture-less or visually degraded environments (e.g., plain walls or low lighting) lead to poor pose estimation and track loss. However, robots are typically equipped with sensors that provide some form of dead reckoning odometry with reasonable short-time performance but unreliable long-time performance. The Good Weights (GW) algorithm described here provides a framework to adaptively integrate dead reck…

Is there somebody who migrated a fairly complex #Syncthing setup to something else like #NextCloud?
I've got ~100 different shares with ~40 Linux/Windows/Android hosts.
As Syncthing getting more and more unreliable not just on Android (using the fork) but also the desktop, I need to repl…

The main reasons for me to use Safari on macOS despite being behind on web standards is integration with its iOS siblings and performance.
However: bookmark sync is unreliable and relatedly reading list is so janky too that I switched to GoodLinks. And somehow the UI gets increasingly sluggish until I restart it like an old Windows system.
I guess it's time for an experiment.

"Unreliable public charging stations deter many potential electric vehicle buyers"
#EV #ElectricVehicles #Cars #Vehicles

Unreliable public charging stations deter many potential electric vehicle buyers
Public electric vehicle charging stations in America have a bad reputation. They're notorious for breaking down, charging at a snail's pace, refusing customer payment and leaving drivers stranded without juice.

Last week, while the country reacted to Kennedy’s rewrite of vaccine reality,
Jay Bhattacharya continued his own campaign to rewrite pandemic history.
He did this by retweeting misleading content from an unreliable source.
A closer look at that source, and at Bhattacharya’s long-standing relationship with it, raises serious concerns for science-based public health.

HHS Science Is Rotting From the Head
How RFK Jr and Jay Bhattacharya Are Undermining Science at HHS

'Don’t argue with strangers… and 11 more rules to survive the information crisis' https://www.theguardian.com/books/2025/nov/15/dont-argue-with-strangers-and-11-more-rules-to-survive-the-information-cris…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Junyu Lu, Songxin Zhang, Zejian Xie, Zhuoyang Song, Jiaxing Zhang
https://arxiv.org/abs/2509.17917 https://…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Recent advances in GUI agents have achieved remarkable grounding and action-prediction performance, yet existing models struggle with unreliable reward signals and limited online trajectory generation. In this paper, we introduce Orcust, a framework that integrates Principle-Constrained Reward Modeling (PCRM) and Online VM-Grounded Trajectory Construction (OVTC) to enhance reasoning reliability and data efficiency in interactive GUI tasks. We leverages environment-verifiable and LLM-derived pri…

The contrast between Google AI search results, which are terrible and totally unreliable, and NotebookLM is amazing.
I think it is irresponsible from Google to integrate AI search results in Google Search...
#ai #googlesearch

Reports of Russia advances in Ukraine are really describing the advance of Chinese drones on fiber optic cable.
The Russian infantry remains incompetent and unreliable, even though it outnumbers Ukraine in troops and ammo.
Ukraine needs ammo. Same story since the beginning of the war.
Giorgio is on the ground, dodging drones in order to provide updates from the front lines, where US reporters never venture.
He shares the friend link to bypass the paywall, so the world…

Russian Troops Are Not Advancing On The Ground. Technology Is.
Nightmare Days and Nights in Kramatorsk

Ensuring Reliable Participation in Subjective Video Quality Tests Across Platforms
Babak Naderi, Ross Cutler
https://arxiv.org/abs/2509.20001 https://arxiv…

Ensuring Reliable Participation in Subjective Video Quality Tests Across Platforms
Subjective video quality assessment (VQA) is the gold standard for measuring end-user experience across communication, streaming, and UGC pipelines. Beyond high-validity lab studies, crowdsourcing offers accurate, reliable, faster, and cheaper evaluation-but suffers from unreliable submissions by workers who ignore instructions or game rewards. Recent tests reveal sophisticated exploits of video metadata and rising use of remote-desktop (RD) connections, both of which bias results. We propose o…

Many mainstream media reporters no longer seem to have the morality or incentive to check sources, verify stories or offer retractions when they screw up.
For reporters in nearly every field, Clicks & Scoop > Accuracy & Ethics
😢
From: @…

PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning
Xiangmo Zhao, Nan Yang, Yang Wang, Zhanwen Liu
https://arxiv.org/abs/2509.22481

PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning
Mainstream event-based spatio-temporal representation learning methods typically process event streams by converting them into sequences of event frames, achieving remarkable performance. However, they neglect the high spatial sparsity and inter-frame motion redundancy inherent in event frame sequences, leading to significant computational overhead. Existing token sparsification methods for RGB videos rely on unreliable intermediate token representations and neglect the influence of event noise…

A look at Opera's Neon, a $20-per-month agentic AI browser that can take over browsing tasks, but its three built-in AI chatbots can be confusing and unreliable (The Verge)
https://www.theverge.com/tech/801899/opera-neon-ai-browser-trial-run

Opera’s Neon shows just how confusing AI browsers still are
Opera’s Neon browser has three AI agents and a $20 per month subscription attached.

AHA! #docker #nginx #mac Success!
After much logging and inspecting and reconfiguring you know what worked? (It's what always works!). Turning it off and on…
It's just a matter of turning the *right* thing off and on.
In this case, it was a matter of rebooting the webcam itself, which I really should have known because it has been unreliable at accepting new settings without a full reboot in the past. Weird that it was making the connection to NGINX but not actually sending data on that connection. The reboot of the webcam appears to have forced the data to be sent to the new proper docker address and on to Youtube.
Unfortunately I tried changing the stream key for Youtube before trying the webcam reboot which creates a new youtube URL, so I'll have to update my YouTube embed codes but oh well, it is worth having success with docker!
I feel much more comfortable working with docker now and understand the build/run workflow more now so I can start customizing my Dockerfile and be confident that I’m not going to miss anything.
Now I should be able to redirect all of my streams off of #footiMac and onto the slightly newer #imac2014 OR... most importantly, anywhere I have this docker container!
Woot!
Thank you everyone who gave tips and pointers along the way.
#learning #askmastodon
stream: https://www.youtube.com/live/WpbMTI9D6hI

A Taxonomy of Prompt Defects in LLM Systems
Haoye Tian, Chong Wang, BoYang Yang, Lyuye Zhang, Yang Liu
https://arxiv.org/abs/2509.14404 https://arxiv.org/p…

A Taxonomy of Prompt Defects in LLM Systems
Large Language Models (LLMs) have become key components of modern software, with prompts acting as their de-facto programming interface. However, prompt design remains largely empirical and small mistakes can cascade into unreliable, insecure, or inefficient behavior. This paper presents the first systematic survey and taxonomy of prompt defects, recurring ways that prompts fail to elicit their intended behavior from LLMs. We organize defects along six dimensions: (1) Specification and Intent, …

One other thing, while we don't claim that our mixed-effects logit model is the perfect way to account for non-independence between languages, we don't think it's correct, as Xia & Lindell assert, to just claim that our results are "counterintuitive", the fix-eff estimates are "unreliable" and that the high model fits are "unrealistic." Whether a mix model better captures the data-generat. process is ultimately an empirical question, not one to be decided by assertion. Take, for instance, our finding that once random effects for either subregion or language family are included, the estimated effect of L1_population reverses direction—from the negative value reported by Xia & Lindell et al. to a positive one.

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Mathijs van Noort, Femke Ongenae, Pieter Bonte
https://arxiv.org/abs/2509.10146 https://

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Temporal reasoning in dynamic, data-intensive environments increasingly demands expressive yet tractable logical frameworks. Traditional approaches often rely on negation to express absence or contradiction. In such contexts, Negation-as-Failure is commonly used to infer negative information from the lack of positive evidence. However, open and distributed systems such as IoT networks or the Semantic Web Negation-as-Failure semantics become unreliable due to incomplete and asynchronous data. Th…

Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters
Ali Baheri, David Millard, Alireza Vahid
https://arxiv.org/abs/2509.14521 https://arxiv.o…

Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters
Distributed systems require fusing heterogeneous local probability distributions into a global summary over sparse and unreliable communication networks. Traditional consensus algorithms, which average distributions in Euclidean space, ignore their inherent geometric structure, leading to misleading results. Wasserstein barycenters offer a geometry-aware alternative by minimizing optimal transport costs, but their entropic approximations via the Sinkhorn algorithm typically require centralized …

Assessing Policy Updates: Toward Trust-Preserving Intelligent User Interfaces
Matan Solomon, Ofra Amir, Omer Ben-Porat
https://arxiv.org/abs/2510.10616 https://

Assessing Policy Updates: Toward Trust-Preserving Intelligent User Interfaces
Reinforcement learning agents are often updated with human feedback, yet such updates can be unreliable: reward misspecification, preference conflicts, or limited data may leave policies unchanged or even worse. Because policies are difficult to interpret directly, users face the challenge of deciding whether an update has truly helped. We propose that assessing model updates -- not just a single model -- is a critical design challenge for intelligent user interfaces. In a controlled study, par…

Multi-Stage Location Optimization Through Power Delay Profile Alignment Using Site-Specific Wireless Ray Tracing
Mingjun Ying, Peijie Ma, Dipankar Shakya, Theodore S. Rappaport
https://arxiv.org/abs/2509.11923

Multi-Stage Location Optimization Through Power Delay Profile Alignment Using Site-Specific Wireless Ray Tracing
Ray tracing (RT) simulations require accurate transmitter (TX) and receiver (RX) location information from real-world measurements to accurately characterize wireless propagation behavior in an environment. Such wireless propagation measurements typically employ GPS-based logging for TX/RX locations, which can produce meter-level errors that lead to unreliable RT calibration and validation. These location misalignments cause inaccurate interactions between RT-generated multipath components (MPC…

ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Dakai Zhai, Jiong Gao, Boya Du, Junwei Xu, Qijie Shen, Jialin Zhu, Yuning Jiang
https://arxiv.org/abs/2510.09393

ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems.Existing approaches face three critical limitations: (i) reliance on noisy and unreliable behavioral signals; (ii) insufficient user-level information due to the lack of diverse interaction data; and (iii) a systemic training bias toward high-activity users that overshadows the needs of low-activity users.To address these challenges, we propose ChoirR…

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Jelle Luijkx, Runyu Ma, Zlatan Ajanovi\'c, Jens Kober
https://arxiv.org/abs/2509.16615 https://…

LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive exploration of large state-action spaces. Recent methods leverage the commonsense knowledge and reasoning abilities of large language models (LLMs) to guide exploration toward more meaningful states. However, LLMs can produce plans that are semantically plausible yet physically infeasible, yielding unreliable behavior. We introduce LLM-TALE, a framewo…

Training Flow Matching Models with Reliable Labels via Self-Purification
Hyeongju Kim, Yechan Yu, June Young Yi, Juheon Lee
https://arxiv.org/abs/2509.19091 https://

On the de-duplication of the Lakh MIDI dataset
Eunjin Choi, Hyerin Kim, Jiwoo Ryu, Juhan Nam, Dasaem Jeong
https://arxiv.org/abs/2509.16662 https://arxiv.o…

On the de-duplication of the Lakh MIDI dataset
A large-scale dataset is essential for training a well-generalized deep-learning model. Most such datasets are collected via scraping from various internet sources, inevitably introducing duplicated data. In the symbolic music domain, these duplicates often come from multiple user arrangements and metadata changes after simple editing. However, despite critical issues such as unreliable training evaluation from data leakage during random splitting, dataset duplication has not been extensively a…

Dynamic Control Aware Semantic Communication Enabled Image Transmission for Lunar Landing
Fangzhou Zhao, Yao Sun, Jianglin Lan, Muhammad Ali Imran
https://arxiv.org/abs/2510.06916

Dynamic Control Aware Semantic Communication Enabled Image Transmission for Lunar Landing
The primary challenge in autonomous lunar landing missions lies in the unreliable local control system, which has limited capacity to handle high-dynamic conditions, severely affecting landing precision and safety. Recent advancements in lunar satellite communication make it possible to establish a wireless link between lunar orbit satellites and the lunar lander. This enables satellites to run high-performance autonomous landing algorithms, improving landing accuracy while reducing the lander'…

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
Meryem Malak Dif, Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane
https://arxiv.org/abs/2509.12233

Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust a…

Graph-based Point Cloud Surface Reconstruction using B-Splines
Stuti Pathak, Rhys G. Evans, Gunther Steenackers, Rudi Penne
https://arxiv.org/abs/2509.16050 https://

Graph-based Point Cloud Surface Reconstruction using B-Splines
Generating continuous surfaces from discrete point cloud data is a fundamental task in several 3D vision applications. Real-world point clouds are inherently noisy due to various technical and environmental factors. Existing data-driven surface reconstruction algorithms rely heavily on ground truth normals or compute approximate normals as an intermediate step. This dependency makes them extremely unreliable for noisy point cloud datasets, even if the availability of ground truth training data …

A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes
Viktor A. Lilja, Albin J. Sv\"ardsby, Timo Gahlmann, Philippe Tassin
https://arxiv.org/abs/2509.06130

A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes
Neural networks have been demonstrated to be able to provide fast surrogate models that can accelerate the inverse design of optical and electromagnetic devices. Nevertheless, such neural networks can be unreliable and normally require extreme amounts of data for training. Here we show that these limitations can be alleviated by constraining neural-network models using prior knowledge about the governing physics. We propose a universal physics-informed neural network for electromagnetic scatter…

GrACE: A Generative Approach to Better Confidence Elicitation in Large Language Models
Zhaohan Zhang, Ziquan Liu, Ioannis Patras
https://arxiv.org/abs/2509.09438 https://…

GrACE: A Generative Approach to Better Confidence Elicitation in Large Language Models
Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in high-stakes applications, such as healthcare and finance. Existing methods either require expensive computational overhead or suffer from poor calibration, making them impractical and unreliable for real-world deployment. In this work, we propose GrACE, a Generative Approach to Confidence Elicitation that enables scalable and reliable confidence elicitation for LLMs. GrACE…

Robustness and accuracy of mean opinion scores with hard and soft outlier detection
Dietmar Saupe, Tim Bleile
https://arxiv.org/abs/2509.06554 https://arxi…

Robustness and accuracy of mean opinion scores with hard and soft outlier detection
In subjective assessment of image and video quality, observers rate or compare selected stimuli. Before calculating the mean opinion scores (MOS) for these stimuli from the ratings, it is recommended to identify and deal with outliers that may have given unreliable ratings. Several methods are available for this purpose, some of which have been standardized. These methods are typically based on statistics and sometimes tested by introducing synthetic ratings from artificial outliers, such as ra…

CaR1: A Multi-Modal Baseline for BEV Vehicle Segmentation via Camera-Radar Fusion
Santiago Montiel-Mar\'in, Angel Llamazares, Miguel Antunes-Garc\'ia, Fabio S\'anchez-Garc\'ia, Luis M. Bergasa
https://arxiv.org/abs/2509.10139

CaR1: A Multi-Modal Baseline for BEV Vehicle Segmentation via Camera-Radar Fusion
Camera-radar fusion offers a robust and cost-effective alternative to LiDAR-based autonomous driving systems by combining complementary sensing capabilities: cameras provide rich semantic cues but unreliable depth, while radar delivers sparse yet reliable position and motion information. We introduce CaR1, a novel camera-radar fusion architecture for BEV vehicle segmentation. Built upon BEVFusion, our approach incorporates a grid-wise radar encoding that discretizes point clouds into structured…

DRACO: Data Replication and Collection Framework for Enhanced Data Availability and Robustness in IoT Networks
Waleed Bin Qaim, Oznur Ozkasap, Rabia Qadar, Moncef Gabbouj
https://arxiv.org/abs/2510.07464

DRACO: Data Replication and Collection Framework for Enhanced Data Availability and Robustness in IoT Networks
The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected devices, which can lead to data loss. This paper presents DRACO (Data Replication and Collection), a framework that integrates a distributed hop-…

MemTraceDB: Reconstructing MySQL User Activity Using ActiviTimeTrace Algorithm
Mahfuzul I. Nissan
https://arxiv.org/abs/2509.05891 https://arxiv.org/pdf/25…

MemTraceDB: Reconstructing MySQL User Activity Using ActiviTimeTrace Algorithm
Database audit and transaction logs are fundamental to forensic investigations, but they are vulnerable to tampering by privileged attackers. Malicious insiders or external threats with administrative access can alter, purge, or temporarily disable logging mechanisms, creating significant blind spots and rendering disk-based records unreliable. Memory analysis offers a vital alternative, providing investigators direct access to volatile artifacts that represent a ground-truth source of recent u…

Acetrans: An Autonomous Corridor-Based and Efficient UAV Suspended Transport System
Weiyan Lu, Huizhe Li, Yuhao Fang, Zhexuan Zhou, Junda Wu, Yude Li, Youmin Gong, Jie Mei
https://arxiv.org/abs/2509.10349

Acetrans: An Autonomous Corridor-Based and Efficient UAV Suspended Transport System
Unmanned aerial vehicles (UAVs) with suspended payloads offer significant advantages for aerial transportation in complex and cluttered environments. However, existing systems face critical limitations, including unreliable perception of the cable-payload dynamics, inefficient planning in large-scale environments, and the inability to guarantee whole-body safety under cable bending and external disturbances. This paper presents Acetrans, an Autonomous, Corridor-based, and Efficient UAV suspende…

Deep Visual Odometry for Stereo Event Cameras
Sheng Zhong, Junkai Niu, Yi Zhou
https://arxiv.org/abs/2509.08235 https://arxiv.org/pdf/2509.08235

Deep Visual Odometry for Stereo Event Cameras
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HD…