Tootfinder

💢 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.

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…

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: @…

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
https://arxiv.org/abs/2508.10212

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Underground mining operations rely on distributed sensor networks to collect critical data daily, including mine temperature, toxic gas concentrations, and miner movements for hazard detection and operational decision-making. However, transmitting raw sensor data to a central server for training deep learning models introduces significant privacy risks, potentially exposing sensitive mine-specific information. Federated Learning (FL) offers a transformative solution by enabling collaborative mo…

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…

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…

Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Jakub \v{S}m\'id, Pavel P\v{r}ib\'a\v{n}, Pavel Kr\'al
https://arxiv.org/abs/2508.10369

Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current cross-lingual ABSA approaches focus on limited, less complex tasks and often rely on external translation tools. This paper introduces a novel approach using constrained decoding with sequence-to-sequence models, eliminating the need for unreliable translation tools and improving cross-lingual performance by 5\% on ave…

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…

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

Hands-on with Alexa : fun to talk to and good at handling multistep requests, but it is buggy, unreliable, and worse at some basic tasks than the original Alexa (Kevin Roose/New York Times)
https://www.nytimes.com/2025/0…

Alexa Got an A.I. Brain Transplant. How Smart Is It Now?
It took Amazon several years to overcome technical hurdles as it remade its voice assistant with new artificial intelligence technology.

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…

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

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…

From my LinkedIn post: “Telling your dev team to use AI coding tools is like telling your 2010 ops team to use AWS. They didn’t know how to code, they were ticket and click-it VMware people… developers who don’t have product management mindset or have never managed a dev team will fail by trying to micromanage the output of the tool rather than specifying the outcome of the product and managing the agent team to deliver that outcome.”

Back in 2010 when we first started using AWS to run Netflix, the AWS services were very incomplete, somewhat unreliable, had scaling issues, but were super fast to provision compared to filing… | Adrian Cockcroft
Back in 2010 when we first started using AWS to run Netflix, the AWS services were very incomplete, somewhat unreliable, had scaling issues, but were super fast to provision compared to filing tickets and waiting for data center ops teams to do what we need. We worked around the issues, built a new cloud native developer driven architecture and scaled Netflix at speed. At the time many people said we were crazy to run a large enterprise on cloud and would be back in datacenters when it failed. …

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.

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
https://arxiv.org/abs/2508.07299

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Neuro-symbolic (Nesy) learning improves the target task performance of models by enabling them to satisfy knowledge, while semi/self-supervised learning (SSL) improves the target task performance by designing unsupervised pretext tasks for unlabeled data to make models satisfy corresponding assumptions. We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge (i.e., assumptions), thereby unifying the theoretical frameworks of SSL and Nesy. Through rigorous t…

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.

In case you run Linux with a reasonably current kernel and are experiencing strange network issues like unexpected connection failures, unreliable browsing, etc.: there's been a bug in the kernel's network code, affecting earlier 6.16.x versions. Had this myself and heard from some other people who were affected. Fix is in latest 6.16.5.

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…

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…

The Dual Role of Low-Weight Pauli Propagation: A Flawed Simulator but a Powerful Initializer for Variational Quantum Algorithms
Zong-Liang Li, Shi-Xin Zhang
https://arxiv.org/abs/2508.06358

The Dual Role of Low-Weight Pauli Propagation: A Flawed Simulator but a Powerful Initializer for Variational Quantum Algorithms
Variational quantum algorithms (VQAs) rely on a classical optimizer to tune a parameterized quantum circuit, raising the question of whether classical methods can assist in this process. In this work, we investigate the low-weight Pauli propagation (LWPP) algorithm as a potential classical tool for simulating the VQA circuit. We first find that LWPP is an unreliable estimator of the true energy, limiting its utility as a direct simulator. However, we uncover its real value: despite this numeric…

Age of Information Minimization in Goal-Oriented Communication with Processing and Cost of Actuation Error Constraints
Rishabh S. Pomaje, Jayanth S., Rajshekhar V. Bhat, Nikolaos Pappas
https://arxiv.org/abs/2508.07865

Age of Information Minimization in Goal-Oriented Communication with Processing and Cost of Actuation Error Constraints
We study a goal-oriented communication system in which a source monitors an environment that evolves as a discrete-time, two-state Markov chain. At each time slot, a controller decides whether to sample the environment and if so whether to transmit a raw or processed sample, to the controller. Processing improves transmission reliability over an unreliable wireless channel, but incurs an additional cost. The objective is to minimize the long-term average age of information (AoI), subject to con…

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.

Under Trump, the US Department of Energy has issued a climate report that is pure anti-scientific nonsense. And if #AI is trained on such utterly unreliable publications, the results will be disastrous.
https://www.youtube.com/watch?v=f5nF3JUthV…

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
Jinhwa Kim, Ian G. Harris
https://arxiv.org/abs/2508.10031 https://

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks. Malicious users often exploit adversarial context to deceive LLMs, prompting them to generate responses to harmful queries. In this study, we propose a new defense mechanism called Context Filtering model, an input pre-processing method designed to filter out untrustworthy and unreliable context while identifying the primary prompts contai…

"Please do not use Google AI to find out our specials. Please go on our Facebook page or our website,"
the restaurant wrote in a weary Facebook post.
"Google AI is not accurate and is telling people specials that do not exist
which is causing angry customers yelling at our employees."
https://

Local Restaurant Exhausted as Google AI Keeps Telling Customers About Daily Specials That Don't Exist
A restaurant is begging its customers to please stop using notoriously unreliable Google AI to ask about its menu items.

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…

CIVQLLIE: Causal Intervention with Vector Quantization for Low-Light Image Enhancement
Tongshun Zhang, Pingping Liu, Zhe Zhang, Qiuzhan Zhou
https://arxiv.org/abs/2508.03338 htt…

CIVQLLIE: Causal Intervention with Vector Quantization for Low-Light Image Enhancement
Images captured in nighttime scenes suffer from severely reduced visibility, hindering effective content perception. Current low-light image enhancement (LLIE) methods face significant challenges: data-driven end-to-end mapping networks lack interpretability or rely on unreliable prior guidance, struggling under extremely dark conditions, while physics-based methods depend on simplified assumptions that often fail in complex real-world scenarios. To address these limitations, we propose CIVQLLI…

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…

`aria-label` still does not (consistently) translate.
https://adrianroselli.com/2019/11/aria-label-does-not-translate.html#Update07
Unless you only translate never-hidden content, your `aria-label`s never change, your content is an arbitrary subs…

aria-label Does Not Translate
As of my 25 July 2025 update at the end of this post, aria-label auto-translation support is less spotty than when I first wrote this post, but still unreliable. It does, actually. Sometimes. One of the big risks of using ARIA to define text content is that it often gets…

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…

Simple task: Which days of class should go on my syllabus?
❌ 4o-mini: shortened Nov break
❌ 4o: omitted days in Aug, Dec
✅ o3
❌ Claude 3-haiku: extended Oct break
❌ Claude 3.5 Sonnet V2: ext Oct br
✅ Claude 3.7 Sonnet
❌ Claude 4 Sonnet: extended Oct break
This is really unreliable AI performance.
#generativeAI

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-…

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Zihan Wang, Nina Mahmoudian
https://arxiv.org/abs/2508.09971 https://

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Vision-driven autonomous river following by Unmanned Aerial Vehicles is critical for applications such as rescue, surveillance, and environmental monitoring, particularly in dense riverine environments where GPS signals are unreliable. We formalize river following as a coverage control problem in which the reward function is submodular, yielding diminishing returns as more unique river segments are visited, thereby framing the task as a Submodular Markov Decision Process. First, we introduce Ma…

Understanding the Fundamental Trade-Off Between Age of Information and Throughput in Unreliable Wireless Networks
Lin Wang, I-Hong Hou
https://arxiv.org/abs/2508.12185 https://

Understanding the Fundamental Trade-Off Between Age of Information and Throughput in Unreliable Wireless Networks
This paper characterizes the fundamental trade-off between throughput and Age of Information (AoI) in wireless networks where multiple devices transmit status updates to a central base station over unreliable channels. To address the complexity introduced by stochastic transmission successes, we propose the throughput-AoI capacity region, which defines all feasible throughput-AoI pairs achievable under any scheduling policy. Using a second-order approximation that incorporates both mean and tem…

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens
https://arxiv.org/abs/2508.07617

On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making
AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI predictions, can result in worse decisions. Selective prediction, in which potentially unreliable model predictions are hidden from users, has been proposed as a solution. This approach assumes that when AI abstains and informs the user so, humans make decisions as they would w…

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…

I would really like to understand the productivity claims of "AI" for normal office workers.
If you summarize all emails with unreliable technology, you will miss important things; so in order to not fail at your job when your company now pays for "AI" you will have to read "AI" summaries _and_ all the whole emails?
Where's the savings in time and money there exactly? Last time I checked addition makes things larger?
I guess I'm a naive luddite.

@… 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…

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…

Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Yuqin Dai, Shuo Yang, Guoqing Wang, Yong Deng, Zhanwei Zhang, Jun Yin, Pengyu Zeng, Zhenzhe Ying, Changhua Meng, Can Yi, Yuchen Zhou, Weiqiang Wang, Shuai Lu
https://arxiv.org/abs/2508.07956…

Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unreliable or misleading content that can degrade retrieval accuracy, and the underutilization of web tools, which, if effectively employed, could enhance query precision and help mitigate this noise, ul…

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…

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…

"Ontario Superior Court Justice Maria Carroccia not only found that the Crown didn’t prove its case but also concluded that E.M. was an unreliable witness and said the woman did in fact consent."
it's over.
#innocent

"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.

Optimal rate-variance coding due to firing threshold adaptation near criticality
Mauricio Girardi-Schappo, Leonard Maler, Andr\'e Longtin
https://arxiv.org/abs/2509.04106 ht…

Optimal rate-variance coding due to firing threshold adaptation near criticality
Recurrently connected neuron populations play key roles in sensory perception and memory storage across various brain regions. While these populations are often assumed to encode information through firing rates, this method becomes unreliable with weak stimuli. We propose that in such cases, information can be transmitted via spatial spike patterns, employing a sparse or combinatorial coding based on firing rate variance. Around the critical point of a stochastic recurrent excitable network, w…

I'm giving up on CachyOS. Sank the rest of my evening into it, to no avail. DNS resolution is still unreliable in pacman and Firefox (and everything else) even after taking my network-local DNS server out of the chain, getting rid of systemd-resolved, and trying several different providers (quad9, Cloudflare, Google). The other ThinkPad running openSUSE is having no such issues tonight: it's CachyOS specific (since I see no evidence that it is a hardware issue - local network traffic…

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…

Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
Yuvraj Virk, Dongyu Liu
https://arxiv.org/abs/2508.06484 https://

Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
Non-technical end-users increasingly rely on AI code generation to perform technical tasks like data analysis. However, large language models (LLMs) remain unreliable, and it is unclear whether end-users can effectively identify model errors $\unicode{x2014}$ especially in realistic and domain-specific scenarios. We surveyed marketing and sales professionals to assess their ability to critically evaluate LLM-generated analyses of marketing data. Participants were shown natural language explanat…

Bootstrap Diagnostic Tests
Giuseppe Cavaliere, Luca Fanelli, Iliyan Georgiev
https://arxiv.org/abs/2509.01351 https://arxiv.org/pdf/2509.01351

Bootstrap Diagnostic Tests
Violation of the assumptions underlying classical (Gaussian) limit theory frequently leads to unreliable statistical inference. This paper shows the novel result that the bootstrap can detect such violation by means of simple and powerful tests which (a) induce no pre-testing bias, (b) can be performed using the same critical values in a broad range of applications, and (c) are consistent against deviations from asymptotic normality. By focusing on the discrepancy between the conditional distri…

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…

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'…

I was told that `aria-label` now always auto-translates in browsers. The evidence was meh. So I tested and updated:
https://adrianroselli.com/2019/11/aria-label-does-not-translate.html#Update07
Still nope.
Safari only knows 21 la…

aria-label Does Not Translate
As of my 25 July 2025 update at the end of this post, aria-label auto-translation support is less spotty than when I first wrote this post, but still unreliable. It does, actually. Sometimes. One of the big risks of using ARIA to define text content is that it often gets…

Never Come Up Empty: Adaptive HyDE Retrieval for Improving LLM Developer Support
Fangjian Lei, Mariam El Mezouar, Shayan Noei, Ying Zou
https://arxiv.org/abs/2507.16754

Never Come Up Empty: Adaptive HyDE Retrieval for Improving LLM Developer Support
Large Language Models (LLMs) have shown promise in assisting developers with code-related questions; however, LLMs carry the risk of generating unreliable answers. To address this, Retrieval-Augmented Generation (RAG) has been proposed to reduce the unreliability (i.e., hallucinations) of LLMs. However, designing effective pipelines remains challenging due to numerous design choices. In this paper, we construct a retrieval corpus of over 3 million Java and Python related Stack Overflow posts wi…

SIREN: Software Identification and Recognition in HPC Systems
Thomas Jakobsche, Fredrik Roberts\'en, Jessica R. Jones, Utz-Uwe Haus, Florina M. Ciorba
https://arxiv.org/abs/2508.18950

SIREN: Software Identification and Recognition in HPC Systems
HPC systems use monitoring and operational data analytics to ensure efficiency, performance, and orderly operations. Application-specific insights are crucial for analyzing the increasing complexity and diversity of HPC workloads, particularly through the identification of unknown software and recognition of repeated executions, which facilitate system optimization and security improvements. However, traditional identification methods using job or file names are unreliable for arbitrary user-pr…

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…

Mass conservation analysis of extrusion-based 3D printing simulations based on the level-set method
Carlos J. G. Rojas, C. A. G\'omez-P\'erez, Leyla \"Ozkan
https://arxiv.org/abs/2508.20617

Mass conservation analysis of extrusion-based 3D printing simulations based on the level-set method
Numerical simulations of extrusion-based printing require tracking evolving material bound- aries, a challenging task due to possible topological changes and mass conservation issues. Inaccurate conservation of mass can lead to a mismatch between the extruded and simulated shapes, and generally to unreliable predictions of the actual ink behavior. This work investigates the mass conservation properties of the conservative level-set method in extrusion-based 3D printing applications. We analyze …

L2Calib: $SE(3)$-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience
Baorun Li, Chengrui Zhu, Siyi Du, Bingran Chen, Jie Ren, Wenfei Wang, Yong Liu, Jiajun Lv
https://arxiv.org/abs/2508.06330

L2Calib: $SE(3)$-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience
Extrinsic calibration is essential for multi-sensor fusion, existing methods rely on structured targets or fully-excited data, limiting real-world applicability. Online calibration further suffers from weak excitation, leading to unreliable estimates. To address these limitations, we propose a reinforcement learning (RL)-based extrinsic calibration framework that formulates extrinsic calibration as a decision-making problem, directly optimizes $SE(3)$ extrinsics to enhance odometry accuracy. Ou…

Design of radiotelemetry systems for animal tracking
Laila Kazimierski, Guillermo Abramson, Nicol\'as Catalano
https://arxiv.org/abs/2507.19331 https://

Design of radiotelemetry systems for animal tracking
We describe the design and implementation of two low-cost, low-weight, radiotelemetry systems to measure the movement of small animals in a dense forest, where satellite positioning systems are unreliable and the attenuation of the vegetation poses several challenges. Both methods use stationary receiving stations that record the signal emitted by a portable transmitter carried by the animal. One of the methods measures the received power, while the other one registers the phase difference rece…

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 …

Neural Correction Operator: A Reliable and Fast Approach for Electrical Impedance Tomography
Amit Bhat, Ke Chen, Chunmei Wang
https://arxiv.org/abs/2507.18875 https://

Neural Correction Operator: A Reliable and Fast Approach for Electrical Impedance Tomography
Electrical Impedance Tomography (EIT) is a non-invasive medical imaging method that reconstructs electrical conductivity mediums from boundary voltage-current measurements, but its severe ill-posedness renders direct operator learning with neural networks unreliable. We propose the neural correction operator framework, which learns the inverse map as a composition of two operators: a reconstruction operator using L-BFGS optimization with limited iterations to obtain an initial estimate from mea…

Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation
Eman Ali, Chetan Arora, Muhammad Haris Khan
https://arxiv.org/abs/2507.22075 htt…

Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation
In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shifts or in visually complex scenarios. Conventional pseudo-label filtering approaches, which rely on fixed confidence thresholds, tend to be unreliable in fully unsupervised settings. In this work, we propose a novel adaptive pseudo-labeling framework that enhances CLIP's adaptation performance by integrating prototype …

Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco Barile
https://arxiv.org/abs/2507.13705

Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
Large Language Models (LLMs) are increasingly being implemented as joint decision-makers and explanation generators for Group Recommender Systems (GRS). In this paper, we evaluate these recommendations and explanations by comparing them to social choice-based aggregation strategies. Our results indicate that LLM-generated recommendations often resembled those produced by Additive Utilitarian (ADD) aggregation. However, the explanations typically referred to averaging ratings (resembling but not…

Dissecting RISC-V Performance: Practical PMU Profiling and Hardware-Agnostic Roofline Analysis on Emerging Platforms
Alexander Batashev
https://arxiv.org/abs/2507.22451 https://…

Dissecting RISC-V Performance: Practical PMU Profiling and Hardware-Agnostic Roofline Analysis on Emerging Platforms
As RISC-V architectures proliferate across embedded and high-performance domains, developers face persistent challenges in performance optimization due to fragmented tooling, immature hardware features, and platform-specific defects. This paper delivers a pragmatic methodology for extracting actionable performance insights on RISC-V systems, even under constrained or unreliable hardware conditions. We present a workaround to circumvent hardware bugs in one of the popular RISC-V implementations,…

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
Ioannis Tzortzis, Themistoklis Charalambous, Charalambos D. Charalambous
https://arxiv.org/abs/2509.04116

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
This paper considers the problem of remote state estimation for Markov jump linear systems in the presence of uncertainty in the posterior mode probabilities. Such uncertainty may arise when the estimator receives noisy or incomplete measurements over an unreliable communication network. To address this challenge, the estimation problem is formulated within a distributionally robust framework, where the true posterior is assumed to lie within a total variation distance ball centered at the nomi…

Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
Ver\'onica \'Alvarez, Santiago Mazuelas, Steven An, Sanjoy Dasgupta
https://arxiv.org/abs/2508.03896

Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities
The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programmatic weak supervision obtains probabilistic predictions for the labels by leveraging multiple weak labeling functions (LFs) that provide rough guesses for labels. Weak LFs commonly provide guesses with assorted types and unknown interdependences that can result in unreliable predictions. Furthermore, existing techniques for programmatic weak supervision cannot provide assessments for t…

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…

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…

Mechanism of the quasi-elastic scattering based on the dinuclear system concept
Zehong Liao, Yu Yang, Zepeng Gao, Jun Su, Long Zhu
https://arxiv.org/abs/2509.03778 https://

Mechanism of the quasi-elastic scattering based on the dinuclear system concept
A unified description of full reaction channels in low-energy heavy-ion collisions is a great challenge. Although the theoretical models based on the dinuclear system (DNS) concept have been successfully employed in multinucleon transfer (MNT) reactions, the underestimation of the quasi-elastic (QE) channel results in unreliable description of few nucleon transfer, especially for the light reaction systems. In this work, the DNS-sysu model is improved by introducing the impact-parameter-depende…

QuMAB: Query-based Multi-annotator Behavior Pattern Learning
Liyun Zhang, Zheng Lian, Hong Liu, Takanori Takebe, Yuta Nakashima
https://arxiv.org/abs/2507.17653 https://

QuMAB: Query-based Multi-annotator Behavior Pattern Learning
Multi-annotator learning traditionally aggregates diverse annotations to approximate a single ground truth, treating disagreements as noise. However, this paradigm faces fundamental challenges: subjective tasks often lack absolute ground truth, and sparse annotation coverage makes aggregation statistically unreliable. We introduce a paradigm shift from sample-wise aggregation to annotator-wise behavior modeling. By treating annotator disagreements as valuable information rather than noise, mode…

An adaptive design for optimizing treatment assignment in randomized clinical trials
Wei Zhang, Zhiwei Zhang, Aiyi Liu
https://arxiv.org/abs/2509.00429 https://

An adaptive design for optimizing treatment assignment in randomized clinical trials
The treatment assignment mechanism in a randomized clinical trial can be optimized for statistical efficiency within a specified class of randomization mechanisms. Optimal designs of this type have been characterized in terms of the variances of potential outcomes conditional on baseline covariates. Approximating these optimal designs requires information about the conditional variance functions, which is often unavailable or unreliable at the design stage. As a practical solution to this dilem…

Your AI, Not Your View: The Bias of LLMs in Investment Analysis
Hoyoung Lee, Junhyuk Seo, Suhwan Park, Junhyeong Lee, Wonbin Ahn, Chanyeol Choi, Alejandro Lopez-Lira, Yongjae Lee
https://arxiv.org/abs/2507.20957

Your AI, Not Your View: The Bias of LLMs in Investment Analysis
In finance, Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data. These conflicts become particularly problematic when LLMs are deployed in real-world investment services, where misalignment between a model's embedded preferences and those of the financial institution can lead to unreliable recommendations. Yet little research has examined what investment views LLMs actually hold. We propose an exp…

LLM4Sweat: A Trustworthy Large Language Model for Hyperhidrosis Support
Wenjie Lin, Jin Wei-Kocsis
https://arxiv.org/abs/2508.15192 https://arxiv.org/pdf/2…

LLM4Sweat: A Trustworthy Large Language Model for Hyperhidrosis Support
While large language models (LLMs) have shown promise in healthcare, their application for rare medical conditions is still hindered by scarce and unreliable datasets for fine-tuning. Hyperhidrosis, a disorder causing excessive sweating beyond physiological needs, is one such rare disorder, affecting 2-3% of the population and significantly impacting both physical comfort and psychosocial well-being. To date, no work has tailored LLMs to advance the diagnosis or care of hyperhidrosis. To addres…

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez, Syed Ali Zaidi, Hamed Ahmadi
https://arxiv.org/abs/2507.21698

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication ef…

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…

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion
Zoe Kotti, Konstantina Dritsa, Diomidis Spinellis, Panos Louridas
https://arxiv.org/abs/2508.16131

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion
Code completion entails the task of providing missing tokens given a surrounding context. It can boost developer productivity while providing a powerful code discovery tool. Following the Large Language Model (LLM) wave, code completion has been approached with diverse LLMs fine-tuned on code (code LLMs). The performance of code LLMs can be assessed with downstream and intrinsic metrics. Downstream metrics are usually employed to evaluate the practical utility of a model, but can be unreliable …

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…

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…

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…

APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
Tobias Vente, Michael Heep, Abdullah Abbas, Theodor Sperle, Joeran Beel, Bart Goethals
https://arxiv.org/abs/2508.19399

APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
Dataset selection is crucial for offline recommender system experiments, as mismatched data (e.g., sparse interaction scenarios require datasets with low user-item density) can lead to unreliable results. Yet, 86\% of ACM RecSys 2024 papers provide no justification for their dataset choices, with most relying on just four datasets: Amazon (38\%), MovieLens (34\%), Yelp (15\%), and Gowalla (12\%). While Algorithm Performance Spaces (APS) were proposed to guide dataset selection, their adoption h…

Zak-OTFS Based Coded Random Access for Uplink mMTC
Alessandro Mirri, Venkatesh Khammammetti, Beyza Dabak, Enrico Paolini, Krishna Narayanan, Robert Calderbank
https://arxiv.org/abs/2507.22013

Zak-OTFS Based Coded Random Access for Uplink mMTC
This paper proposes a grant-free coded random access (CRA) scheme for uplink massive machine-type communications (mMTC), based on Zak-orthogonal time frequency space (Zak-OTFS) modulation in the delay-Doppler domain. The scheme is tailored for doubly selective wireless channels, where conventional orthogonal frequency-division multiplexing (OFDM)-based CRA suffers from unreliable inter-slot channel prediction due to time-frequency variability. By exploiting the predictable nature of Zak-OTFS, t…

Beyond Line-of-Sight: Cooperative Localization Using Vision and V2X Communication
Annika Wong, Zhiqi Tang, Frank J. Jiang, Karl H. Johansson, Jonas M{\aa}rtensson
https://arxiv.org/abs/2507.20772

Beyond Line-of-Sight: Cooperative Localization Using Vision and V2X Communication
Accurate and robust localization is critical for the safe operation of Connected and Automated Vehicles (CAVs), especially in complex urban environments where Global Navigation Satellite System (GNSS) signals are unreliable. This paper presents a novel vision-based cooperative localization algorithm that leverages onboard cameras and Vehicle-to-Everything (V2X) communication to enable CAVs to estimate their poses, even in occlusion-heavy scenarios such as busy intersections. In particular, we p…

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…

LowKeyEMG: Electromyographic typing with a reduced keyset
Johannes Y. Lee, Derek Xiao, Shreyas Kaasyap, Nima R. Hadidi, John L. Zhou, Jacob Cunningham, Rakshith R. Gore, Deniz O. Eren, Jonathan C. Kao
https://arxiv.org/abs/2507.19736

LowKeyEMG: Electromyographic typing with a reduced keyset
We introduce LowKeyEMG, a real-time human-computer interface that enables efficient text entry using only 7 gesture classes decoded from surface electromyography (sEMG). Prior work has attempted full-alphabet decoding from sEMG, but decoding large character sets remains unreliable, especially for individuals with motor impairments. Instead, LowKeyEMG reduces the English alphabet to 4 gesture keys, with 3 more for space and system interaction, to reliably translate simple one-handed gestures int…

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, …

Connectivity Analysis of LoRaWAN-Based Non-Terrestrial Networks for Subterranean mMTC
Kaiqiang Lin, Mohamed-Slim Alouini
https://arxiv.org/abs/2508.19350 https://

Connectivity Analysis of LoRaWAN-Based Non-Terrestrial Networks for Subterranean mMTC
Wireless underground sensor networks (WUSNs) offer significant social and economic benefits by enabling the monitoring of subterranean entities. However, the communication reliability of WUSNs diminishes in harsh environments where terrestrial network infrastructure is either unavailable or unreliable. To address this challenge, we explore the feasibility of integrating buried massive machine-type communication (mMTC) sensors with non-terrestrial networks (NTNs), including unmanned aerial vehic…

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 …

Error Detection Based on Generalized Successive Cancellation List Decoding for Polar Codes
Alexander Sauter, Mustafa Cemil Co\c{s}kun, Gianluigi Liva
https://arxiv.org/abs/2507.16699

Error Detection Based on Generalized Successive Cancellation List Decoding for Polar Codes
Successive cancellation list (SCL) decoding has been widely adopted for polar codes, which allows near maximum likelihood performance with sufficiently large list size. In this work, we show that, if the list size is $2^γ$, where $γ$ is the fundamental quantity called mixing factor, then a modification to SCL decoding can implement Forney's generalized decoding rule. Hence, it provides an efficient means to discard unreliable decisions. The performance achieved by short polar codes under the …

Diffusion-Driven High-Dimensional Variable Selection
Minjie Wang, Xiaotong Shen, Wei Pan
https://arxiv.org/abs/2508.13890 https://arxiv.org/pdf/2508.13890

Diffusion-Driven High-Dimensional Variable Selection
Variable selection for high-dimensional, highly correlated data has long been a challenging problem, often yielding unstable and unreliable models. We propose a resample-aggregate framework that exploits diffusion models' ability to generate high-fidelity synthetic data. Specifically, we draw multiple pseudo-data sets from a diffusion model fitted to the original data, apply any off-the-shelf selector (e.g., lasso or SCAD), and store the resulting inclusion indicators and coefficients. Aggregat…

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…

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Zijian Zhang, Pan Chen, Fangshi Du, Runlong Ye, Oliver Huang, Michael Liut, Al\'an Aspuru-Guzik
https://arxiv.org/abs/2507.18945

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative st…

GeMS: Efficient Gaussian Splatting for Extreme Motion Blur
Gopi Raju Matta, Trisha Reddypalli, Vemunuri Divya Madhuri, Kaushik Mitra
https://arxiv.org/abs/2508.14682 https://

GeMS: Efficient Gaussian Splatting for Extreme Motion Blur
We introduce GeMS, a framework for 3D Gaussian Splatting (3DGS) designed to handle severely motion-blurred images. State-of-the-art deblurring methods for extreme blur, such as ExBluRF, as well as Gaussian Splatting-based approaches like Deblur-GS, typically assume access to sharp images for camera pose estimation and point cloud generation, an unrealistic assumption. Methods relying on COLMAP initialization, such as BAD-Gaussians, also fail due to unreliable feature correspondences under sever…

DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base
Song Mao, Lejun Cheng, Pinlong Cai, Guohang Yan, Ding Wang, Botian Shi
https://arxiv.org/abs/2507.14189

DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base
Large Language Models (LLMs) have demonstrated remarkable capabilities in various applications. However, their use as writing assistants in specialized domains like finance, medicine, and law is often hampered by a lack of deep domain-specific knowledge and a tendency to hallucinate. Existing solutions, such as Retrieval-Augmented Generation (RAG), can suffer from inconsistency across multiple retrieval steps, while online search-based methods often degrade quality due to unreliable web content…

ActLoc: Learning to Localize on the Move via Active Viewpoint Selection
Jiajie Li, Boyang Sun, Luca Di Giammarino, Hermann Blum, Marc Pollefeys
https://arxiv.org/abs/2508.20981 …

ActLoc: Learning to Localize on the Move via Active Viewpoint Selection
Reliable localization is critical for robot navigation, yet most existing systems implicitly assume that all viewing directions at a location are equally informative. In practice, localization becomes unreliable when the robot observes unmapped, ambiguous, or uninformative regions. To address this, we present ActLoc, an active viewpoint-aware planning framework for enhancing localization accuracy for general robot navigation tasks. At its core, ActLoc employs a largescale trained attention-base…

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 …

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Zhe Chen, Yusheng Liao, Shuyang Jiang, Zhiyuan Zhu, Haolin Li, Yanfeng Wang, Yu Wang
https://arxiv.org/abs/2508.12778

HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world diagnostics. While retrieval-augmented generation has emerged as a potential solution, current medical multimodal RAG systems are unable to perform effective retrieval across heterogeneous sources. The irrelevance of retrieved reports affects the factuality of analysis, while insufficient knowledge affects the credib…

Preprint: Did I Just Browse A Website Written by LLMs?
Sichang "Steven" He, Ramesh Govindan, Harsha V. Madhyastha
https://arxiv.org/abs/2507.13933

Preprint: Did I Just Browse A Website Written by LLMs?
Increasingly, web content is automatically generated by large language models (LLMs) with little human input. We call this "LLM-dominant" content. Since LLMs plagiarize and hallucinate, LLM-dominant content can be unreliable and unethical. Yet, websites rarely disclose such content, and human readers struggle to distinguish it. Thus, we must develop reliable detectors for LLM-dominant content. However, state-of-the-art LLM detectors are insufficient, because they perform well mainly on clean, p…

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing
Youssef Maklad, Fares Wael, Ali Hamdi, Wael Elsersy, Khaled Shaban
https://arxiv.org/abs/2508.14300

MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing
Traditional protocol fuzzing techniques, such as those employed by AFL-based systems, often lack effectiveness due to a limited semantic understanding of complex protocol grammars and rigid seed mutation strategies. Recent works, such as ChatAFL, have integrated Large Language Models (LLMs) to guide protocol fuzzing and address these limitations, pushing protocol fuzzers to wider exploration of the protocol state space. But ChatAFL still faces issues like unreliable output, LLM hallucinations, …

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…