Tootfinder

A Researcher Made an AI That Completely Breaks the Online Surveys Scientists Rely On: «We can no longer trust that survey responses are coming from real people," Westwood said in a press release. "With survey data tainted by bots, AI can poison the entire knowledge ecosystem.”»
It's not like online surveys were particularly reliable before 😂
https://www.404media.co/a-researcher-made-an-ai-that-completely-breaks-the-online-surveys-scientists-rely-on/

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
Nico Bistolfi, Andreea Georgescu, Dave Hodson
https://arxiv.org/abs/2510.09494 …

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
As cloud infrastructure evolves to support dynamic and distributed workflows, accelerated now by AI-driven processes, the outdated model of standing permissions has become a critical vulnerability. Based on the Cloud Security Alliance (CSA) Top Threats to Cloud Computing Deep Dive 2025 Report, our analysis details how standing permissions cause catastrophic cloud breaches. While current security tools are addressing network and API security, the challenge of securing granular data access remain…

The thing about a life-logger, is you input sensitive data about your life, lifestyle and activities, so privacy and data-integrity are some of the most important issues.
There can be no server, the data has to be yours and yours alone. Because you can’t tell what is happening to the data in a closed-source app, it must be completely free and open source.
You can’t trust a corporate diary, they must sell to anyone offering enough money.
So it is with my life log app, all data completely in your own device. No home server ever sees anything.
There is no home server. Just the code.
To achieve this Exocortex Log is a Progressive Web App. It downloads when you are online at the website and can be installed onto the homepage of your phone.
It keeps all data on the local device using indexdb.
This means you must be responsible for your own backups. Be sure to export and back up your data regularly. I have gaps in my ten year record where my phone was stolen and most recent backup was months prior.
Once installed it will work offline, airplane mode, no internet, down in the tube station at midnight, anywhere.
There's a blog on the website saying this and more: https://exocortexlog.com/news/articles/2025-12-06-release/

Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse
Jacopo Tagliabue, Ciro Greco
https://arxiv.org/abs/2510.09567 https://a…

Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse
Data lakehouses run sensitive workloads, where AI-driven automation raises concerns about trust, correctness, and governance. We argue that API-first, programmable lakehouses provide the right abstractions for safe-by-design, agentic workflows. Using Bauplan as a case study, we show how data branching and declarative environments extend naturally to agents, enabling reproducibility and observability while reducing the attack surface. We present a proof-of-concept in which agents repair data pip…

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…

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Berlin Buzzwords is Europe’s leading conference for modern data infrastructure, search and machine learning and is focused on open source software projects.
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The Right to Communications Confidentiality in Europe: Protecting Privacy, Freedom of Expression, and Trust
Frederik J. Zuiderveen Borgesius, Wilfred Steenbruggen
https://arxiv.org/abs/2510.08247

The Right to Communications Confidentiality in Europe: Protecting Privacy, Freedom of Expression, and Trust
In the European Union, the General Data Protection Regulation (GDPR) provides comprehensive rules for the processing of personal data. In addition, the EU lawmaker intends to adopt specific rules to protect confidentiality of communications, in a separate ePrivacy Regulation. Some have argued that there is no need for such additional rules for communications confidentiality. This Article discusses the protection of the right to confidentiality of communications in Europe. We look at the right's…

KnowRL: Teaching Language Models to Know What They Know
Sahil Kale, Devendra Singh Dhami
https://arxiv.org/abs/2510.11407 https://arxiv.org/pdf/2510.11407

KnowRL: Teaching Language Models to Know What They Know
Truly reliable AI requires more than simply scaling up knowledge; it demands the ability to know what it knows and when it does not. Yet recent research shows that even the best LLMs misjudge their own competence in more than one in five cases, making any response born of such internal uncertainty impossible to fully trust. Inspired by self-improvement reinforcement learning techniques that require minimal data, we present a simple but powerful framework KnowRL that strengthens a model's intern…

Structure-aware Propagation Generation with Large Language Models for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou, Songlin Hu
https://arxiv.org/abs/2510.12125 https…

Structure-aware Propagation Generation with Large Language Models for Fake News Detection
The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how information spreads, they often suffer from incomplete propagation data. Recent work leverages large language models (LLMs) to generate synthetic propagation, but typically overlooks the structural patterns of real-world discussions. In this paper, we propose a novel structure-aware synthetic propagation enhanced detec…

Data center real estate investment trust Fermi, co-founded by former US Energy Secretary Rick Perry, closed up 55% in its Nasdaq debut, valuing Fermi at ~$19.3B (Bloomberg)
https://www.bloomberg.com/news/articles/2025-10-01/ric…

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Bogdan Raoni\'c, Siddhartha Mishra, Samuel Lanthaler
https://arxiv.org/abs/2509.25080 https://

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI
Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out-of-distribution (OOD) data, but detecting such failures in regression tasks is an open challenge. We propose a new OOD detection method based on estimating joint likelihoods using a score-based diffusion model. This approach considers not just the input but also the regression model's prediction, providing a task-aware reliability score. Across num…

🙋‍♂️ What the fuck is wrong with you?
❓ Have you ever seen a bandwagon you didn’t want to jump on?
💩 Can you please be less shit? https://ec.social-network.europa.eu/@EUCommission/115643458015221823

ActorDB: A Unified Database Model Integrating Single-Writer Actors, Incremental View Maintenance, and Zero-Trust Messaging
Jun Kawasaki
https://arxiv.org/abs/2509.25285 https://…

ActorDB: A Unified Database Model Integrating Single-Writer Actors, Incremental View Maintenance, and Zero-Trust Messaging
This paper presents ActorDB ( Dekigoto ) , a novel database architecture that tightly integrates a single-writer actor model for writes, Incremental View Maintenance (IVM), and a zero-trust security model as a core component. The primary contribution of this work is the unification of these powerful but complex concepts into a single, cohesive system designed to reduce architectural complexity for developers of modern, data-intensive applications. We argue that by providing these capabilities o…

Interplay between Security, Privacy and Trust in 6G-enabled Intelligent Transportation Systems
Ahmed Danladi Abdullahi, Erfan Bahrami, Tooska Dargahi, Mohammed Al-Khalidi, Mohammad Hammoudeh
https://arxiv.org/abs/2510.02487

Interplay between Security, Privacy and Trust in 6G-enabled Intelligent Transportation Systems
The advancement of 6G technology has the potential to revolutionize the transportation sector and significantly improve how we travel. 6G-enabled Intelligent Transportation Systems (ITS) promise to offer high-speed, low-latency communication and advanced data analytics capabilities, supporting the development of safer, more efficient, and more sustainable transportation solutions. However, various security and privacy challenges were identified in the literature that must be addressed to enable…

When Clear Skies Cloud Trust: Environmental Cues and the Paradox of Confidence in Government
Xiangzhe Xu, Ran Wu
https://arxiv.org/abs/2509.23554 https://a…

When Clear Skies Cloud Trust: Environmental Cues and the Paradox of Confidence in Government
Government trust, as a core concept in political economy and public policy research, serves as a fundamental cornerstone of democratic legitimacy and state capacity. This paper examines how environmental conditions, particularly sunlight efficiency, influence reported government trust through both affective and cognitive mechanisms. Leveraging World Values Survey Wave 7 data merged with NASA POWER high-frequency weather data, we propose and validate a novel ``salience and attribution'' mechanis…

🎨 Perfect for #AI agents needing grounded web context and research tools demanding trust and freshness
🚀 Enables custom products where developers want complete control over how search data is used
📊 Structured response format eliminates need for complex data parsing and preprocessing steps
🌐

Swarm Oracle: Trustless Blockchain Agreements through Robot Swarms
Alexandre Pacheco, Hanqing Zhao, Volker Strobel, Tarik Roukny, Gregory Dudek, Andreagiovanni Reina, Marco Dorigo
https://arxiv.org/abs/2509.15956

Swarm Oracle: Trustless Blockchain Agreements through Robot Swarms
Blockchain consensus, rooted in the principle ``don't trust, verify'', limits access to real-world data, which may be ambiguous or inaccessible to some participants. Oracles address this limitation by supplying data to blockchains, but existing solutions may reduce autonomy, transparency, or reintroduce the need for trust. We propose Swarm Oracle: a decentralized network of autonomous robots -- that is, a robot swarm -- that use onboard sensors and peer-to-peer communication to collectively ver…

Weekend Reads
* Monitoring AS-SETs
https://blog.cloudflare.com/monitoring-as-sets-and-why-they-matter/
* IX LAN broadcast traffic

Monitoring AS-SETs and why they matter
AS-SETs, for better or worse, are critical data sources that help build BGP route filters across the Internet. Because these AS-SET objects are based solely on trust, they can be misused. We will cover some of the reasons why operators need to monitor the AS-SET memberships for their ASN, and now Cloudflare Radar can help.

The hyperscalers' plans for powering their AI datacentres are mostly nuclear, including investing in small modular reactors.
I'm all for that, as long as they don't externalise all the negatives to governments and leave them (us) holding the bag of deadly waste when the bubble bursts.
That sounds too negative. I am genuinely pro-nuclear energy and look forward to the research, just, I don't trust billionaires.
"LINX125: AI Mega Data Centers – Power From…

FHEON: A Configurable Framework for Developing Privacy-Preserving Neural Networks Using Homomorphic Encryption
Nges Brian Njungle, Eric Jahns, Michel A. Kinsy
https://arxiv.org/abs/2510.03996

FHEON: A Configurable Framework for Developing Privacy-Preserving Neural Networks Using Homomorphic Encryption
The widespread adoption of Machine Learning as a Service raises critical privacy and security concerns, particularly about data confidentiality and trust in both cloud providers and the machine learning models. Homomorphic Encryption (HE) has emerged as a promising solution to this problems, allowing computations on encrypted data without decryption. Despite its potential, existing approaches to integrate HE into neural networks are often limited to specific architectures, leaving a wide gap in…

Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy
Ivan Reyes-Amezcua, Francisco Lopez-Tiro, Cl\'ement Larose, Christian Daul, Andres Mendez-Vazquez, Gilberto Ochoa-Ruiz
https://arxiv.org/abs/2510.06230

Robust Federated Anomaly Detection Using Dual-Signal Autoencoders: Application to Kidney Stone Identification in Ureteroscopy
This work introduces Federated Adaptive Gain via Dual Signal Trust (FedAgain), a novel federated learning algorithm designed to enhance anomaly detection in medical imaging under decentralized and heterogeneous conditions. Focusing on the task of kidney stone classification, FedAgain addresses the common challenge of corrupted or low-quality client data in real-world clinical environments by implementing a dual-signal trust mechanism based on reconstruction error and model divergence. This mech…

Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach
Shoeb Joarder, Mohamed Amine Chatti
https://arxiv.org/abs/2510.08395

Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach
The aim of learning analytics is to turn educational data into insights, decisions, and actions to improve learning and teaching. The reasoning of the provided insights, decisions, and actions is often not transparent to the end-user, and this can lead to trust and acceptance issues when interventions, feedback, and recommendations fail. In this paper, we shed light on achieving transparent learning analytics by following a transparency through exploration approach. To this end, we present the …

Trust and Human Autonomy after Cobot Failures: Communication is Key for Industry 5.0
Felix Glawe, Laura Kremer, Luisa Vervier, Philipp Brauner, Martina Ziefle
https://arxiv.org/abs/2509.22298

Trust and Human Autonomy after Cobot Failures: Communication is Key for Industry 5.0
Collaborative robots (cobots) are a core technology of Industry 4.0. Industry 4.0 uses cyber-physical systems, IoT and smart automation to improve efficiency and data-driven decision-making. Cobots, as cyber-physical systems, enable the introduction of lightweight automation to smaller companies through their flexibility, low cost and ability to work alongside humans, while keeping humans and their skills in the loop. Industry 5.0, the evolution of Industry 4.0, places the worker at the centre …

Crosslisted article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/2]:
- Trust and Transparency in AI: Industry Voices on Data, Ethics, and Compliance
Louise McCormack, Diletta Huyskes, Dave Lewis, Malika Bendechache

Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models
Kang Wei, Xin Yuan, Fushuo Huo, Chuan Ma, Long Yuan, Songze Li, Ming Ding, Dacheng Tao
https://arxiv.org/abs/2509.22723

Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models
Diffusion models (DMs) have been investigated in various domains due to their ability to generate high-quality data, thereby attracting significant attention. However, similar to traditional deep learning systems, there also exist potential threats to DMs. To provide advanced and comprehensive insights into safety, ethics, and trust in DMs, this survey comprehensively elucidates its framework, threats, and countermeasures. Each threat and its countermeasures are systematically examined and cate…

Oh-Trust: Overbooking and Hybrid Trading-empowered Resource Scheduling with Smart Reputation Update over Dynamic Edge Networks
Houyi Qi, Minghui Liwang, Liqun Fu, Xianbin Wang, Huaiyu Dai, Xiaoyu Xia
https://arxiv.org/abs/2509.25683

Oh-Trust: Overbooking and Hybrid Trading-empowered Resource Scheduling with Smart Reputation Update over Dynamic Edge Networks
Incentive-driven computing resource sharing is crucial for meeting the ever-growing demands of emerging mobile applications. Although conventional spot trading offers a solution, it frequently leads to excessive overhead due to the need for real-time trading related interactions. Likewise, traditional futures trading, which depends on historical data, is susceptible to risks from network dynamics. This paper explores a dynamic and uncertain edge network comprising a computing platform, e.g., an…

Benchmarking Offline Reinforcement Learning for Emotion-Adaptive Social Robotics
Soon Jynn Chu, Raju Gottumukkala, Alan Barhorst
https://arxiv.org/abs/2509.16858 https://…

Benchmarking Offline Reinforcement Learning for Emotion-Adaptive Social Robotics
The ability of social robots to respond to human emotions is crucial for building trust and acceptance in human-robot collaborative environments. However, developing such capabilities through online reinforcement learning is sometimes impractical due to the prohibitive cost of data collection and the risk of generating unsafe behaviors. In this paper, we study the use of offline reinforcement learning as a practical and efficient alternative. This technique uses pre-collected data to enable emo…

What is #bbuzz all about?
Our video team captured the essence of #bbuzz on film: #bbuzz by joining us as a speaker! We are looking for talks on search, data science, streams, operations, scale, storage, people and community, society, ethics, sustainability, and more: https://2026.berlinbuzzwords.de/call-for-papers/

Can You Trust Your Copilot? A Privacy Scorecard for AI Coding Assistants
Amir AL-Maamari
https://arxiv.org/abs/2509.20388 https://arxiv.org/pdf/2509.20388

Can You Trust Your Copilot? A Privacy Scorecard for AI Coding Assistants
The rapid integration of AI-powered coding assistants into developer workflows has raised significant privacy and trust concerns. As developers entrust proprietary code to services like OpenAI's GPT, Google's Gemini, and GitHub Copilot, the unclear data handling practices of these tools create security and compliance risks. This paper addresses this challenge by introducing and applying a novel, expert-validated privacy scorecard. The methodology involves a detailed analysis of four document ty…

Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas
Mengisti Berihu Girmay, Jakob Droste, Hannah Deters, Joerg Doerr
https://arxiv.org/abs/2509.16249

Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas
Artificial Intelligence (AI) promises new opportunities across many domains, including agriculture. However, the adoption of AI systems in this sector faces several challenges. System complexity can impede trust, as farmers' livelihoods depend on their decision-making and they may reject opaque or hard-to-understand recommendations. Data privacy concerns also pose a barrier, especially when farmers lack transparency regarding who can access their data and for what purposes. This paper examine…