Tootfinder

Convexity in tensor triangular geometry
Changhan Zou
https://arxiv.org/abs/2506.12429 https://arxiv.org/pdf/2506.12429

Convexity in tensor triangular geometry
We classify the dualizable localizing ideals of rigidly-compactly generated tt-$\infty$-categories that are cohomologically stratified. By definition, these are the localizing ideals that are dualizable with respect to the Lurie tensor product. We prove that these ideals correspond to the convex subsets of the Balmer spectrum. More generally, we establish this classification for categories which are locally cohomologically stratified and whose Balmer spectrum is noetherian. The classification t…

This https://arxiv.org/abs/2502.04097 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Impermanent loss and Loss-vs-Rebalancing II
This paper examines the relationship between impermanent loss (IL) and loss-versus-rebalancing (LVR) in automated market makers (AMMs). Our main focus is on statistical properties, the impact of fees, the role of block times, and, related to the latter, the continuous time limit. We find there are three relevant regimes: (i) very short times where LVR and IL are identical; (ii) intermediate time where LVR and IL show distinct distribution functions but are connected via the central limit theore…

Unstable $1$-semiadditivity as classifying Goodwillie towers
Connor Malin
https://arxiv.org/abs/2506.11245 https://arxiv.org/pdf/2506…

Unstable $1$-semiadditivity as classifying Goodwillie towers
A stable $\infty$-category is $1$-semiadditive if all norms for finite group actions are equivalences. In the presence of $1$-semiadditivity, Goodwillie calculus simplifies drastically. We introduce two unstable analogs of $1$-semiadditivity for an $\infty$-category $C$ and study their relation to the Goodwillie calculus of functors $C \rightarrow \s(C)$. We demonstrate these qualities are complete obstructions to the problem of endowing $\partial_\ast F$ with either a right module or a divided…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
Improving Large Language Models with Concept-Aware Fine-Tuning
https://

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
Fish feeding behavior recognition and intensity quantification methods in aquaculture: From singl...

[2025-06-17 Tue (UTC), no new articles found for cs.PF Performance]
toXiv_bot_toot

Recent Advances and Future Directions in Literature-Based Discovery
Andrej Kastrin, Bojan Cestnik, Nada Lavra\v{c}
https://arxiv.org/abs/2506.12385 https:/…

Recent Advances and Future Directions in Literature-Based Discovery
The explosive growth of scientific publications has created an urgent need for automated methods that facilitate knowledge synthesis and hypothesis generation. Literature-based discovery (LBD) addresses this challenge by uncovering previously unknown associations between disparate domains. This article surveys recent methodological advances in LBD, focusing on developments from 2000 to the present. We review progress in three key areas: knowledge graph construction, deep learning approaches, an…

Please note that the photo below, which is making the rounds, is from 1987 and not 2025; Golden Gate anniversary not a protest. See this page:
https://www.vintag.es/2013/09/pictures-of-golden-gate-bridge-50th.html

Replaced article(s) found for cs.CC. https://arxiv.org/list/cs.CC/new
[1/1]:
Limit-sure reachability for small memory policies in POMDPs is NP-complete

Knapsack and Shortest Path Problems Generalizations From A Quantum-Inspired Tensor Network Perspective
Sergio Mu\~niz Subi\~nas, Jorge Mart\'inez Mart\'in, Alejandro Mata Ali, Javier Sedano, \'Angel Miguel Garc\'ia-Vico
https://arxiv.org/abs/2506.11711

Knapsack and Shortest Path Problems Generalizations From A Quantum-Inspired Tensor Network Perspective
In this paper, we present two tensor network quantum-inspired algorithms to solve the knapsack and the shortest path problems, and enables to solve some of its variations. These methods provide an exact equation which returns the optimal solution of the problems. As in other tensor network algorithms for combinatorial optimization problems, the method is based on imaginary time evolution and the implementation of restrictions in the tensor network. In addition, we introduce the use of symmetrie…

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Shova Kuikel, Aritran Piplai, Palvi Aggarwal
https://arxiv.org/abs/2506.13746

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) sho…

This https://arxiv.org/abs/2501.17707 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

vNV-Heap: An Ownership-Based Virtually Non-Volatile Heap for Embedded Systems
The Internet of Batteryless Things might revolutionize our understanding of connected devices by harvesting required operational energy from the environment. These systems come with the system-software challenge that the intermittently powered IoT devices have to checkpoint their state in non-volatile memory to later resume with this state when sufficient energy is available. The scarce energy resources demand that only modified data is persisted before a power failure, which requires precise m…

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools
Philippe J. Giabbanelli, John Beverley, Istvan David, Andreas Tolk
https://arxiv.org/abs/2506.11141

From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools
Large Language Models (LLMs) offer transformative potential for Modeling & Simulation (M&S) through natural language interfaces that simplify workflows. However, over-reliance risks compromising quality due to ambiguities, logical shortcuts, and hallucinations. This paper advocates integrating LLMs as middleware or translators between specialized tools to mitigate complexity in M&S tasks. Acting as translators, LLMs can enhance interoperability across multi-formalism, multi-semantics, and multi…

Die Bilder und Videos von den #NoKing Protesten haben mir übrigens Hoffnung gemacht.
https://omfg.town/@dansinker/114690663638438881
Gesehen habe ich sie auf imgur.…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
Gaussian Process Regression for Inverse Problems in Linear PDEs
ht…

Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
Frederic Gmeiner, Kaitao Luo, Ye Wang, Kenneth Holstein, Nikolas Martelaro
https://arxiv.org/abs/2506.12879

Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. …

Replaced article(s) found for astro-ph.EP. https://arxiv.org/list/astro-ph.EP/new
[1/1]:
A Next-Generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTR...

Secure API-Driven Research Automation to Accelerate Scientific Discovery
Tyler J. Skluzacek, Paul Bryant, A. J. Ruckman, Daniel Rosendo, Suzanne Prentice, Michael J. Brim, Ryan Adamson, Sarp Oral, Mallikarjun Shankar, Rafael Ferreira da Silva
https://arxiv.org/abs/2506.11950

Secure API-Driven Research Automation to Accelerate Scientific Discovery
The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M revolutionizes programmatic access to high performance computing (HPC) while maintaining uncompromising security. This framework allows intelligent agents and experimental facilities to dyn…

Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?
Noemi Dreksler, Lucius Caviola, David Chalmers, Carter Allen, Alex Rand, Joshua Lewis, Philip Waggoner, Kate Mays, Jeff Sebo
https://arxiv.org/abs/2506.11945

Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?
We surveyed 582 AI researchers who have published in leading AI venues and 838 nationally representative US participants about their views on the potential development of AI systems with subjective experience and how such systems should be treated and governed. When asked to estimate the chances that such systems will exist on specific dates, the median responses were 1% (AI researchers) and 5% (public) by 2024, 25% and 30% by 2034, and 70% and 60% by 2100, respectively. The median member of th…

The Moduli Stack of Breuil-Kisin Modules with Descent Data for Reductive Groups
Eivind Otto Hjelle
https://arxiv.org/abs/2506.11910 https://

The Moduli Stack of Breuil-Kisin Modules with Descent Data for Reductive Groups
We introduce and study the moduli stack $\mathcal{Y}$ of Breuil-Kisin modules with $\hat{G}$-structure and descent data, or Breuil-Kisin $(Γ,\hat{G})$-torsors for short. Specifically, for a dominant cocharacter $μ$, we define the moduli stack $\mathcal{Y}^{\leq μ}$ of Breuil-Kisin $(Γ,\hat{G})$-torsors with Hodge-Tate weights bounded by $μ$. We prove that $\mathcal{Y}^{\leq μ}$ is a $p$-adic formal algebraic stack, and show that it is smoothly equivalent to (the $p$-adic completion of) a …

This https://arxiv.org/abs/2410.00854 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Impermanent loss and loss-vs-rebalancing I: some statistical properties
There are two predominant metrics to assess the performance of automated market makers and their profitability for liquidity providers: 'impermanent loss' (IL) and 'loss-versus-rebalance' (LVR). In this short paper we shed light on the statistical aspects of both concepts and show that they are more similar than conventionally appreciated. Our analysis uses the properties of a random walk and some analytical properties of the statistical integral combined with the mechanics of a constant functi…

On pure monomorphisms and pure epimorphisms in accessible categories
Leonid Positselski
https://arxiv.org/abs/2506.13374 https://arxi…

On pure monomorphisms and pure epimorphisms in accessible categories
In all $κ$-accessible additive categories, $κ$-pure monomorphisms and $κ$-pure epimorphisms are well-behaved, as shown in our previous paper arXiv:2311.02418. This is known to be not always true in $κ$-accessible nonadditive categories. Nevertheless, mild assumptions on a $κ$-accessible category are sufficient to prove good properties of $κ$-pure monomorphisms and $κ$-pure epimorphisms. In particular, in a $κ$-accessible category with finite products, all $κ$-pure monomorphisms are $κ…

Gradients of unitary optical neural networks using parameter-shift rule
Jinzhe Jiang, Yaqian Zhao, Xin Zhang, Chen Li, Yunlong Yu, Hailing Liu
https://arxiv.org/abs/2506.11565

Gradients of unitary optical neural networks using parameter-shift rule
This paper explores the application of the parameter-shift rule (PSR) for computing gradients in unitary optical neural networks (UONNs). While backpropagation has been fundamental to training conventional neural networks, its implementation in optical neural networks faces significant challenges due to the physical constraints of optical systems. We demonstrate how PSR, which calculates gradients by evaluating functions at shifted parameter values, can be effectively adapted for training UONNs…

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
EvalNet: A Practical Toolchain for Generation and Analysis of Extreme-Scale Interconnects

ExtendAttack: Attacking Servers of LRMs via Extending Reasoning
Zhenhao Zhu, Yue Liu, Yingwei Ma, Hongcheng Gao, Nuo Chen, Yanpei Guo, Wenjie Qu, Huiying Xu, Xinzhong Zhu, Jiaheng Zhang
https://arxiv.org/abs/2506.13737

ExtendAttack: Attacking Servers of LRMs via Extending Reasoning
Large Reasoning Models (LRMs) have demonstrated promising performance in complex tasks. However, the resource-consuming reasoning processes may be exploited by attackers to maliciously occupy the resources of the servers, leading to a crash, like the DDoS attack in cyber. To this end, we propose a novel attack method on LRMs termed ExtendAttack to maliciously occupy the resources of servers by stealthily extending the reasoning processes of LRMs. Concretely, we systematically obfuscate characte…

Spectral Estimation with Free Decompression
Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Michael W. Mahoney
https://arxiv.org/abs/2506.11994 https:…

Spectral Estimation with Free Decompression
Computing eigenvalues of very large matrices is a critical task in many machine learning applications, including the evaluation of log-determinants, the trace of matrix functions, and other important metrics. As datasets continue to grow in scale, the corresponding covariance and kernel matrices become increasingly large, often reaching magnitudes that make their direct formation impractical or impossible. Existing techniques typically rely on matrix-vector products, which can provide efficient…

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Vincent Lannurien, Cam\'elia Slimani, Louis Morge-Rollet, Laurent Lemarchand, David Espes, Fr\'ed\'eric Le Roy, Jalil Boukhobza
https://arxiv.org/abs/2506.11800

A retrospective on DISPEED -- Leveraging heterogeneity in a drone swarm for IDS execution
Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems ((N)IDS) are promising solutions to detect malicious behavior on network traffic. However, modern NIDS rely on resource-hungry machine learning techniques, that can be difficult to deploy on a swarm of drones. The goal of the DISPEED project is to leverage the heterogeneity (exec…

Development of a Smart Autonomous Irrigation System Using Iot and AI
Yunus Emre Kunt
https://arxiv.org/abs/2506.11835 https://arxiv.o…

Development of a Smart Autonomous Irrigation System Using Iot and AI
Agricultural irrigation ensures that the water required for plant growth is delivered to the soil in a controlled manner. However, uncontrolled management can lead to water waste while reducing agricultural productivity. Drip irrigation systems, which have been one of the most efficient methods since the 1970s, are modernised with IoT and artificial intelligence in this study, aiming to both increase efficiency and prevent water waste. The developed system is designed to be applicable to differ…

On the sum of a prime and a square-free number with divisibility conditions
Ethan S. Lee, Rowan O'Clarey
https://arxiv.org/abs/2506.11814 https://

On the sum of a prime and a square-free number with divisibility conditions
We prove that every natural number greater than $2$ can be written as the sum of a prime and a square-free number that is co-prime to any prime $q > 3$. We also demonstrate that this result can be extended in a natural way, by assuming the Generalised Riemann Hypothesis is true.

This https://arxiv.org/abs/2406.12417 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Fees in AMMs: A quantitative study
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called 'informed orderflow'. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we…

Torsion Theories in a Non-pointed Context
Andrea Cappelletti, Andrea Montoli
https://arxiv.org/abs/2506.13349 https://arxiv.org/pdf/2…

Torsion Theories in a Non-pointed Context
We study a non-pointed version of the notion of torsion theory in the framework of categories equipped with a posetal monocoreflective subcategory such that the coreflector inverts monomorphisms. We explore the connections of such torsion theories with factorization systems and categorical Galois structures. We describe several examples of these torsion theories, in the dual of elementary toposes, in varieties of universal algebras used as models for non-classical logic, and in coslices of the …

[2025-06-16 Mon (UTC), 1 new article found for cs.ET Emerging Technologies]
toXiv_bot_toot

[2025-06-16 Mon (UTC), no new articles found for cs.PF Performance]
toXiv_bot_toot

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Zhiqiang Li, Haiyong Bao, Menghong Guan, Hao Pan, Cheng Huang, Hong-Ning Dai
https://arxiv.org/abs/2506.13612

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and R…

This https://arxiv.org/abs/2404.03976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

A theoretical framework for fees in AMMs
In the ever evolving landscape of decentralized finance automated market makers (AMMs) play a key role: they provide a market place for trading assets in a decentralized manner. For so-called bluechip pairs, arbitrage activity provides a major part of the revenue generation of AMMs but also a major source of loss due to the so-called informed orderflow. Finding ways to minimize those losses while still keeping uninformed trading activity alive is a major problem in the field. In this paper we w…

Bounded Memory in Distributed Networks
Ran Ben Basat, Keren Censor-Hillel, Yi-Jun Chang, Wenchen Han, Dean Leitersdorf, Gregory Schwartzman
https://arxiv.org/abs/2506.11644

Bounded Memory in Distributed Networks
The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and practice that prevent the smooth adaptation of CONGEST algorithms to these environments. In this paper, we focus on the memory restrictions that arise in real-world deployments. We introduce the $μ$-CONGEST model where on top of the bandwidth restriction, the memory of nodes is also limited to $μ$ words, in line with rea…

Designing Effective LLM-Assisted Interfaces for Curriculum Development
Abdolali Faraji, Mohammadreza Tavakoli, Mohammad Moein, Mohammadreza Molavi, G\'abor Kismih\'ok
https://arxiv.org/abs/2506.11767

Designing Effective LLM-Assisted Interfaces for Curriculum Development
Large Language Models (LLMs) have the potential to transform the way a dynamic curriculum can be delivered. However, educators face significant challenges in interacting with these models, particularly due to complex prompt engineering and usability issues, which increase workload. Additionally, inaccuracies in LLM outputs can raise issues around output quality and ethical concerns in educational content delivery. Addressing these issues requires careful oversight, best achieved through coopera…

On generalised Pythagorean triples over number fields
Pedro-Jos\'e Cazorla Garc\'ia
https://arxiv.org/abs/2506.11636 https://…

On generalised Pythagorean triples over number fields
Generalised Pythagorean triples are integer tuples $(x,y,z)$ satisfying the equation $E_{a,b,c}: ax^2+by^2+cz^2=0$. A significant amount of research has been devoted towards understanding generalised Pythagorean triples and, in particular, we can now determine whether $E_{a,b,c}$ has solutions and find them in a computationally effective manner. In this paper, we consider an extension of generalised Pythagorean triples to number fields $K$. In particular, we survey and extend the existing res…

Integration of a categorical operad
Dominik Trnka
https://arxiv.org/abs/2506.12399 https://arxiv.org/pdf/2506.12399

Integration of a categorical operad
I describe Grothendieck construction for non-symmetric operads with values in categories, and hence in groupoids and posets. The construction produces a 2-category which is operadicaly fibered over the category $Δ_s$ of finite non-empty ordinals and surjections. I describe an inverse for the construction, yielding a 1-1 correspondence of non-symmetric categorical operads and operadic 2-categories (split-)fibered over $Δ_s$, which resembles the correspondence of categorical presheaves and fibe…

Unlearning-Enhanced Website Fingerprinting Attack: Against Backdoor Poisoning in Anonymous Networks
Yali Yuan, Kai Xu, Ruolin Ma, Yuchen Zhang
https://arxiv.org/abs/2506.13563

Unlearning-Enhanced Website Fingerprinting Attack: Against Backdoor Poisoning in Anonymous Networks
Website Fingerprinting (WF) is an effective tool for regulating and governing the dark web. However, its performance can be significantly degraded by backdoor poisoning attacks in practical deployments. This paper aims to address the problem of hidden backdoor poisoning attacks faced by Website Fingerprinting attack, and designs a feasible mothed that integrates unlearning technology to realize detection of automatic poisoned points and complete removal of its destructive effects, requiring onl…

Capsule: Efficient Player Isolation for Datacenters
Zhouheng Du, Nima Davari, Li Li, Nodir Kodirov
https://arxiv.org/abs/2506.11483 https://

Capsule: Efficient Player Isolation for Datacenters
Cloud gaming is increasingly popular. A challenge for cloud provider is to keep datacenter utilization high: a non-trivial task due to application variety. These applications come in different shapes and sizes. So do cloud datacenter resources, e.g., CPUs, GPUs, NPUs. Part of the challenge stems from game engines being predominantly designed to run only one player. One player in a lightweight game might utilize only a fraction of the cloud server GPU. The remaining GPU capacity will be left u…

Stackification via adjunction
Zheng Wei
https://arxiv.org/abs/2506.12050 https://arxiv.org/pdf/2506.12050

Stackification via adjunction
We establish a form of 2-adjunction (tentatively termed the *fundamental 2-adjunction*), building on the fundamental adjunction proposed by Olivia Caramello and Riccardo Zanfa, which provides a constructive method for the associated stack functor. Additionally, we investigate 2-local homeomorphisms through the lens of indexed fibrations.

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
Xiao Zhan, Juan Carlos Carrillo, William Seymour, Jose Such
https://arxiv.org/abs/2506.11680

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information
LLM-based Conversational AIs (CAIs), also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs. Recent research has demonstrated that LLM-based CAIs could be used for malicious purposes. However, a novel and particularly concerning type of malicious LLM application remains unexplored: an LLM-based CAI that is deliberately designed to extract personal infor…

Mechanisms of information communication and market price movements. The case of SP 500 market
Inga Ivanova, Grzegorz Rzadkowski
https://arxiv.org/abs/2505.09625

Mechanisms of information communication and market price movements. The case of SP 500 market
In this paper we analyze how market prices change in response to information processing among the market participants and how non-linear information dynamics drive market price movement. We analyze historical data of the SP 500 market for the period 1950 -2025 using the logistic Continuous Wavelet Transformation method. This approach allows us to identify various patterns in market dynamics. These patterns are conceptualized using a new theory of reflexive communication of information in a mark…

A geometric determinant method and geometric dimension growth
Tijs Buggenhout, Yotam I. Hendel, Floris Vermeulen
https://arxiv.org/abs/2506.11624 https://

A geometric determinant method and geometric dimension growth
We study a geometric version of the dimension growth conjecture. Let $X$ be an irreducible projective variety defined over $\mathbb{C}(t)$ of dimension $m$ and degree $d \geq 2$. The set $X(b)$ of $\mathbb{C}(t)$-rational points of degree smaller than $b$ lying on $X$, has a natural structure of an algebraic variety over $\mathbb{C}$. We study the dimension and irreducibility of this variety for each $b$, and obtain a geometric analogue of the classical dimension growth conjecture in this case,…

Watermarking LLM-Generated Datasets in Downstream Tasks
Yugeng Liu, Tianshuo Cong, Michael Backes, Zheng Li, Yang Zhang
https://arxiv.org/abs/2506.13494 ht…

Watermarking LLM-Generated Datasets in Downstream Tasks
Large Language Models (LLMs) have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated data to train their models. However, the inability to track content produced by LLMs poses a significant challenge, potentially leading to copyright infringement for the LLM owners. In this paper, we pr…

[2025-06-17 Tue (UTC), 5 new articles found for math.CT Category Theory]
toXiv_bot_toot

The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today
Nicolas Houli\'e
https://arxiv.org/abs/2505.09620

The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today
I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic factors (MEF), including an inflation metric (CPI), US treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liqu…

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
Ziyi Zhang, Ziheng Jiang, Chengquan Jiang, Menghan Yu, Size Zheng, Haibin Lin, Henry Hoffmann, Xin Liu
https://arxiv.org/abs/2506.11309

SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
Low-latency decoding for large language models (LLMs) is crucial for applications like chatbots and code assistants, yet generating long outputs remains slow in single-query settings. Prior work on speculative decoding (which combines a small draft model with a larger target model) and tensor parallelism has each accelerated decoding. However, conventional approaches fail to apply both simultaneously due to imbalanced compute requirements (between draft and target models), KV-cache inconsistenc…

Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption:A Simulation-Driven Systematic Framework
Wei Meng
https://arxiv.org/abs/2506.11645

Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption:A Simulation-Driven Systematic Framework
This study constructs a quantifiable modelling framework to simulate non-kinetic strategic deterrence pathways in rare earth supply disruption scenarios, based on structured responses from expert interviews led by Dr. Daniel O'Connor, CEO of the Rare Earth Exchange (REE). Focusing on disruption impacts on national security systems, the study proposes four core modelling components: Security Critical Zones (SCZ), Strategic Signal Injection Function (SSIF), System-Capability Migration Function (S…

Equations defining Jacobians with Real Multiplication
Rahul Mistry, Ramesh Sreekantan
https://arxiv.org/abs/2506.11459 https://arxiv.…

Equations defining Jacobians with Real Multiplication
If $C:y^2=x(x-1)(x-a_1)(x-a_2)(x-a_3)$ is genus $2$ curve a natural question to ask is: Under what conditions on $a_1,a_2,a_3$ does the Jacobian $J(C)$ have real multiplication by $\mathbb{Z}[\sqrtΔ]$ for some $Δ>0$. Over a hundred years ago Humbert gave an answer to this question for $Δ=5$ and $Δ=8$. In this paper we use work of Birkenhake and Wilhelm along with some classical results in enumerative geometry to generalize this to all discriminants, in principle. We also work it out explici…

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings
https://arxiv.org/abs/2506.13434

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity fram…

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
Double groupoids and $2$-groupoids in regular Mal'tsev categories

[2025-06-16 Mon (UTC), no new articles found for q-fin.ST Statistical Finance]
toXiv_bot_toot

[2025-06-16 Mon (UTC), 5 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
toXiv_bot_toot

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
Naresh Tiwari
https://arxiv.org/abs/2506.11412 https://…

The Strategic Imperative for Healthcare Organizations to Build Proprietary Foundation Models
This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and th…

A survey of The Prouhet-Tarry-Escott Problem and its Generalizations
Chen Shuwen
https://arxiv.org/abs/2506.11429 https://arxiv.org/p…

A survey of The Prouhet-Tarry-Escott Problem and its Generalizations
This paper explores the Prouhet-Tarry-Escott problem (PTE), the Generalized PTE problem (GPTE), and the Fermat form of Generalized PTE problem (FPTE). The GPTE problem extends the PTE problem by allowing different sets of exponents, while the FPTE problem considers cases where the number of integers in the two sets differs by one. Multigrade chains are also investigated, involving multiple sets of integers satisfying the GPTE system. The study of PTE and GPTE problems is further extended from i…

New characterization of full weight spectrum one-orbit cyclic subspace codes
Minjia Shi, Wenhao Song
https://arxiv.org/abs/2506.13418 https://

New characterization of full weight spectrum one-orbit cyclic subspace codes
Castello $\textit{et al}$. [J. Comb. Theory Ser. A, 212, 106005 (2025)] provided a complete classification for full weight spectrum (FWS) one-orbit cyclic subspace codes. In this paper, we determine the weight distributions of a family of FWS codes and exhibit some equivalence classes of FWS codes under certain conditions. Furthermore, we provide a complete classification for $r$-FWS codes.

[2025-06-16 Mon (UTC), no new articles found for math.CT Category Theory]
toXiv_bot_toot

WIP: Exploring the Value of a Debugging Cheat Sheet and Mini Lecture in Improving Undergraduate Debugging Skills and Mindset
Andrew Ash, John Hu
https://arxiv.org/abs/2506.11339

WIP: Exploring the Value of a Debugging Cheat Sheet and Mini Lecture in Improving Undergraduate Debugging Skills and Mindset
This work-in-progress research paper explores the efficacy of a small-scale microelectronics debugging education intervention utilizing quasi-experimental design in an introductory microelectronics course for third-year electrical and computer engineering (ECE) students. In the first semester of research, the experimental group attended a debugging "mini lecture" covering two common sources of circuit error and received a debugging cheat sheet with recommendations for testing and hypothesis for…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
Advancing Hybrid Defense for Byzantine Attacks in Federated Learning
htt…

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Akira Sakurai, Kazuyuki Shudo
https://arxiv.org/abs/2506.13360 https://

The Rich Get Richer in Bitcoin Mining Induced by Blockchain Forks
Bitcoin is a representative decentralized currency system. For the security of Bitcoin, fairness in the distribution of mining rewards plays a crucial role in preventing the concentration of computational power in a few miners. Here, fairness refers to the distribution of block rewards in proportion to contributed computational resources. If miners with greater computational resources receive disproportionately higher rewards, i.e., if the Rich Get Richer (TRGR) phenomenon holds in Bitcoin, it …

On the intervals for the non-existence of covering systems with distinct moduli
Jack Dalton, Nic Jones
https://arxiv.org/abs/2506.11359 https://

On the intervals for the non-existence of covering systems with distinct moduli
In a research seminar in $2006$, M. Filaseta, O. Trifonov, and G. Yu showed for each integer $n\geq3$ there is no distinct covering with all moduli in the interval $[n, 6n]$. In $2022$, this interval was subsequently improved to $[n, 8n]$ by the first author and O. Trifonov. The first author then improved this bound to $[n, 9n]$ in $2023$. Building off their method, we show that for each integer $n\geq 3$, there does not exist a distinct covering system with all moduli in the interval $[n, 10n]…

Tady: A Neural Disassembler without Structural Constraint Violations
Siliang Qin, Fengrui Yang, Hao Wang, Bolun Zhang, Zeyu Gao, Chao Zhang, Kai Chen
https://arxiv.org/abs/2506.13323

Tady: A Neural Disassembler without Structural Constraint Violations
Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread e…

Social Scientists on the Role of AI in Research
Tatiana Chakravorti, Xinyu Wang, Pranav Narayanan Venkit, Sai Koneru, Kevin Munger, Sarah Rajtmajer
https://arxiv.org/abs/2506.11255

Social Scientists on the Role of AI in Research
The integration of artificial intelligence (AI) into social science research practices raises significant technological, methodological, and ethical issues. We present a community-centric study drawing on 284 survey responses and 15 semi-structured interviews with social scientists, describing their familiarity with, perceptions of the usefulness of, and ethical concerns about the use of AI in their field. A crucial innovation in study design is to split our survey sample in half, providing the…

Building Automotive Security on Internet Standards: An Integration of DNSSEC, DANE, and DANCE to Authenticate and Authorize In-Car Services
Timo Salomon, Mehmet Mueller, Philipp Meyer, Thomas C. Schmidt
https://arxiv.org/abs/2506.13261

Building Automotive Security on Internet Standards: An Integration of DNSSEC, DANE, and DANCE to Authenticate and Authorize In-Car Services
The automotive industry is undergoing a software-as-a-service transformation that enables software-defined functions and post-sale updates via cloud and vehicle-to-everything communication. Connectivity in cars introduces significant security challenges, as remote attacks on vehicles have become increasingly prevalent. Current automotive designs call for security solutions that address the entire lifetime of a vehicle. In this paper, we propose to authenticate and authorize in-vehicle services …

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
Barbara Oakley, Michael Johnston, Ken-Zen Chen, Eulho Jung, Terrence J. Sejnowski
https://arxiv.org/abs/2506.11015

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
In the age of generative AI and ubiquitous digital tools, human cognition faces a structural paradox: as external aids become more capable, internal memory systems risk atrophy. Drawing on neuroscience and cognitive psychology, this paper examines how heavy reliance on AI systems and discovery-based pedagogies may impair the consolidation of declarative and procedural memory -- systems essential for expertise, critical thinking, and long-term retention. We review how tools like ChatGPT and calc…

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
Craig Steven Wright
https://arxiv.org/abs/2506.13246

On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent tran…

[2025-06-16 Mon (UTC), 9 new articles found for cs.CY Computers and Society]
toXiv_bot_toot

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments
Xuan Wang, Siyuan Liang, Zhe Liu, Yi Yu, Yuliang Lu, Xiaochun Cao, Ee-Chien Chang
https://arxiv.org/abs/2506.13205

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments
With the growing integration of vision-language models (VLMs), mobile agents are now widely used for tasks like UI automation and camera-based user assistance. These agents are often fine-tuned on limited user-generated datasets, leaving them vulnerable to covert threats during the training process. In this work we present GHOST, the first clean-label backdoor attack specifically designed for mobile agents built upon VLMs. Our method manipulates only the visual inputs of a portion of the traini…

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-AI Interactions
Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar
https://arxiv.org/abs/2506.13510

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-AI Interactions
As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical imperative. Despite progress in AI safety, current evaluations predominantly focus on adults, neglecting the unique vulnerabilities of minors engaging with generative AI. We introduce Safe-Child-LLM, a comprehensive benchmark and dataset for systematically assessing LLM safety across two developmental stages: children (7-12…

Dual Protection Ring: User Profiling Via Differential Privacy and Service Dissemination Through Private Information Retrieval
Imdad Ullah, Najm Hassan, Tariq Ahamed Ahangar, Zawar Hussain Shah, Mehregan Mahdavi Andrew Levula
https://arxiv.org/abs/2506.13170

Dual Protection Ring: User Profiling Via Differential Privacy and Service Dissemination Through Private Information Retrieval
User profiling is crucial in providing personalised services, as it relies on analysing user behaviour and preferences to deliver targeted services. This approach enhances user experience and promotes heightened engagement. Nevertheless, user profiling also gives rise to noteworthy privacy considerations due to the extensive tracking and monitoring of personal data, potentially leading to surveillance or identity theft. We propose a dual-ring protection mechanism to protect user privacy by exam…

pySpainMobility: a Python Package to Access and Manage Spanish Open Mobility Data
Ciro Beneduce, Tania Gull\'on Mu\~noz-Repiso, Bruno Lepri, Massimiliano Luca
https://arxiv.org/abs/2506.13385

pySpainMobility: a Python Package to Access and Manage Spanish Open Mobility Data
Mobility patterns play a critical role in a wide range of societal challenges, from epidemic modeling and emergency response to transportation planning and regional development. Yet, access to high-quality, timely, and openly available mobility data remains limited. In response, the Spanish Ministry of Transportation and Sustainable Mobility has released daily mobility datasets based on anonymized mobile phone data, covering districts, municipalities, and greater urban areas from February 2020 …

Using LLMs for Security Advisory Investigations: How Far Are We?
Bayu Fedra Abdullah, Yusuf Sulistyo Nugroho, Brittany Reid, Raula Gaikovina Kula, Kazumasa Shimari, Kenichi Matsumoto
https://arxiv.org/abs/2506.13161

Using LLMs for Security Advisory Investigations: How Far Are We?
Large Language Models (LLMs) are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study investigates the ability of ChatGPT to (1) generate plausible security advisories from CVE-IDs, (2) differentiate real from fake CVE-IDs, and (3) extract CVE-IDs from advisory descriptions. Using a curated dataset of 100 real and 100 fake CVE-IDs, we manually analyzed the credibility and consistency of the model's output…

Detecting Hard-Coded Credentials in Software Repositories via LLMs
Chidera Biringa, Gokhan Kul
https://arxiv.org/abs/2506.13090 https://

Detecting Hard-Coded Credentials in Software Repositories via LLMs
Software developers frequently hard-code credentials such as passwords, generic secrets, private keys, and generic tokens in software repositories, even though it is strictly advised against due to the severe threat to the security of the software. These credentials create attack surfaces exploitable by a potential adversary to conduct malicious exploits such as backdoor attacks. Recent detection efforts utilize embedding models to vectorize textual credentials before passing them to classifier…

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions
Steven Su, Erik Rye, Robert Beverly, Dave Levin
https://arxiv.org/abs/2506.13052

Buy it Now, Track Me Later: Attacking User Privacy via Wi-Fi AP Online Auctions
Static and hard-coded layer-two network identifiers are well known to present security vulnerabilities and endanger user privacy. In this work, we introduce a new privacy attack against Wi-Fi access points listed on secondhand marketplaces. Specifically, we demonstrate the ability to remotely gather a large quantity of layer-two Wi-Fi identifiers by programmatically querying the eBay marketplace and applying state-of-the-art computer vision techniques to extract IEEE 802.11 BSSIDs from the sell…

Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew C. Cullen, Paul Montague, Sarah M. Erfani, Benjamin I. P. Rubinstein
https://arxiv.org/abs/2506.13024

Position: Certified Robustness Does Not (Yet) Imply Model Security
While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the paradox of detection without distinction, the lack of clear criteria for practitioners to evaluate certification schemes, and the potential security risks arising from users' expectations surrounding ``guara…

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang, Jie Fu, Wendy Hui Wang, Weiran Liu, Yuan Hong
https://arxiv.org/abs/2506.13009

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing interest in inexact unlearning methods. However, the lack of formal guarantees in these methods necessitates the need for robust evaluation frameworks to assess their privacy and effectiveness. In this wo…

Open Source, Open Threats? Investigating Security Challenges in Open-Source Software
Seyed Ali Akhavani, Behzad Ousat, Amin Kharraz
https://arxiv.org/abs/2506.12995

Open Source, Open Threats? Investigating Security Challenges in Open-Source Software
Open-source software (OSS) has become increasingly more popular across different domains. However, this rapid development and widespread adoption come with a security cost. The growing complexity and openness of OSS ecosystems have led to increased exposure to vulnerabilities and attack surfaces. This paper investigates the trends and patterns of reported vulnerabilities within OSS platforms, focusing on the implications of these findings for security practices. To understand the dynamics of OS…

Cut Tracing with E-Graphs for Boolean FHE Circuit Synthesis
Julien de Castelnau, Mingfei Yu, Giovanni De Micheli
https://arxiv.org/abs/2506.12883 https://

Cut Tracing with E-Graphs for Boolean FHE Circuit Synthesis
Fully Homomorphic Encryption (FHE) is a promising privacy-preserving technology enabling secure computation over encrypted data. A major limitation of current FHE schemes is their high runtime overhead. As a result, automatic optimization of circuits describing FHE computation has garnered significant attention in the logic synthesis community. Existing works primarily target the multiplicative depth (MD) and multiplicative complexity (MC) of FHE circuits, corresponding to the total number of m…

Universal Jailbreak Suffixes Are Strong Attention Hijackers
Matan Ben-Tov, Mor Geva, Mahmood Sharif
https://arxiv.org/abs/2506.12880 https://

Universal Jailbreak Suffixes Are Strong Attention Hijackers
We study suffix-based jailbreaks$\unicode{x2013}$a powerful family of attacks against large language models (LLMs) that optimize adversarial suffixes to circumvent safety alignment. Focusing on the widely used foundational GCG attack (Zou et al., 2023), we observe that suffixes vary in efficacy: some markedly more universal$\unicode{x2013}$generalizing to many unseen harmful instructions$\unicode{x2013}$than others. We first show that GCG's effectiveness is driven by a shallow, critical mechani…

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Nina Cai, Jinguang Han
https://arxiv.org/abs/2506.12846

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malici…

Bidirectional Biometric Authentication Using Transciphering and (T)FHE
Joon Soo Yoo, Tae Min Ahn, Ji Won Yoon
https://arxiv.org/abs/2506.12802 https://

Bidirectional Biometric Authentication Using Transciphering and (T)FHE
Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption (FHE) enables secure encrypted evaluation, but its deployment is hindered by large ciphertexts, high key overhead, and limited trust models. We propose the Bidirectional Transciphering Framework (BTF), combining FHE, transciphering, and a non-colluding trusted party to enable efficient and privacy-preserving biometric authentication. T…

Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus
Joon Soo Yoo, Taeho Kim, Ji Won Yoon
https://arxiv.org/abs/2506.12761

Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus
Location-based services often require users to share sensitive locational data, raising privacy concerns due to potential misuse or exploitation by untrusted servers. In response, we present VeLoPIR, a versatile location-based private information retrieval (PIR) system designed to preserve user privacy while enabling efficient and scalable query processing. VeLoPIR introduces three operational modes-interval validation, coordinate validation, and identifier matching-that support a broad range o…

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
Yucheng Li, Surin Ahn, Huiqiang Jiang, Amir H. Abdi, Yuqing Yang, Lili Qiu
https://arxiv.org/abs/2506.12707

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. Previous research has proposed methods such as adversarial training and prompt rephrasing to mitigate these safety vulnerabilities, but these methods often reduce the utility of LL…

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
Yashothara Shanmugarasa, Ming Ding, M. A. P Chamikara, Thierry Rakotoarivelo
https://arxiv.org/abs/2506.12699

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation
Large language models (LLMs) are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer significant technological progress, their development using vast amounts of user data scraped from the web and collected from extensive user interactions poses risks of sensitive information leakage. Most existing surveys focus on the privacy implications of the training data but tend to overlook privacy risks from user inter…

Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity
Bilal Saleh Husain
https://arxiv.org/abs/2506.12685 https://…

Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their susceptibility to adversarial attacks, particularly jailbreaking, poses significant safety and ethical concerns. While numerous jailbreak methods exist, many suffer from computational expense, high token usage, or complex decoding schemes. Liu et al. (2024) introduced FlipAttack, a black-box method that achieves high attack success rates (ASR) through simple prompt manipulation. This paper investigates the underly…

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training
Limengnan Zhou, Hanzhou Wu
https://arxiv.org/abs/2506.12675 https://

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training
Quantum neural networks (QNNs) leverage quantum computing to create powerful and efficient artificial intelligence models capable of solving complex problems significantly faster than traditional computers. With the fast development of quantum hardware technology, such as superconducting qubits, trapped ions, and integrated photonics, quantum computers may become reality, accelerating the applications of QNNs. However, preparing quantum circuits and optimizing parameters for QNNs require quantu…

GNSS Spoofing Detection Based on Opportunistic Position Information
Wenjie Liu, Panos Papadimitratos
https://arxiv.org/abs/2506.12580 https://

GNSS Spoofing Detection Based on Opportunistic Position Information
The limited or no protection for civilian Global Navigation Satellite System (GNSS) signals makes spoofing attacks relatively easy. With modern mobile devices often featuring network interfaces, state-of-the-art signals of opportunity (SOP) schemes can provide accurate network positions in replacement of GNSS. The use of onboard inertial sensors can also assist in the absence of GNSS, possibly in the presence of jammers. The combination of SOP and inertial sensors has received limited attention…

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models
Jingxuan Zhang, Zhenhua Xu, Rui Hu, Wenpeng Xing, Xuhong Zhang, Meng Han
https://arxiv.org/abs/2506.12551

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models
Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection. Although backdoor-based fingerprinting has emerged as a promising solution for model authentication, effective attacks for removing these fingerprints remain largely unexplored. Therefore, we present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining …

Privacy-preserving and reward-based mechanisms of proof of engagement
Matteo Marco Montanari, Alessandro Aldini
https://arxiv.org/abs/2506.12523 https://…

Privacy-preserving and reward-based mechanisms of proof of engagement
Proof-of-Attendance (PoA) mechanisms are typically employed to demonstrate a specific user's participation in an event, whether virtual or in-person. The goal of this study is to extend such mechanisms to broader contexts where the user wishes to digitally demonstrate her involvement in a specific activity (Proof-of-Engagement, PoE). This work explores different solutions, including DLTs as well as established technologies based on centralized systems. The main aspects we consider include the l…

When Forgetting Triggers Backdoors: A Clean Unlearning Attack
Marco Arazzi, Antonino Nocera, Vinod P
https://arxiv.org/abs/2506.12522 https://

When Forgetting Triggers Backdoors: A Clean Unlearning Attack
Machine unlearning has emerged as a key component in ensuring ``Right to be Forgotten'', enabling the removal of specific data points from trained models. However, even when the unlearning is performed without poisoning the forget-set (clean unlearning), it can be exploited for stealthy attacks that existing defenses struggle to detect. In this paper, we propose a novel {\em clean} backdoor attack that exploits both the model learning phase and the subsequent unlearning requests. Unlike traditi…

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
Saskia Laura Schr\"oer, Luca Pajola, Alberto Castagnaro, Giovanni Apruzzese, Mauro Conti
https://arxiv.org/abs/2506.12519

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
As Artificial Intelligence (AI) continues to evolve, it has transitioned from a research-focused discipline to a widely adopted technology, enabling intelligent solutions across various sectors. In security, AI's role in strengthening organizational resilience has been studied for over two decades. While much attention has focused on AI's constructive applications, the increasing maturity and integration of AI have also exposed its darker potentials. This article explores two emerging AI-relate…

Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation
Alexander Geiger, Immanuel Hacker, \"Omer Sen, Andreas Ulbig
https://arxiv.org/abs/2506.12466

Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation
The increasing complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors and a higher risk of faults through misconfiguration. We propose to meet those risks with a declarative approach to describe cyberphysical power systems and to automatically evaluate security and safety controls. We leverage Semantic Web technologies as a well-standardized framework, providing languages to specify ontologies, rules and shape constraints. We model infrastr…

Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao, Peng Ying, Shixuan Sun, Pengyu Chen, Junze Chen, Hao Du, Kaiwen Shen, Shangkun Wu, Jiwei Wei, Shiyuan He, Yang Yang, Xiaohai Xu, Ke Ma, Qianqian Xu, Qingming Huang, Shi Lin, Xun Wang, Changting Lin, Meng Han, Yilei Jiang, Siqi Lai, Yaozhi Zheng, Yifei Song, Xiangyu Yue, Zonglei Jing, Tianyuan Zhang, Zhilei Zhu, Aishan Liu, Jiakai Wang, Siyuan Liang, Xianglong Kong, Hainan Li, …

Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks that induce harmful outputs. To systematically evaluate and improve their safety, we organized the Adversarial Testing & Large-model Alignment Safety Grand Challenge (ATLAS) 2025}. This technical report presents findings from the competition, which involved 86 teams testing MLLM vulnerabilities via adversarial image-…

InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning
Mengyuan Sun, Yu Li, Yuchen Liu, Bo Du, Yunjie Ge
https://arxiv.org/abs/2506.12411

InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning
Multimodal contrastive learning models like CLIP have demonstrated remarkable vision-language alignment capabilities, yet their vulnerability to backdoor attacks poses critical security risks. Attackers can implant latent triggers that persist through downstream tasks, enabling malicious control of model behavior upon trigger presentation. Despite great success in recent defense mechanisms, they remain impractical due to strong assumptions about attacker knowledge or excessive clean data requir…

Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Haoyu Zhai, Shuo Wang, Pirouz Naghavi, Qingying Hao, Gang Wang
https://arxiv.org/abs/2506.12344

Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Gaussian blur is widely used to blur human faces in sensitive photos before the photos are posted on the Internet. However, it is unclear to what extent the blurred faces can be restored and used to re-identify the person, especially under a high-blurring setting. In this paper, we explore this question by developing a deblurring method called Revelio. The key intuition is to leverage a generative model's memorization effect and approximate the inverse function of Gaussian blur for face restora…

Information-theoretic Estimation of the Risk of Privacy Leaks
Kenneth Odoh
https://arxiv.org/abs/2506.12328 https://arxiv.org/pdf/250…

Information-theoretic Estimation of the Risk of Privacy Leaks
Recent work~\cite{Liu2016} has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the original data and their noisy responses from a randomizer as an indicator of potential privacy breaches. This paper aims to leverage information-theoretic measures, such as the Maximal Information Coe…

QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety
Taegyeong Lee, Jeonghwa Yoo, Hyoungseo Cho, Soo Yong Kim, Yunho Maeng
https://arxiv.org/abs/2506.12299

QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety
The recent advancements in Large Language Models(LLMs) have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for malicious attacks. Although there have been many efforts to prevent harmful prompts and jailbreak prompts, protecting LLMs from such malicious attacks remains an important and challenging task. In this p…

InfoFlood: Jailbreaking Large Language Models with Information Overload
Advait Yadav, Haibo Jin, Man Luo, Jun Zhuang, Haohan Wang
https://arxiv.org/abs/2506.12274

InfoFlood: Jailbreaking Large Language Models with Information Overload
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, their potential to generate harmful responses has raised significant societal and regulatory concerns, especially when manipulated by adversarial techniques known as "jailbreak" attacks. Existing jailbreak methods typically involve appending carefully crafted prefixes or suffixes to malicious prompts in order to bypass the built-in safety mechanisms of these models. In this work, we identif…