Tootfinder

Interesting: The Artificial Intelligence Disclosure (AID) Framework, a standard, comprehensive, and detailed framework meant to inform the development and writing of GenAI disclosure for education and research'. Based on CRediT statements for different roles in research publications
https://arxiv.org/abs/2408.01904

The Artificial Intelligence Disclosure (AID) Framework: An Introduction
As the use of Generative Artificial Intelligence tools have grown in higher education and research, there have been increasing calls for transparency and granularity around the use and attribution of the use of these tools. Thus far, this need has been met via the recommended inclusion of a note, with little to no guidance on what the note itself should include. This has been identified as a problem to the use of AI in academic and research contexts. This article introduces The Artificial Intel…

from my link log —
Faster remainder by multiplication, with applications to compilers and software libraries.
https://arxiv.org/abs/1902.01961
saved 2021-05-23

Faster Remainder by Direct Computation: Applications to Compilers and Software Libraries
On common processors, integer multiplication is many times faster than integer division. Dividing a numerator n by a divisor d is mathematically equivalent to multiplication by the inverse of the divisor (n / d = n x 1/d). If the divisor is known in advance---or if repeated integer divisions will be performed with the same divisor---it can be beneficial to substitute a less costly multiplication for an expensive division. Currently, the remainder of the division by a constant is computed from…

Sorcha - a Solar System Survey Simulator for the Legacy Survey of Space and Time: #Rubin Observatory will discover millions of solar system objects: https://www.geekwire.com/2025/sorcha-software-rubin-observatory-millions/

This https://arxiv.org/abs/2505.13182 has been replaced.
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Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

This https://arxiv.org/abs/2504.20606 has been replaced.
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Monoidal Relative Categories Model Monoidal $\infty$-Categories
We show that the homotopy theory of monoidal relative categories is equivalent to that of monoidal $\infty$-categories, as well as its symmetric monoidal version. As an application, we give a concise and complete proof of the fact that every presentably monoidal or presentably symmetric monoidal $\infty$-category is presented by a monoidal or symmetric monoidal model category, which, in the monoidal case, was sketched by Lurie, and in the symmetric monoidal case, was proved by Nikolaus--Sagave.

This https://arxiv.org/abs/2504.07732 has been replaced.
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Efficient Formal Verification of Quantum Error Correcting Programs
Quantum error correction (QEC) is fundamental for suppressing noise in quantum hardware and enabling fault-tolerant quantum computation. In this paper, we propose an efficient verification framework for QEC programs. We define an assertion logic and a program logic specifically crafted for QEC programs and establish a sound proof system. We then develop an efficient method for handling verification conditions (VCs) of QEC programs: for Pauli errors, the VCs are reduced to classical assertions t…

This https://arxiv.org/abs/2505.24765 has been replaced.
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Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantag…

This https://arxiv.org/abs/2506.03896 has been replaced.
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FLIP: Flowability-Informed Powder Weighing
Autonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow, coupled with variability in laboratory conditions necessitates adaptive automation. This work introduces FLIP, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. Our key contribution lies in using material flowability, quantifie…

This https://arxiv.org/abs/2506.03308 has been replaced.
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Hermes: High-Performance Homomorphically Encrypted Vector Databases
Fully Homomorphic Encryption (FHE) has long promised the ability to compute over encrypted data without revealing sensitive contents -- a foundational goal for secure cloud analytics. Yet despite decades of cryptographic advances, practical integration of FHE into real-world relational databases remains elusive. This paper presents \textbf{Hermes}, the first system to enable FHE-native vector query processing inside a standard SQL engine. By leveraging the multi-slot capabilities of modern sche…

This https://arxiv.org/abs/2506.03903 has been replaced.
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Multi-Language Detection of Design Pattern Instances
Code comprehension is often supported by source code analysis tools which provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multi-language pattern detection tool that uses the multi-language capability of the LARA framework to support finding p…

This https://arxiv.org/abs/2505.24195 has been replaced.
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WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions
With more than 11 times as many pageviews as the next, English Wikipedia dominates global knowledge access relative to other language editions. Readers are prone to assuming English Wikipedia as a superset of all language editions, leading many to prefer it even when their primary language is not English. Other language editions, however, comprise complementary facts rooted in their respective cultures and media environments, which are marginalized in English Wikipedia. While Wikipedia's user i…

Nonlinear Causal Discovery for Grouped Data
Konstantin G\"obler, Tobias Windisch, Mathias Drton
https://arxiv.org/abs/2506.05120 https://

Nonlinear Causal Discovery for Grouped Data
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many important domains, including neuroscience, psychology, social science, and industrial manufacturing, the causal units of interest are groups of variables rather than individual scalar measurements. Motivated by these applications, we extend nonlinear additive noise models to handle random vectors, establishing a two-step …

This https://arxiv.org/abs/2502.13033 has been replaced.
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Classical notions of computation and the Hasegawa-Thielecke theorem
In the spirit of the Curry-Howard correspondence between proofs and programs, we define and study a syntax and semantics for classical logic equipped with a computationally involutive negation, using a polarised effect calculus. A main challenge in designing a denotational semantics is to accommodate both call-by-value and call-by-name evaluation strategies, which leads to a failure of associativity of composition. Building on the work of the third author, we devise the notion of dialogue duplo…

This https://arxiv.org/abs/2501.14429 has been replaced.
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Various topos of types constructions
We study and compare some topos of types constructions, which were defined by Garner, Joyal, Reyes and Makkai.

Planets similar in size are often dissimilar in interior: #planets with similar radii, we show that their masses and interior structures are often disparate, indicating that even within the same system, similarity in radii is not necessarily a good proxy for similarity in composition or the physical nature of the planets."

This https://arxiv.org/abs/2505.24502 has been replaced.
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Predictability of quantum observables: Applications in QKD and top quarks
We quantify and optimize the predictability of local observables in bipartite quantum systems by employing the Bayes risk and the inference variance, two measures rooted in statistical learning theory. Specifically, we minimized these quantities when the prediction is improved by an additional quantum system, providing analytical expressions for arbitrary two-qubit states, and showcasing a connection with Einstein-Podolsky-Rosen steering criteria. Then, we embed our Bayes risk minimization into…

This https://arxiv.org/abs/2506.03568 has been replaced.
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Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving
Autonomous driving promises significant advancements in mobility, road safety and traffic efficiency, yet reinforcement learning and imitation learning face safe-exploration and distribution-shift challenges. Although human-AI collaboration alleviates these issues, it often relies heavily on extensive human intervention, which increases costs and reduces efficiency. This paper develops a confidence-guided human-AI collaboration (C-HAC) strategy to overcome these limitations. First, C-HAC employ…

This https://arxiv.org/abs/2506.02040 has been replaced.
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Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol Ecosystem
The Model Context Protocol (MCP) is an emerging standard designed to enable seamless interaction between Large Language Model (LLM) applications and external tools or resources. Within a short period, thousands of MCP services have already been developed and deployed. However, the client-server integration architecture inherent in MCP may expand the attack surface against LLM Agent systems, introducing new vulnerabilities that allow attackers to exploit by designing malicious MCP servers. In th…

This https://arxiv.org/abs/2506.03507 has been replaced.
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Software Bill of Materials in Software Supply Chain Security A Systematic Literature Review
Software Bill of Materials (SBOMs) are increasingly regarded as essential tools for securing software supply chains (SSCs), yet their real-world use and adoption barriers remain poorly understood. This systematic literature review synthesizes evidence from 40 peer-reviewed studies to evaluate how SBOMs are currently used to bolster SSC security. We identify five primary application areas: vulnerability management, transparency, component assessment, risk assessment, and SSC integrity. Despite c…

This https://arxiv.org/abs/2505.22769 has been replaced.
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MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking
Mobile gaze tracking faces a fundamental challenge: maintaining accuracy as users naturally change their postures and device orientations. Traditional calibration approaches, like one-off, fail to adapt to these dynamic conditions, leading to degraded performance over time. We present MAC-Gaze, a Motion-Aware continual Calibration approach that leverages smartphone Inertial measurement unit (IMU) sensors and continual learning techniques to automatically detect changes in user motion states and…

Admissibility of Completely Randomized Trials: A Large-Deviation Approach
Guido Imbens, Chao Qin, Stefan Wager
https://arxiv.org/abs/2506.05329 https://

Admissibility of Completely Randomized Trials: A Large-Deviation Approach
When an experimenter has the option of running an adaptive trial, is it admissible to ignore this option and run a non-adaptive trial instead? We provide a negative answer to this question in the best-arm identification problem, where the experimenter aims to allocate measurement efforts judiciously to confidently deploy the most effective treatment arm. We find that, whenever there are at least three treatment arms, there exist simple adaptive designs that universally and strictly dominate non…

This https://arxiv.org/abs/2409.11231 has been replaced.
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Positively closed $Sh(B)$-valued models
We study positively closed and strongly positively closed topos-valued models of coherent theories. Positively closed is a global notion (it is defined in terms of all possible outgoing homomorphisms), while strongly positively closed is a local notion (it only concerns the definable sets inside the model). For $\mathbf{Set}$-valued models of coherent theories they coincide. We prove that if $\mathcal{E}=Sh(X)$ for an extremally disconnected Stone space (or equivalently $\mathcal{E}=Sh(B,τ_{…

Logical Inferentialism & Attacks on Classical Logic
Khashayar Irani
https://arxiv.org/abs/2506.04295 https://arxiv.org/pdf/2506.0…

Logical Inferentialism & Attacks on Classical Logic
This paper undertakes a foundational inquiry into logical inferentialism with particular emphasis on the normative standards it establishes and the implications these pose for classical logic. The central question addressed herein is: 'What is Logical Inferentialism & How do its Standards challenge Classical Logic?' In response, the study begins with a survey of the three principal proof systems that is, David Hilbert's axiomatic systems and Gerhard Gentzen's natural deduction and his sequent c…

This https://arxiv.org/abs/2505.20863 has been replaced.
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Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a generative approach based on denoising diffusion models (DMs) to synthesize parameterized quantum circuits (PQCs). Extending the recent diffusion model pipeline of Fürrutter et al. [1], our model effectively conditions the synthesis process, enabling the simultaneous generation of circuit architectures and their continuous…

This https://arxiv.org/abs/2506.01759 has been replaced.
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ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured environments. The effectiveness of these methods hinges on two essential elements: (1) the use of massively parallel physics simulations to expedite policy training, and (2) an environment generator tasked with crafting sufficiently challenging yet attainable environments to facilitate continuous policy improvement. Existing metho…

This https://arxiv.org/abs/2505.23847 has been replaced.
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Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or viola…

This https://arxiv.org/abs/2505.19887 has been replaced.
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Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities
Large language models (LLMs) have shown promise in software engineering, yet their effectiveness for binary analysis remains unexplored. We present the first comprehensive evaluation of commercial LLMs for assembly code deobfuscation. Testing seven state-of-the-art models against four obfuscation scenarios (bogus control flow, instruction substitution, control flow flattening, and their combination), we found striking performance variations--from autonomous deobfuscation to complete failure. We…

This https://arxiv.org/abs/2505.10661 has been replaced.
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It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fairness highly depend on human perceptions of AI. Positive fairness perceptions have been shown to foster trust and acceptance of AI recommendations. Yet, work on confirmation bias highlights that humans selectively adhere to AI recommendations that align with their expectations and beliefs -- despite not being necessa…

Distributional encoding for Gaussian process regression with qualitative inputs
S\'ebastien Da Veiga (ENSAI, CREST, RT-UQ)
https://arxiv.org/abs/2506.04813

Distributional encoding for Gaussian process regression with qualitative inputs
Gaussian Process (GP) regression is a popular and sample-efficient approach for many engineering applications, where observations are expensive to acquire, and is also a central ingredient of Bayesian optimization (BO), a highly prevailing method for the optimization of black-box functions. However, when all or some input variables are categorical, building a predictive and computationally efficient GP remains challenging. Starting from the naive target encoding idea, where the original categor…

On Top-Down Pseudo-Boolean Model Counting
Suwei Yang, Yong Lai, Kuldeep S. Meel
https://arxiv.org/abs/2506.05232 https://arxiv.org/pd…

On Top-Down Pseudo-Boolean Model Counting
Pseudo-Boolean model counting involves computing the number of satisfying assignments of a given pseudo-Boolean (PB) formula. In recent years, PB model counting has seen increased interest partly owing to the succinctness of PB formulas over typical propositional Boolean formulas in conjunctive normal form (CNF) at describing problem constraints. In particular, the research community has developed tools to tackle exact PB model counting. These recently developed counters follow one of the two e…

Pushforwards in Inverse Homotopical Diagrams
Chris Kapulkin, Yufeng Li
https://arxiv.org/abs/2506.04472 https://arxiv.org/pdf/2506.04…

Pushforwards in Inverse Homotopical Diagrams
We establish a sufficient condition for the category of homotopical inverse diagrams to be closed under pushforward inside the category of inverse diagrams in a fibration category.

This https://arxiv.org/abs/2505.01214 has been replaced.
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A Warm-start QAOA based approach using a swap-based mixer for the TSP: theoretical considerations,implementation and experiments
This paper investigates quantum heuristics based on Mixer Hamiltonians, which allow the search to be restricted to a specific subspace and enable warm-start strategies for solving the Traveling Salesman Problem (TSP). Approaches involving Mixer Hamiltonians can be integrated into the Quantum Approximate Optimization Algorithm (QAOA), where the Mixer acts as a mapping function that transforms qubit strings into feasible solution sets. We first introduce a swap-based mixer tailored to the TSP, wh…

This https://arxiv.org/abs/2506.01135 has been replaced.
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$\text{TREX}^2$: Dual-Reconstruction Framework for Teleoperated-Robot with EXtended Reality
Robot teleoperation with extended reality (XR teleoperation) enables intuitive interaction by allowing remote robots to mimic user motions with real-time 3D feedback. However, existing systems face significant motion-to-motion (M2M) latency--the delay between the user's latest motion and the corresponding robot feedback--leading to high teleoperation error and mission completion time. This issue stems from the system's exclusive reliance on network communication, making it highly vulnerable to …

This https://arxiv.org/abs/2505.12612 has been replaced.
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EPSpatial: Achieving Efficient and Private Statistical Analytics of Geospatial Data
Geospatial data statistics involve the aggregation and analysis of location data to derive the distribution of clients within geospatial. The need for privacy protection in geospatial data analysis has become paramount due to concerns over the misuse or unauthorized access of client location information. However, existing private geospatial data statistics mainly rely on privacy computing techniques such as cryptographic tools and differential privacy, which leads to significant overhead and in…

This https://arxiv.org/abs/2503.00271 has been replaced.
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Why Johnny Signs with Next-Generation Tools: A Usability Case Study of Sigstore
Software signing is the most robust method for ensuring the integrity and authenticity of components in a software supply chain. However, traditional signing tools place key management and signer identification burdens on practitioners, leading to both security vulnerabilities and usability challenges. Next-generation signing tools such as Sigstore have automated some of these concerns, but little is known about their usability and adoption dynamics. This knowledge gap hampers the integration o…

This https://arxiv.org/abs/2504.17934 has been replaced.
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Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents
The rise of Large Language Models (LLMs) has revolutionized Graphical User Interface (GUI) automation through LLM-powered GUI agents, yet their ability to process sensitive data with limited human oversight raises significant privacy and security risks. This position paper identifies three key risks of GUI agents and examines how they differ from traditional GUI automation and general autonomous agents. Despite these risks, existing evaluations focus primarily on performance, leaving privacy an…

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2506.04626

Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-policy single-agent reinforcement learning (RL) and federated RL (FRL) with a focus on minimizing burn-in costs (the sample sizes needed to reach near-optimal regret) and policy switching or communication costs. In parallel finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states and $A$ actions, existing m…

Trustworthiness Preservation by Copies of Machine Learning Systems
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2506.05203 https://

Trustworthiness Preservation by Copies of Machine Learning Systems
A common practice of ML systems development concerns the training of the same model under different data sets, and the use of the same (training and test) sets for different learning models. The first case is a desirable practice for identifying high quality and unbiased training conditions. The latter case coincides with the search for optimal models under a common dataset for training. These differently obtained systems have been considered akin to copies. In the quest for responsible AI, a l…

This https://arxiv.org/abs/2504.15265 has been replaced.
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Realization of maximally-entangling two-qutrit gates using the Cross-Resonance scheme
In this letter, we present a comprehensive theoretical framework for realizing maximally entangling two-qutrit gates on fixed-frequency transmons that generalize the qubit-centric cross-resonance (CR) interaction beyond the 0-1 subspace, which we call as generalized cross-resonance (GCR) scheme. We use the GCR scheme to design parametric two-qutrit gates, namely, $U_{CR}^{01}$ and $U_{CR}^{12}$, that act on the $0{-}1$ and $1{-}2$ energy transitions of transmons, respectively. Our gates improve…

This https://arxiv.org/abs/2505.24305 has been replaced.
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SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Recent advancements in 3D robotic manipulation have improved grasping of everyday objects, but transparent and specular materials remain challenging due to depth sensing limitations. While several 3D reconstruction and depth completion approaches address these challenges, they suffer from setup complexity or limited observation information utilization. To address this, leveraging the power of single view 3D object reconstruction approaches, we propose a training free framework SR3D that enables…

This https://arxiv.org/abs/2505.05849 has been replaced.
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AGENTFUZZER: Generic Black-Box Fuzzing for Indirect Prompt Injection against LLM Agents
The strong planning and reasoning capabilities of Large Language Models (LLMs) have fostered the development of agent-based systems capable of leveraging external tools and interacting with increasingly complex environments. However, these powerful features also introduce a critical security risk: indirect prompt injection, a sophisticated attack vector that compromises the core of these agents, the LLM, by manipulating contextual information rather than direct user prompts. In this work, we pr…

This https://arxiv.org/abs/2502.14948 has been replaced.
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Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation
Recent advances in large language models (LLMs) have improved their performance on coding benchmarks. However, improvement is plateauing due to the exhaustion of readily available high-quality data. Prior work has shown the potential of synthetic self-instruct data, but naively training on a model's own outputs can cause error accumulation, especially in coding tasks, where generalization may collapse due to overly simple or erroneous training data, highlighting the need for rigorous quality ch…

This https://arxiv.org/abs/2504.14522 has been replaced.
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Biased by Design: Leveraging AI Inherent Biases to Enhance Critical Thinking of News Readers
This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical thinking in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bia…

Redefining Functionality and Construction-Defining Capacity: Functions as Principles of Syntactic and Semantic Generation
Yumiko Nishiyama
https://arxiv.org/abs/2506.04278

Redefining Functionality and Construction-Defining Capacity: Functions as Principles of Syntactic and Semantic Generation
This study redefines the notion of functionality-traditionally understood as a property of mappings or structure preservation-from a more fundamental and generative perspective. Introducing the concept of a Construction-Defining function (CDF), we formalize functionality as a dual capacity to generate both syntactic terms and semantic interpretations. We provide an explicit axiomatization of CDF based on syntactic generativity and semantic compositionality, and further construct categorical mod…

This https://arxiv.org/abs/2501.09622 has been replaced.
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Optimizing hypergraph product codes with random walks, simulated annealing and reinforcement learning
Hypergraph products are quantum low-density parity-check (LDPC) codes constructed from two classical LDPC codes. Although their dimension and distance depend only on the parameters of the underlying classical codes, optimizing their performance against various noise channels remains challenging. This difficulty partly stems from the complexity of decoding in the quantum setting. The standard, ad hoc approach typically involves selecting classical LDPC codes with large girth. In this work, we fo…

This https://arxiv.org/abs/2505.24266 has been replaced.
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SignBot: Learning Human-to-Humanoid Sign Language Interaction
Sign language is a natural and visual form of language that uses movements and expressions to convey meaning, serving as a crucial means of communication for individuals who are deaf or hard-of-hearing (DHH). However, the number of people proficient in sign language remains limited, highlighting the need for technological advancements to bridge communication gaps and foster interactions with minorities. Based on recent advancements in embodied humanoid robots, we propose SignBot, a novel framew…

This https://arxiv.org/abs/2505.03768 has been replaced.
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From Concept to Measurement: A Survey of How the Blockchain Trilemma Can Be Analyzed
To meet non-functional requirements, practitioners must identify Pareto-optimal configurations of the degree of decentralization, scalability, and security of blockchain systems. Maximizing all of these subconcepts is, however, impossible due to the trade-offs highlighted by the blockchain trilemma. We reviewed analysis approaches to identify constructs and their operationalization through metrics for analyzing the blockchain trilemma subconcepts and to assess the applicability of the operation…

This https://arxiv.org/abs/2504.02234 has been replaced.
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LLM Social Simulations Are a Promising Research Method
Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted this method. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a review of empirical comparisons between LLMs and human r…

BINGO! Simple Optimizers Win Big if Problems Collapse to a Few Buckets
Kishan Kumar Ganguly, Tim Menzies
https://arxiv.org/abs/2506.04509 https://

BINGO! Simple Optimizers Win Big if Problems Collapse to a Few Buckets
Traditional multi-objective optimization in software engineering (SE) can be slow and complex. This paper introduces the BINGO effect: a novel phenomenon where SE data surprisingly collapses into a tiny fraction of possible solution "buckets" (e.g., only 100 used from 4,096 expected). We show the BINGO effect's prevalence across 39 optimization in SE problems. Exploiting this, we optimize 10,000 times faster than state-of-the-art methods, with comparable effectiveness. Our new algorithms (LIT…

Proceedings of the 19th International Workshop on Logical and Semantic Frameworks, with Applications
Cynthia Kop (Radboud Universiteit Nijmegen), Helida Salles Santos (Universidade Federal do Rio Grande)
https://arxiv.org/abs/2506.05219

Proceedings of the 19th International Workshop on Logical and Semantic Frameworks, with Applications
This volume contains the post-proceedings of the 19th LSFA, which was held in Goiânia, the capital of Goiás state in Brazil, from September 18 to September 20, 2024. Logical and semantic frameworks are formal languages used to represent logics, languages and systems. These frameworks provide foundations for the formal specification of systems and programming languages, supporting tool development and reasoning. The aim of this series is bringing together theoreticians and practitioners t…

This https://arxiv.org/abs/2412.20925 has been replaced.
link: https://scholar.google.com/scholar?q=a

Active Learning with Variational Quantum Circuits for Quantum Process Tomography
Quantum process tomography (QPT) is a fundamental tool for fully characterizing quantum systems. It relies on querying a set of quantum states as input to the quantum process. Previous QPT methods typically employ a straightforward strategy for randomly selecting quantum states, overlooking differences in informativeness among them. In this work, we propose a general active learning (AL) framework that adaptively selects the most informative subset of quantum states for reconstruction. We desig…

This https://arxiv.org/abs/2505.10033 has been replaced.
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Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected…

This https://arxiv.org/abs/2503.00555 has been replaced.
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Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
Safety alignment is an important procedure before the official deployment of a Large Language Model (LLM). While safety alignment has been extensively studied for LLM, there is still a large research gap for Large Reasoning Models (LRMs) that equip with improved reasoning capability. We in this paper systematically examine a simplified pipeline for producing safety aligned LRMs. With our evaluation of various LRMs, we deliver two main findings: i) Safety alignment can be done upon the LRM to re…

This https://arxiv.org/abs/2503.16920 has been replaced.
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Assessing AI Explainability: A Usability Study Using a Novel Framework Involving Clinicians
An AI design framework was developed based on three core principles, namely understandability, trust, and usability. The framework was conceptualized by synthesizing evidence from the literature and by consulting with experts. The initial version of the AI Explainability Framework was validated based on an in-depth expert engagement and review process. For evaluation purposes, an AI-anchored prototype, incorporating novel explainability features, was built and deployed online. The primary funct…

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair
Zanis Ali Khan, Aayush Garg, Qiang Tang
https://arxiv.org/abs/2506.04987 https://…

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair
Software vulnerabilities pose significant security threats, requiring effective mitigation. While Automated Program Repair (APR) has advanced in fixing general bugs, vulnerability patching, a security-critical aspect of APR remains underexplored. This study investigates pre-trained language models, CodeBERT and CodeT5, for automated vulnerability patching across six datasets and four languages. We evaluate their accuracy and generalization to unknown vulnerabilities. Results show that while bot…

This https://arxiv.org/abs/2412.00908 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Optomechanical systems with nonlinear interactions: photon blockade, collapse-revival effect and Fano-like resonance
Closed-form expressions for the average amplitude of the optical field in optomechanical systems are obtained, in which, in addition to the linear interaction, quadratic and cubic interactions of the vibrational mode of the mechanical resonator with the mode of the optical resonator are considered. In the framework of the non-secular perturbation theory, using the Bogoliubov averaging method, it is shown that the effects of photon blockade, collapse and revival of optical oscillations in such s…

This https://arxiv.org/abs/2505.09833 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Rock Pushability on Rough Planetary Terrain
In the context of mobile navigation in unstructured environments, the predominant approach entails the avoidance of obstacles. The prevailing path planning algorithms are contingent upon deviating from the intended path for an indefinite duration and returning to the closest point on the route after the obstacle is left behind spatially. However, avoiding an obstacle on a path that will be used repeatedly by multiple agents can hinder long-term efficiency and lead to a lasting reliance on an ac…

This https://arxiv.org/abs/2502.18504 has been replaced.
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TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice
Jailbreaking large-language models (LLMs) involves testing their robustness against adversarial prompts and evaluating their ability to withstand prompt attacks that could elicit unauthorized or malicious responses. In this paper, we present TurboFuzzLLM, a mutation-based fuzzing technique for efficiently finding a collection of effective jailbreaking templates that, when combined with harmful questions, can lead a target LLM to produce harmful responses through black-box access via user prompt…

This https://arxiv.org/abs/2410.06415 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Biased AI can Influence Political Decision-Making
As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conser…

BacPrep: An Experimental Platform for Evaluating LLM-Based Bacalaureat Assessment
Dumitran Adrian Marius, Dita Radu
https://arxiv.org/abs/2506.04989 https:…

BacPrep: An Experimental Platform for Evaluating LLM-Based Bacalaureat Assessment
Accessing quality preparation and feedback for the Romanian Bacalaureat exam is challenging, particularly for students in remote or underserved areas. This paper introduces BacPrep, an experimental online platform exploring Large Language Model (LLM) potential for automated assessment, aiming to offer a free, accessible resource. Using official exam questions from the last 5 years, BacPrep employs one of Google's newest models, Gemini 2.0 Flash (released Feb 2025), guided by official grading sc…

This https://arxiv.org/abs/2412.00193 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Spacetime Markov length: a diagnostic for fault tolerance via mixed-state phases
We establish a correspondence between the fault-tolerance of local stabilizer codes experiencing measurement and physical errors and the mixed-state phases of decohered resource states in one higher dimension. Drawing from recent developments in mixed-state phases of matter, this motivates a diagnostic of fault-tolerance, which we refer to as the spacetime Markov length. This is a length scale determined by the decay of the (classical) conditional mutual information of repeated syndrome measure…

This https://arxiv.org/abs/2505.09430 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches
We present a method that reduces, by an order of magnitude, the time and memory needed to train multi-task vision-language robotic diffusion policies. This improvement arises from a previously underexplored distinction between action diffusion and the image diffusion techniques that inspired it: In image generation, the target is high-dimensional. By contrast, in action generation, the dimensionality of the target is comparatively small, and only the image condition is high-dimensional. Our app…

This https://arxiv.org/abs/2502.09755 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Jailbreak Attack Initializations as Extractors of Compliance Directions
Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model's activation space. Recent works show that initializing attacks via self-transfer from other prompts significantly enhances their performance. However, the underlying mechanisms of these initializations remain unclear, and attacks utilize arbitrary or hand-picked initializations. This work presents that each gradient-based jailbreak attack and subsequent initializatio…

This https://arxiv.org/abs/2409.17800 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workin…

QuanUML: Towards A Modeling Language for Model-Driven Quantum Software Development
Xiaoyu Guo, Shinobu Saito, Jianjun Zhao
https://arxiv.org/abs/2506.04639

QuanUML: Towards A Modeling Language for Model-Driven Quantum Software Development
This paper introduces QuanUML, an extension of the Unified Modeling Language (UML) tailored for quantum software systems. QuanUML integrates quantum-specific constructs, such as qubits and quantum gates, into the UML framework, enabling the modeling of both quantum and hybrid quantum-classical systems. We apply QuanUML to Efficient Long-Range Entanglement using Dynamic Circuits and Shor's Algorithm, demonstrating its utility in designing and visualizing quantum algorithms. Our approach supports…

This https://arxiv.org/abs/2411.13669 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Algorithm for Vibronic Dynamics: Case Study on Singlet Fission Solar Cell Design
Vibronic interactions between nuclear motion and electronic states are critical for the accurate modeling of photochemistry. However, accurate simulations of fully quantum non-adiabatic dynamics are often prohibitively expensive for classical methods beyond small systems. In this work, we present a quantum algorithm based on product formulas for simulating time evolution under a general vibronic Hamiltonian in real space, capable of handling an arbitrary number of electronic states and vibratio…

This https://arxiv.org/abs/2505.03448 has been replaced.
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AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames
Many underwater applications, such as offshore asset inspections, rely on visual inspection and detailed 3D reconstruction. Recent advancements in underwater visual SLAM systems for aquatic environments have garnered significant attention in marine robotics research. However, existing underwater visual SLAM datasets often lack groundtruth trajectory data, making it difficult to objectively compare the performance of different SLAM algorithms based solely on qualitative results or COLMAP reconst…

This https://arxiv.org/abs/2501.12911 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning
Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However, current security implementations for these systems face a fundamental trade-off: rigorous cryptographic protections like fully homomorphic encryption (FHE) impose prohibitive computational overhead, while lightweight alternatives risk vulnerable data leakage th…

This https://arxiv.org/abs/2401.08960 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utiliz…

This https://arxiv.org/abs/2409.17061 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Against (unitary) interpretation (of quantum mechanics): removing the metaphysical load
On 6 June 1925 Heisenberg arrived at Helgoland/Heligoland island to escape a fit of hay fever. He returned with a sketch of a strange theory of the micro-world of atoms, which we now call quantum mechanics. This e-print of ours attempts to present a look on that theory which tries to return to the original insight of Heisenberg, on what should be the essence of a theory of atomic realm: it must be a theory of the observable events, in which fundamentally unobservable quantities have no place. N…

This https://arxiv.org/abs/2504.14170 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Collision Induced Binding and Transport of Shape Changing Robot Pairs
We report in experiment and simulation the spontaneous formation of dynamically bound pairs of shape changing robots undergoing locally repulsive collisions. These physical `gliders' robustly emerge from an ensemble of individually undulating three-link two-motor robots and can remain bound for hundreds of undulations and travel for multiple robot dimensions. Gliders occur in two distinct binding symmetries and form over a wide range of angular oscillation extent. This parameter sets the maxima…

This https://arxiv.org/abs/2501.09023 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Cyber-Physical Security Vulnerabilities Identification and Classification in Smart Manufacturing -- A Defense-in-Depth Driven Framework and Taxonomy
The increasing cybersecurity threats to critical manufacturing infrastructure necessitate proactive strategies for vulnerability identification, classification, and assessment. Traditional approaches, which define vulnerabilities as weaknesses in computational logic or information systems, often overlook the physical and cyber-physical dimensions critical to manufacturing systems, comprising intertwined cyber, physical, and human elements. As a result, existing solutions fall short in addressin…

This https://arxiv.org/abs/2201.03074 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey of Passive Sensing for Workplace Wellbeing and Productivity
The modern workplace is undergoing a radical transformation, driven by technological advances that blur the boundaries between human capability and digital augmentation. At the forefront of this evolution is passive sensing technology - a suite of tools that quietly monitor and interpret human behavior without active user engagement. This paper examines how these technologies are reshaping our understanding of workplace dynamics, with a particular focus on employee wellbeing and productivity. T…

This https://arxiv.org/abs/2407.11523 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Improved Belief Propagation Decoding Algorithms for Surface Codes
Quantum error correction is crucial for universal fault-tolerant quantum computing. Highly accurate and low-time-complexity decoding algorithms play an indispensable role in ensuring quantum error correction works effectively. Among existing decoding algorithms, belief propagation (BP) is notable for its nearly linear time complexity and general applicability to stabilizer codes. However, BP's decoding accuracy without post-processing is unsatisfactory in most situations. This article focuses o…

This https://arxiv.org/abs/2502.06466 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Inflatable Kirigami Crawlers
Kirigami offers unique opportunities for guided morphing by leveraging the geometry of the cuts. This work presents inflatable kirigami crawlers created by introducing cut patterns into heat-sealable textiles to achieve locomotion upon cyclic pneumatic actuation. Inflating traditional air pouches results in symmetric bulging and contraction. In inflated kirigami actuators, the accumulated compressive forces uniformly break the symmetry, enhance contraction compared to simple air pouches by two …

This https://arxiv.org/abs/2410.11295 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

BRC20 Pinning Attack
BRC20 tokens are a type of non-fungible asset on the Bitcoin network. They allow users to embed customised content within Bitcoin's satoshis. The token frenzy reached a market size of US\$2.811\,b (2023Q3--2025Q1). However, this intuitive design has not undergone serious security scrutiny. We present the first analysis of BRC20's \emph{transfer} mechanism and identify a new attack vector. A typical BRC20 transfer involves two "bundled" on-chain transactions with different fee levels: the firs…

This https://arxiv.org/abs/2407.00903 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Implementation and topological characterization of Weyl exceptional rings in quantum-mechanical systems
Non-Hermiticity can lead to the emergence of many intriguing phenomena that are absent in Hermitian systems, enabled by exceptional topological defects, among which Weyl exceptional rings (WER) are particularly interesting. The topology of a WER can be characterized by the quantized Berry phase and a nonzero Chern number, both encoded in the eigenvectors of the non-Hermitian Hamiltonian. So far, WERs have been realized with classical wave systems, whose eigenvectors can be well described by cla…

This https://arxiv.org/abs/2409.17469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain
Reinforcement Learning (RL) has the potential to enable extreme off-road mobility by circumventing complex kinodynamic modeling, planning, and control by simulated end-to-end trial-and-error learning experiences. However, most RL methods are sample-inefficient when training in a large amount of manually designed simulation environments and struggle at generalizing to the real world. To address these issues, we introduce VertiSelector (VS), an automatic curriculum learning framework designed to …

This https://arxiv.org/abs/2409.18858 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Predicting memorization within Large Language Models fine-tuned for classification
Large Language Models have received significant attention due to their abilities to solve a wide range of complex tasks. However these models memorize a significant proportion of their training data, posing a serious threat when disclosed at inference time. To mitigate this unintended memorization, it is crucial to understand what elements are memorized and why. This area of research is largely unexplored, with most existing works providing a posteriori explanations. To address this gap, we pro…

This https://arxiv.org/abs/2406.18370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Learning pure quantum states (almost) without regret
We initiate the study of sample-optimal quantum state tomography with minimal disturbance to the samples. Can we efficiently learn a precise description of a quantum state through sequential measurements of samples while at the same time making sure that the post-measurement state of the samples is only minimally perturbed? Defining regret as the cumulative disturbance of all samples, the challenge is to find a balance between the most informative sequence of measurements on the one hand and me…

This https://arxiv.org/abs/2409.11962 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Reactive Collision Avoidance for Safe Agile Navigation
Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently challenging because it requires a tight integration of perception, planning, and control, which traditional methods often handle separately, resulting in compounded errors and delays. This paper introduces a novel approach that unifies these tasks into a single reactive framework using solely onboard sensing and computing.…

Incentivizing Collaborative Breach Detection
Mridu Nanda, Michael K. Reiter
https://arxiv.org/abs/2506.04634 https://arxiv.org/pdf/25…

Incentivizing Collaborative Breach Detection
Decoy passwords, or "honeywords," alert a site to its breach if they are ever entered in a login attempt on that site. However, an attacker can identify a user-chosen password from among the decoys, without risk of alerting the site to its breach, by performing credential stuffing, i.e., entering the stolen passwords at another site where the same user reused her password. Prior work has thus proposed that sites monitor for the entry of their honeywords at other sites. Unfortunately, it is not …

This https://arxiv.org/abs/2406.13785 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Efficient Implementation of a Quantum Search Algorithm for Arbitrary N
This paper presents an enhancement to Grover's search algorithm for instances where the number of items (or the size of the search problem) $N$ is not a power of 2. By employing an efficient algorithm for the preparation of uniform quantum superposition states over a subset of the computational basis states, we demonstrate that a considerable reduction in the number of oracle calls (and Grover's iterations) can be achieved in many cases. For special cases (i.e., when $N$ is of the form such tha…

This https://arxiv.org/abs/2409.08704 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

QueryCAD: Grounded Question Answering for CAD Models
CAD models are widely used in industry and are essential for robotic automation processes. However, these models are rarely considered in novel AI-based approaches, such as the automatic synthesis of robot programs, as there are no readily available methods that would allow CAD models to be incorporated for the analysis, interpretation, or extraction of information. To address these limitations, we propose QueryCAD, the first system designed for CAD question answering, enabling the extraction o…

This https://arxiv.org/abs/2405.10886 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Three-mode tunable coupler for superconducting two-qubit gates
Building a scalable universal high-performance quantum processor is a formidable challenge. In particular, the problem of realizing fast high-perfomance two-qubit gates of high-fidelity remains needful. Here we propose a building block for a scalable quantum processor consisting of two transmons and a tunable three-mode coupler allowing for a ZZ interaction control. We experimentally demonstrate the native CZ gate with the pulse duration of 60 ns achieving the two-qubit gate fidelity above 98%,…

OpenCCA: An Open Framework to Enable Arm CCA Research
Andrin Bertschi, Shweta Shinde
https://arxiv.org/abs/2506.05129 https://arxiv.o…

OpenCCA: An Open Framework to Enable Arm CCA Research
Confidential computing has gained traction across major architectures with Intel TDX, AMD SEV-SNP, and Arm CCA. Unlike TDX and SEV-SNP, a key challenge in researching Arm CCA is the absence of hardware support, forcing researchers to develop ad-hoc performance prototypes on non-CCA Arm boards. This approach leads to duplicated efforts, inconsistent performance comparisons, and high barriers to entry. To address this, we present OpenCCA, an open research platform that enables the execution of CC…

This https://arxiv.org/abs/2408.11978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Optimized Kalman Filter based State Estimation and Height Control in Hopping Robots
Rotor-based hopping locomotion significantly improves efficiency and operation time as compared to purely flying systems; where most hopping robots use the liftoff states and an assumed ballistic trajectory to determine the hopping height. However, significant aerial phase force (e.g., thrust and drag) can invalidate this assumption and lead to poor estimation performance. To combat this issue, a group has implemented multiple sensors (active and passive optical, inertial, and contact) and sign…

This https://arxiv.org/abs/2405.10282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

GKLS Vector Field Dynamics for Gaussian States
We construct the vector field associated to the GKLS generator for systems described by Gaussian states. This vector field is defined on the dual space of the algebra of operators, restricted to operators quadratic in position and momentum. It is shown that the GKLS dynamics accepts a decomposition principle, that is, this vector field can be decomposed in three parts, a conservative Hamiltonian component, a gradient-like, and a Choi-Kraus or jump vector field. The two last terms are considered…

This https://arxiv.org/abs/2401.04003 has been replaced.
link: https://scholar.google.com/scholar?q=a

Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Research in robotic planning with temporal logic specifications, such as Linear Temporal Logic (LTL), has relied on single formulas. However, as task complexity increases, LTL formulas become lengthy, making them difficult to interpret and generate, and straining the computational capacities of planners. To address this, we introduce a hierarchical structure for a widely used specification type -- LTL on finite traces (LTL$_f$). The resulting language, termed H-LTL$_f$, is defined with both its…

Membership Inference Attacks on Sequence Models
Lorenzo Rossi, Michael Aerni, Jie Zhang, Florian Tram\`er
https://arxiv.org/abs/2506.05126 https://

Membership Inference Attacks on Sequence Models
Sequence models, such as Large Language Models (LLMs) and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tendency has critical legal implications, existing tools are insufficient to audit the resulting risks. We hypothesize that those tools' shortcomings are due to mismatched assumptions. Thus, we argue that effectively measuring privacy leakage in sequence models requires leveraging the correlations inherent in sequential g…

Attack Effect Model based Malicious Behavior Detection
Limin Wang, Lei Bu, Muzimiao Zhang, Shihong Cang, Kai Ye
https://arxiv.org/abs/2506.05001 https://…

Attack Effect Model based Malicious Behavior Detection
Traditional security detection methods face three key challenges: inadequate data collection that misses critical security events, resource-intensive monitoring systems, and poor detection algorithms with high false positive rates. We present FEAD (Focus-Enhanced Attack Detection), a framework that addresses these issues through three innovations: (1) an attack model-driven approach that extracts security-critical monitoring items from online attack reports for comprehensive coverage; (2) effic…

Hiding in Plain Sight: Query Obfuscation via Random Multilingual Searches
Anton Firc, Jan Klus\'a\v{c}ek, Kamil Malinka
https://arxiv.org/abs/2506.04963

Hiding in Plain Sight: Query Obfuscation via Random Multilingual Searches
Modern search engines extensively personalize results by building detailed user profiles based on query history and behaviour. While personalization can enhance relevance, it introduces privacy risks and can lead to filter bubbles. This paper proposes and evaluates a lightweight, client-side query obfuscation strategy using randomly generated multilingual search queries to disrupt user profiling. Through controlled experiments on the Seznam.cz search engine, we assess the impact of interleaving…

A Private Smart Wallet with Probabilistic Compliance
Andrea Rizzini, Marco Esposito, Francesco Bruschi, Donatella Sciuto
https://arxiv.org/abs/2506.04853 h…

A Private Smart Wallet with Probabilistic Compliance
We propose a privacy-preserving smart wallet with a novel invitation-based private onboarding mechanism. The solution integrates two levels of compliance in concert with an authority party: a proof of innocence mechanism and an ancestral commitment tracking system using bloom filters for probabilistic UTXO chain states. Performance analysis demonstrates practical efficiency: private transfers with compliance checks complete within seconds on a consumer-grade laptop, and overall with proof gener…

PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages
Deniz Simsek, Aryaz Eghbali, Michael Pradel
https://arxiv.org/abs/2506.04962

PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages
Security vulnerabilities in software packages are a significant concern for developers and users alike. Patching these vulnerabilities in a timely manner is crucial to restoring the integrity and security of software systems. However, previous work has shown that vulnerability reports often lack proof-of-concept (PoC) exploits, which are essential for fixing the vulnerability, testing patches, and avoiding regressions. Creating a PoC exploit is challenging because vulnerability reports are info…

The Hashed Fractal Key Recovery (HFKR) Problem: From Symbolic Path Inversion to Post-Quantum Cryptographic Keys
Mohamed Aly Bouke
https://arxiv.org/abs/2506.04383

The Hashed Fractal Key Recovery (HFKR) Problem: From Symbolic Path Inversion to Post-Quantum Cryptographic Keys
Classical cryptographic systems rely heavily on structured algebraic problems, such as factorization, discrete logarithms, or lattice-based assumptions, which are increasingly vulnerable to quantum attacks and structural cryptanalysis. In response, this work introduces the Hashed Fractal Key Recovery (HFKR) problem, a non-algebraic cryptographic construction grounded in symbolic dynamics and chaotic perturbations. HFKR builds on the Symbolic Path Inversion Problem (SPIP), leveraging symbolic tr…

BESA: Boosting Encoder Stealing Attack with Perturbation Recovery
Xuhao Ren, Haotian Liang, Yajie Wang, Chuan Zhang, Zehui Xiong, Liehuang Zhu
https://arxiv.org/abs/2506.04556

BESA: Boosting Encoder Stealing Attack with Perturbation Recovery
To boost the encoder stealing attack under the perturbation-based defense that hinders the attack performance, we propose a boosting encoder stealing attack with perturbation recovery named BESA. It aims to overcome perturbation-based defenses. The core of BESA consists of two modules: perturbation detection and perturbation recovery, which can be combined with canonical encoder stealing attacks. The perturbation detection module utilizes the feature vectors obtained from the target encoder to …

Through the Stealth Lens: Rethinking Attacks and Defenses in RAG
Sarthak Choudhary, Nils Palumbo, Ashish Hooda, Krishnamurthy Dj Dvijotham, Somesh Jha
https://arxiv.org/abs/2506.04390

Through the Stealth Lens: Rethinking Attacks and Defenses in RAG
Retrieval-augmented generation (RAG) systems are vulnerable to attacks that inject poisoned passages into the retrieved set, even at low corruption rates. We show that existing attacks are not designed to be stealthy, allowing reliable detection and mitigation. We formalize stealth using a distinguishability-based security game. If a few poisoned passages are designed to control the response, they must differentiate themselves from benign ones, inherently compromising stealth. This motivates th…

On Automating Security Policies with Contemporary LLMs
Pablo Fern\'andez Saura, K. R. Jayaram, Vatche Isahagian, Jorge Bernal Bernab\'e, Antonio Skarmeta
https://arxiv.org/abs/2506.04838

On Automating Security Policies with Contemporary LLMs
The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models (LLMs) for automating attack mitigation policy compliance through an innovative combination of in-context learning and retrieval-augmented generation (RAG). We begin by describing how our system collects and manages both tool and API specification…

Hello, won't you tell me your name?: Investigating Anonymity Abuse in IPFS
Christos Karapapas, Iakovos Pittaras, George C. Polyzos, Constantinos Patsakis
https://arxiv.org/abs/2506.04307

Hello, won't you tell me your name?: Investigating Anonymity Abuse in IPFS
The InterPlanetary File System~(IPFS) offers a decentralized approach to file storage and sharing, promising resilience and efficiency while also realizing the Web3 paradigm. Simultaneously, the offered anonymity raises significant questions about potential misuse. In this study, we explore methods that malicious actors can exploit IPFS to upload and disseminate harmful content while remaining anonymous. We evaluate the role of pinning services and public gateways, identifying their capabilitie…

MULTISS: un protocole de stockage confidentiel {\`a} long terme sur plusieurs r{\'e}seaux QKD
Thomas Pr\'evost (I3S), Olivier Alibart (INPHYNI), Marc Kaplan, Anne Marin
https://arxiv.org/abs/2506.04800

MULTISS: un protocole de stockage confidentiel {à} long terme sur plusieurs r{é}seaux QKD
This paper presents MULTISS, a new protocol for long-term storage distributed across multiple Quantum Key Distribution (QKD) networks. This protocol is an extension of LINCOS, a secure storage protocol that uses Shamir secret sharing for secret storage on a single QKD network. Our protocol uses hierarchical secret sharing to distribute a secret across multiple QKD networks while ensuring perfect security. Our protocol further allows for sharing updates without having to reconstruct the entire s…