Tootfinder

Dear #saas #cloud platform #software companies: If you have a page titled `Pricing` and then, after a features/tiers matrix that takes seven minutes to scroll past, don't have _any_ pricing information, instead saying `Contact Sa…

Replaced article(s) found for math.NT. https://arxiv.org/list/math.NT/new
[1/1]:
The Schinzel-Szekeres function
https://arxiv…

[2025-05-16 Fri (UTC), 1 new article found for cs.OS Operating Systems]
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Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm
https://arxiv.org/abs/2506.12323

Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset. To address this, diffusion models (DMs) have been considered as a promising avenue for synthetic image generation and augmentation. However, they frequently produce medically inaccurate images, deteriorating the model performance. Expert domain knowledge is critical for synthesizing images that correctly encode clin…

Decidable Reversible Equivalences for Finite Petri Nets
Roberto Gorrieri, Ivan Lanese
https://arxiv.org/abs/2506.11517 https://arxiv.…

Decidable Reversible Equivalences for Finite Petri Nets
In the setting of Petri nets, we prove that {\em causal-net bisimilarity} \cite{G15,Gor22,Gor25a}, which is a refinement of history-preserving bisimilarity \cite{RT88,vGG89,DDM89}, and the novel {\em hereditary} causal-net bisimilarity, which is a refinement of hereditary history-preserving bisimilarity \cite{Bed91,JNW96}, do coincide. This means that causal-net bisimilarity is a {\em reversible behavioral equivalence}, as causal-net bisimilar markings not only are able to match each other's fo…

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

ALEA IACTA EST: A Declarative Domain-Specific Language for Manually Performable Random Experiments
Baltasar Tranc\'on y Widemann, Markus Lepper
https://arxiv.org/abs/2506.11794

ALEA IACTA EST: A Declarative Domain-Specific Language for Manually Performable Random Experiments
Random experiments that are simple and clear enough to be performed by human agents feature prominently in the teaching of elementary stochastics as well as in games. We present Alea, a domain-specific language for the specification of random experiments. Alea code can either be analyzed statically to obtain and inspect probability distributions of outcomes, or be executed with a source pseudo-randomness for simulation or as a game assistant. The language is intended for ease of use by non-expe…

Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis
Pranav Agarwal, Ioana Ciuc\u{a}
https://arxiv.org/abs/2506.12189

Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis
Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and c…

[2025-06-16 Mon (UTC), no new articles found for cs.GT Computer Science and Game Theory]
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Robotic System for Chemical Experiment Automation with Dual Demonstration of End-effector and Jig Operations
Hikaru Sasaki, Naoto Komeno, Takumi Hachimine, Kei Takahashi, Yu-ya Ohnishi, Tetsunori Sugawara, Araki Wakiuchi, Miho Hatanaka, Tomoyuki Miyao, Hiroharu Ajiro, Mikiya Fujii, Takamitsu Matsubara
https://arxiv.org/abs/2506.…

Robotic System for Chemical Experiment Automation with Dual Demonstration of End-effector and Jig Operations
While robotic automation has demonstrated remarkable performance, such as executing hundreds of experiments continuously over several days, it is challenging to design a program that synchronizes the robot's movements with the experimental jigs to conduct an experiment. We propose a concept that enables the automation of experiments by utilizing dual demonstrations of robot motions and jig operations by chemists in an experimental environment constructed to be controlled by a robot. To verify t…

CnC-PRAC: Coalesce, not Cache, Per Row Activation Counts for an Efficient in-DRAM Rowhammer Mitigation
Chris S. Lin, Jeonghyun Woo, Prashant J. Nair, Gururaj Saileshwar
https://arxiv.org/abs/2506.11970

CnC-PRAC: Coalesce, not Cache, Per Row Activation Counts for an Efficient in-DRAM Rowhammer Mitigation
JEDEC has introduced the Per Row Activation Counting (PRAC) framework for DDR5 and future DRAMs to enable precise counting of DRAM row activations using per-row activation counts. While recent PRAC implementations enable holistic mitigation of Rowhammer attacks, they impose slowdowns of up to 10% due to the increased DRAM timings for performing a read-modify-write of the counter. Alternatively, recent work, Chronus, addresses these slowdowns, but incurs energy overheads due to the additional DR…

Refactoring Codebases through Library Design
Ziga Kovacic, Celine Lee, Justin Chiu, Wenting Zhao, Kevin Ellis
https://arxiv.org/abs/2506.11058 https://

Refactoring Codebases through Library Design
Maintainable and general software allows developers to build robust applications efficiently, yet achieving these qualities often requires refactoring specialized solutions into reusable components. This challenge becomes particularly relevant as code agents become increasingly accurate at solving isolated programming problems. We investigate code agents' capacity to refactor code in ways supporting growth and reusability. We present both a method and a benchmark for refactoring: Librarian, a s…

Addressing the $H_0$ tension through matter with pressure and no early dark energy
Youri Carloni, Orlando Luongo, Marco Muccino
https://arxiv.org/abs/2506.11531

Addressing the $H_0$ tension through matter with pressure and no early dark energy
We propose that the Hubble tension arises due to an unaccounted additional component, that behaves as \emph{matter with pressure}. We demonstrate that this fluid remains subdominant compared to both dust and radiation throughout nearly the entire universe expansion history. Specifically, the additional fluid satisfies the Zel'dovic limit with a constant equation of state, $ω_s > 0$, and a quite small normalized energy density, $Ω_s$. Accordingly, this component modifies both the sound horizon…

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…

[2025-05-16 Fri (UTC), no new articles found for q-fin.TR Trading and Market Microstructure]
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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…

[2025-06-16 Mon (UTC), 17 new articles found for eess.IV Image and Video Processing]
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Reconstruction theorems for coadmissible D-cap-modules
Finn Wiersig
https://arxiv.org/abs/2506.12601 https://arxiv.org/pdf/2506.12601…

Reconstruction theorems for coadmissible D-cap-modules
We prove a Riemann-Hilbert correspondence for Ardakov-Wadsley's coadmissible D-cap-modules and, more generally, for Bode's $\mathcal{C}$-complexes. More precisely, we show that any given $\mathcal{C}$-complex can be reconstructed out of its solutions. As a corollary, we find that slight modifications of the solution and de Rham functors introduced by the author are fully faithful on $\mathcal{C}$-complexes and, in particular, on coadmissible D-cap-modules. One of the many steps of our proof is …

Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
https://arxiv.org/abs/2506.11999

Generative Representational Learning of Foundation Models for Recommendation
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexit…

Last-Pair Swapping Polar Codes: A Structure to Improve Polarization on Channels with Memory
Yinuo Mei, Yangyong Zhang, Daiming Qu
https://arxiv.org/abs/2506.11535

Last-Pair Swapping Polar Codes: A Structure to Improve Polarization on Channels with Memory
A new structure of polar codes is proposed for channels with memory to improve polarization and approach the polarization efficiency of conventional polar codes on memoryless channels. We classify trellis channels, a particular type of finite state channels into two categories, called sub-injective trellis channels and non-sub-injective trellis channels, where an explicit polarization loss is observed on the former. For sub-injective trellis channels, we substitute the last kernel of each layer…

Asymmetric Effects Underlying Dynamic Heterogeneity in Miscible Blends of Poly(methyl methacrylate) with Poly(ethylene oxide)
Shannon Zhang, Michael A. Webb
https://arxiv.org/abs/2506.12143

Asymmetric Effects Underlying Dynamic Heterogeneity in Miscible Blends of Poly(methyl methacrylate) with Poly(ethylene oxide)
The emergence of spatially variable local dynamics, or dynamic heterogeneity, is common in multicomponent polymer systems. Such heterogeneity is understood to arise from differences between the intrinsic dynamical fluctuations associated with one component versus another. However, the nature of the dynamic coupling between these components and how it depends on composition, temperature, and environmental fluctuations is not fully understood. Here, we use molecular dynamics simulations to charac…

Replaced article(s) found for hep-th. https://arxiv.org/list/hep-th/new
[1/1]:
Explorations in Scalar Fermion Theories: $\beta$-functions, Supersymmetry and Fixed Points

Reversible Pebble Transducers
Luc Dartois, Paul Gastin, L. Germerie Guizouarn, Shankaranarayanan Krishna
https://arxiv.org/abs/2506.11334 https://

Reversible Pebble Transducers
Deterministic two-way transducers with pebbles (aka pebble transducers) capture the class of polyregular functions, which extend the string-to-string regular functions allowing polynomial growth instead of linear growth. One of the most fundamental operations on functions is composition, and (poly)regular functions can be realized as a composition of several simpler functions. In general, composition of deterministic two-way transducers incur a doubly exponential blow-up in the size of the inpu…

Second-Order Parameterizations for the Complexity Theory of Integrable Functions
Aras Bacho, Martin Ziegler
https://arxiv.org/abs/2506.11210 https://

Second-Order Parameterizations for the Complexity Theory of Integrable Functions
We develop a unified second-order parameterized complexity theory for spaces of integrable functions. This generalizes the well-established case of second-order parameterized complexity theory for spaces of continuous functions. Specifically we prove the mutual linear equivalence of three natural parameterizations of the space $\Lrm{p}$ of $p$-integrable complex functions on the real unit interval: (binary) $\Lrm{p}$-modulus, rate of convergence of Fourier series, and rate of approximation by s…

[2025-06-16 Mon (UTC), 1 new article found for cs.LO Logic in Computer Science]
toXiv_bot_toot

PermRust: A Token-based Permission System for Rust
Lukas Gehring, Sebastian Rehms, Florian Tschorsch
https://arxiv.org/abs/2506.11701 https://

PermRust: A Token-based Permission System for Rust
Permission systems which restrict access to system resources are a well-established technology in operating systems, especially for smartphones. However, as such systems are implemented in the operating system they can at most manage access on the process-level. Since moderns software often (re)uses code from third-parties libraries, a permission system for libraries can be desirable to enhance security. In this short-paper, we adapt concepts from capability systems building a novel theoretical…

Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety
Enes \"Ozeren, Alexander Ulbrich, Sascha Filimon, David R\"ugamer, Andreas Bender
https://arxiv.org/abs/2506.12092

Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety
A comprehensive understanding of traffic accidents is essential for improving city safety and informing policy decisions. In this study, we analyze traffic incidents in Munich to identify patterns and characteristics that distinguish different types of accidents. The dataset consists of both structured tabular features, such as location, time, and weather conditions, as well as unstructured free-text descriptions detailing the circumstances of each accident. Each incident is categorized into on…

Measuring and Minimizing Disturbance of Marine Animals to Underwater Vehicles
Levi Cai, Youenn J\'ez\'equel, T. Aran Mooney, Yogesh Girdhar
https://arxiv.org/abs/2506.11335

Measuring and Minimizing Disturbance of Marine Animals to Underwater Vehicles
Do fish respond to the presence of underwater vehicles, potentially biasing our estimates about them? If so, are there strategies to measure and mitigate this response? This work provides a theoretical and practical framework towards bias-free estimation of animal behavior from underwater vehicle observations. We also provide preliminary results from the field in coral reef environments to address these questions.

Technical Evaluation of a Disruptive Approach in Homomorphic AI
Eric Filiol
https://arxiv.org/abs/2506.11954 https://arxiv.org/pdf/25…

Technical Evaluation of a Disruptive Approach in Homomorphic AI
We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. As a main claim, HbHAI makes now possible to analyze and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented perf…

Enabling Syscall Intercept for RISC-V
Petar Andri\'c, Aaron Call, Ramon Nou
https://arxiv.org/abs/2505.10217 https://arxiv.org/pd…

Enabling Syscall Intercept for RISC-V
The European Union technological sovereignty strategy centers around the RISC-V Instruction Set Architecture, with the European Processor Initiative leading efforts to build production-ready processors. Focusing on realizing a functional RISC-V ecosystem, the BZL initiative (www.bzl.es) is making an effort to create a software stack along with the hardware. In this work, we detail the efforts made in porting a widely used syscall interception library, mainly used on AdHocFS (i.e., DAOS, GekkoFS…

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…

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…

Forgetful by Design? A Critical Audit of YouTube's Search API for Academic Research
Bernhard Rieder, Adrian Padilla, Oscar Coromina
https://arxiv.org/abs/2506.11727

Forgetful by Design? A Critical Audit of YouTube's Search API for Academic Research
This paper critically audits the search endpoint of YouTube's Data API (v3), a common tool for academic research. Through systematic weekly searches over six months using eleven queries, we identify major limitations regarding completeness, representativeness, consistency, and bias. Our findings reveal substantial differences between ranking parameters like relevance and date in terms of video recall and precision, with relevance often retrieving numerous off-topic videos. We also find severe t…

On Differential and Boomerang Properties of a Class of Binomials over Finite Fields of Odd Characteristic
Namhun Koo, Soonhak Kwon
https://arxiv.org/abs/2506.11486

On Differential and Boomerang Properties of a Class of Binomials over Finite Fields of Odd Characteristic
In this paper, we investigate the differential and boomerang properties of a class of binomial $F_{r,u}(x) = x^r(1 + uχ(x))$ over the finite field $\mathbb{F}_{p^n}$, where $r = \frac{p^n+1}{4}$, $p^n \equiv 3 \pmod{4}$, and $χ(x) = x^{\frac{p^n -1}{2}}$ is the quadratic character in $\mathbb{F}_{p^n}$. We show that $F_{r,\pm1}$ is locally-PN with boomerang uniformity $0$ when $p^n \equiv 3 \pmod{8}$. To the best of our knowledge, the second known non-PN function class with boomerang uniformi…

Near-extremal holographic charge correlators
Blaise Gout\'eraux, David M. Ramirez, Mikel Sanchez-Garitaonandia, Cl\'ement Supiot
https://arxiv.org/abs/2506.11974

Near-extremal holographic charge correlators
We analytically compute the low-temperature charge correlators in near-extremal black holes with a planar horizon and an infrared $\mathrm{AdS}_2\times \Bbb{R}^2$ extremal geometry, finding excellent agreement with numerical calculations. The analytical result consistently describes the crossover between the hydrodynamic diffusive regime at low frequencies and wavenumbers, and the quantum, zero-temperature regime at high frequencies and wavenumbers. We analytically resolve the successive collis…

[2025-06-16 Mon (UTC), 1 new article found for cs.FL Formal Languages and Automata Theory]
toXiv_bot_toot

A Performance Model for Warp Specialization Kernels
Zhengyang Liu, Vinod Grover
https://arxiv.org/abs/2506.11209 https://arxiv.org/pd…

A Performance Model for Warp Specialization Kernels
This paper presents a performance model tailored for warp specialization kernels, focusing on factors such as warp size, tilling size, input matrix size, memory bandwidth, and thread divergence. Our model offers accurate predictions of execution time by leveraging differential equations validated through simulations and experiments. The insights gained from this model not only enhance our understanding of warp specialization techniques but also have practical implications for optimizing GPU-acc…

Robust Optimal Task Planning to Maximize Battery Life
Jiachen Li, Chu Jian, Feiyang Zhao, Shihao Li, Wei Li, Dongmei Chen
https://arxiv.org/abs/2506.11264 …

Robust Optimal Task Planning to Maximize Battery Life
This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement of fast AMR task planning while maintaining minimum battery state of charge, thus maximizing the battery life, renders a bilinear optimization problem. McCormick envelop technique is proposed to linearize the bilinear term. A novel planning algorithm with relaxed constraints is also developed to handle parameter unce…

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches
Ranindya Paramitha, Yuan Feng, Fabio Massacci
https://arxiv.org/abs/2506.11939

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches
Vulnerability datasets used for ML testing implicitly contain retrospective information. When tested on the field, one can only use the labels available at the time of training and testing (e.g. seen and assumed negatives). As vulnerabilities are discovered across calendar time, labels change and past performance is not necessarily aligned with future performance. Past works only considered the slices of the whole history (e.g. DiverseVUl) or individual differences between releases (e.g. Jimene…

This https://arxiv.org/abs/2504.16251 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

Adaptive and Efficient Dynamic Memory Management for Hardware Enclaves
The second version of Intel Software Guard Extensions (Intel SGX), or SGX2, adds dynamic management of enclave memory and threads. The first version required the address space and thread counts to be fixed before execution. The Enclave Dynamic Memory Management (EDMM) feature of SGX2 has the potential to lower launch times and overall execution time. Despite reducing the enclave loading time by 28--93%, straightforward EDMM adoption strategies actually slow execution time down by as much as 58%…

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…

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…

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng
https://arxiv.org/abs/2506.11603 https://

TongSearch-QR: Reinforced Query Reasoning for Retrieval
Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale language models like GPT-4 or LLaMA3-70B remains impractical …

Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees
Anders E. Kal{\o}r, Tomoaki Ohtsuki
https://arxiv.org/abs/2506.11391 https…

Black-Box Edge AI Model Selection with Conformal Latency and Accuracy Guarantees
Edge artificial intelligence (AI) will be a central part of 6G, with powerful edge servers supporting devices in performing machine learning (ML) inference. However, it is challenging to deliver the latency and accuracy guarantees required by 6G applications, such as automated driving and robotics. This stems from the black-box nature of ML models, the complexities of the tasks, and the interplay between transmitted data quality, chosen inference model, and the random wireless channel. This pap…

Special Functions and Geometries in Scattering Amplitudes: From Particle Physics to Gravity
Roger Morales
https://arxiv.org/abs/2506.11911 https://

Special Functions and Geometries in Scattering Amplitudes: From Particle Physics to Gravity
This thesis focuses on the fields of scattering amplitudes and Feynman integrals, with an emphasis on the geometries and special functions that they involve, and is devoted to two distinct research directions. In the first half of the thesis, we explore elliptic Feynman integrals in maximally supersymmetric Yang-Mills (N = 4 SYM) theory. In particular, we study elliptic generalizations of ladder diagrams, identifying the first two families of Feynman integrals involving the same elliptic curve …

[2025-06-16 Mon (UTC), 3 new articles found for cs.PL Programming Languages]
toXiv_bot_toot

Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
Christian Brommer, Alessandro Fornasier, Jan Steinbrener, Stephan Weiss
https://arxiv.org/abs/2506.11263

Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined sensor models. Sensor model definitions may require states for rigid-body calibrations and dedicated reference frames to replicate a measurement based on the robot's localization state. A health metric is…

Differential Privacy in Machine Learning: From Symbolic AI to LLMs
Francisco Aguilera-Mart\'inez, Fernando Berzal
https://arxiv.org/abs/2506.11687 http…

Differential Privacy in Machine Learning: From Symbolic AI to LLMs
Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm, thus limiting the exposure of private information. This survey paper explores the foundational definitions of differential privacy, reviews its original formulations and tracing its evolution through…

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…

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…

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…

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
Abdul Haque, Umm e Hani, Ahmad Din, Muhammad Babar, Ali Abbas, Insaf Ullah
https://arxiv.org/abs/2506.11600

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based s…

The complete weight distribution of a family of irreducible cyclic codes of dimension two
Gerardo Vega, F\'elix Hern\'andez
https://arxiv.org/abs/2506.11349

The complete weight distribution of a family of irreducible cyclic codes of dimension two
An important family of codes for data storage systems, cryptography, consumer electronics, and network coding for error control in digital communications are the so-called cyclic codes. This kind of linear codes are also important due to their efficient encoding and decoding algorithms. Because of this, cyclic codes have been studied for many years, however their complete weight distributions are known only for a few cases. The complete weight distribution has a wide range of applications in ma…

Symbol Alphabets in QCD and Flag Cluster Algebras
Andrzej Pokraka, Marcus Spradlin, Anastasia Volovich, He-Chen Weng
https://arxiv.org/abs/2506.11895 https…

Demonstration Sidetracks: Categorizing Systematic Non-Optimality in Human Demonstrations
Shijie Fang, Hang Yu, Qidi Fang, Reuben M. Aronson, Elaine S. Short
https://arxiv.org/abs/2506.11262

Demonstration Sidetracks: Categorizing Systematic Non-Optimality in Human Demonstrations
Learning from Demonstration (LfD) is a popular approach for robots to acquire new skills, but most LfD methods suffer from imperfections in human demonstrations. Prior work typically treats these suboptimalities as random noise. In this paper we study non-optimal behaviors in non-expert demonstrations and show that they are systematic, forming what we call demonstration sidetracks. Using a public space study with 40 participants performing a long-horizon robot task, we recreated the setup in si…

LLMs on support of privacy and security of mobile apps: state of the art and research directions
Tran Thanh Lam Nguyen, Barbara Carminati, Elena Ferrari
https://arxiv.org/abs/2506.11679

LLMs on support of privacy and security of mobile apps: state of the art and research directions
Modern life has witnessed the explosion of mobile devices. However, besides the valuable features that bring convenience to end users, security and privacy risks still threaten users of mobile apps. The increasing sophistication of these threats in recent years has underscored the need for more advanced and efficient detection approaches. In this chapter, we explore the application of Large Language Models (LLMs) to identify security risks and privacy violations and mitigate them for the mobile…

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…

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…

Gondola: Grounded Vision Language Planning for Generalizable Robotic Manipulation
Shizhe Chen, Ricardo Garcia, Paul Pacaud, Cordelia Schmid
https://arxiv.org/abs/2506.11261

Gondola: Grounded Vision Language Planning for Generalizable Robotic Manipulation
Robotic manipulation faces a significant challenge in generalizing across unseen objects, environments and tasks specified by diverse language instructions. To improve generalization capabilities, recent research has incorporated large language models (LLMs) for planning and action execution. While promising, these methods often fall short in generating grounded plans in visual environments. Although efforts have been made to perform visual instructional tuning on LLMs for robotic manipulation,…

Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Shanfan Zhang, Yongyi Lin, Yuan Rao, Chenlong Zhang
https://arxiv.org/abs/2506.11538

Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Disentangling user intentions from implicit feedback has become a promising strategy to enhance recommendation accuracy and interpretability. Prior methods often model intentions independently and lack explicit supervision, thus failing to capture the joint semantics that drive user-item interactions. To address these limitations, we propose DMICF, a unified framework that explicitly models interaction-level intent alignment while leveraging structural signals from both user and item perspectiv…

On the High-Rate FDPC Codes: Construction, Encoding, and a Generalization
Mohsen Moradi, Sheida Rabeti, Hessam Mahdavifar
https://arxiv.org/abs/2506.11345 …

On the High-Rate FDPC Codes: Construction, Encoding, and a Generalization
Recently introduced Fair-Density Parity-Check (FDPC) codes, targeting high-rate applications, offer superior error-correction performance (ECP) compared to 5G Low-Density Parity-Check (LDPC) codes, given the same number of message-passing decoding iterations. In this paper, we present a novel construction method for FDPC codes, introduce a generalization of these codes, and propose a low-complexity encoding algorithm. Numerical results demonstrate the fast convergence of the message-passing dec…

DTHA: A Digital Twin-Assisted Handover Authentication Scheme for 5G and Beyond
Guanjie Li, Tom H. Luan, Chengzhe Lai, Jinkai Zheng, Rongxing Lu
https://arxiv.org/abs/2506.11669

DTHA: A Digital Twin-Assisted Handover Authentication Scheme for 5G and Beyond
With the rapid development and extensive deployment of the fifth-generation wireless system (5G), it has achieved ubiquitous high-speed connectivity and improved overall communication performance. Additionally, as one of the promising technologies for integration beyond 5G, digital twin in cyberspace can interact with the core network, transmit essential information, and further enhance the wireless communication quality of the corresponding mobile device (MD). However, the utilization of milli…

Progress in Einstein-Cartan gravity
Mikhail Shaposhnikov
https://arxiv.org/abs/2506.11847 https://arxiv.org/pdf/2506.11847

Progress in Einstein-Cartan gravity
It is well-known that the gravitational force can be obtained by gauging the Lorentz group, which puts gravity on the same footing as the Standard Model fields. The resulting theory - Einstein-Cartan gravity - has several crucial advantages. I will overview the construction of the Weyl-invariant version of this theory and discuss its applications in particle physics and cosmology, in particular for inflation and the strong CP problem.

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…

[2025-06-16 Mon (UTC), 5 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
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Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
Luke Rowe, Rodrigue de Schaetzen, Roger Girgis, Christopher Pal, Liam Paull
https://arxiv.org/abs/2506.11234

Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
We present Poutine, a 3B-parameter vision-language model (VLM) tailored for end-to-end autonomous driving in long-tail driving scenarios. Poutine is trained in two stages. To obtain strong base driving capabilities, we train Poutine-Base in a self-supervised vision-language-trajectory (VLT) next-token prediction fashion on 83 hours of CoVLA nominal driving and 11 hours of Waymo long-tail driving. Accompanying language annotations are auto-generated with a 72B-parameter VLM. Poutine is obtained …

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
Shaun Shuster, Eyal Zaloof, Asaf Shabtai, Rami Puzis
https://arxiv.org/abs/2506.11635

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
The continuous growth of the e-commerce industry attracts fraudsters who exploit stolen credit card details. Companies often investigate suspicious transactions in order to retain customer trust and address gaps in their fraud detection systems. However, analysts are overwhelmed with an enormous number of alerts from credit card transaction monitoring systems. Each alert investigation requires from the fraud analysts careful attention, specialized knowledge, and precise documentation of the out…

A Reference Model and Patterns for Production Event Data Enrichment
Mark van der Pas, Remco Dijkman, Alp Ak\c{c}ay, Ivo Adan, John Walker
https://arxiv.org/abs/2506.11502

A Reference Model and Patterns for Production Event Data Enrichment
With the advent of digital transformation, organisations are increasingly generating large volumes of data through the execution of various processes across disparate systems. By integrating data from these heterogeneous sources, it becomes possible to derive new insights essential for tasks such as monitoring and analysing process performance. Typically, this information is extracted during a data pre-processing or engineering phase. However, this step is often performed in an ad-hoc manner an…

Decentralized Uplink Adaptive Compression for Cell-Free MIMO with Limited Fronthaul
Zehua Li, Jingjie Wei, Raviraj Adve
https://arxiv.org/abs/2506.11284 ht…

Decentralized Uplink Adaptive Compression for Cell-Free MIMO with Limited Fronthaul
We study the problem of uplink compression for cell-free multi-input multi-output networks with limited fronthaul capacity. In compress-forward mode, remote radio heads (RRHs) compress the received signal and forward it to a central unit for joint processing. While previous work has focused on a transform-based approach, which optimizes the transform matrix that reduces signals of high dimension to a static pre-determined lower dimension, we propose a rate-based approach that simultaneously fin…

On the Gravitational Origin of the QCD Axion
Georgios K. Karananas, Mikhail Shaposhnikov, Sebastian Zell
https://arxiv.org/abs/2506.11836 https://

On the Gravitational Origin of the QCD Axion
Gravity can give rise to (pseudo)scalar fields, for instance due to torsion. In particular, axions of gravitational origin have been proposed as a minimal and compelling solution to the strong CP problem. In this work, we critically examine the feasibility of this approach. We demonstrate that models in which the scalar field couples to fermionic currents only through derivatives do not yield a satisfactory axion. Moreover, we identify the necessary conditions for generating a gravitational axi…

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…

[2025-06-16 Mon (UTC), 21 new articles found for cs.RO Robotics]
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KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Zhijie Liu, Qiyi Tang, Sen Nie, Shi Wu, Liang Feng Zhang, Yutian Tang
https://arxiv.org/abs/2506.11612

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Binary code similarity analysis (BCSA) is a crucial research area in many fields such as cybersecurity. Specifically, function-level diffing tools are the most widely used in BCSA: they perform function matching one by one for evaluating the similarity between binary programs. However, such methods need a high time complexity, making them unscalable in large-scale scenarios (e.g., 1/n-to-n search). Towards effective and efficient program-level BCSA, we propose KEENHash, a novel hashing approach…

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…

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
Jieran Li, Xiuyuan Hu, Yang Zhao, Shengyao Zhuang, Hao Zhang
https://arxiv.org/abs/2506.11452

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
Large Language Models (LLMs) have demonstrated exceptional performance in the task of text ranking for information retrieval. While Pointwise ranking approaches offer computational efficiency by scoring documents independently, they often yield biased relevance estimates due to the lack of inter-document comparisons. In contrast, Pairwise methods improve ranking accuracy by explicitly comparing document pairs, but suffer from substantial computational overhead with quadratic complexity ($O(n^2)…

Bounds and New Constructions for Girth-Constrained Regular Bipartite Graphs
Sheida Rabeti, Mohsen Moradi, Hessam Mahdavifar
https://arxiv.org/abs/2506.11268

Bounds and New Constructions for Girth-Constrained Regular Bipartite Graphs
In this paper, we explore the design and analysis of regular bipartite graphs motivated by their application in low-density parity-check (LDPC) codes specifically with constrained girth and in the high-rate regime. We focus on the relation between the girth of the graph, and the size of the sets of variable and check nodes. We derive bounds on the size of the vertices in regular bipartite graphs, showing how the required number of check nodes grows with respect to the number of variable nodes a…

The Central Emission Vertex of two gravitons
Damiano Barcaro, Vittorio Del Duca
https://arxiv.org/abs/2506.11822 https://arxiv.org/pd…

The Central Emission Vertex of two gravitons
It has recently been shown that there exists an $s$-channel sequence of classical corrections to the $H$ diagram computed long ago by Amati, Ciafaloni and Veneziano. At leading logarithmic accuracy, those corrections feature the gravity BFKL kernel as a crucial element, and may be computed through either rapidity renormalisation group equations or amplitudes built through $s$-channel unitarity cuts. In this paper, we evaluate six-graviton amplitudes in next-to-multi-Regge kinematics, and comput…

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…

Dynamic Collaborative Material Distribution System for Intelligent Robots In Smart Manufacturing
Ziren Xiao, Ruxin Xiao, Chang Liu, Xinheng Wang
https://arxiv.org/abs/2506.11723

Dynamic Collaborative Material Distribution System for Intelligent Robots In Smart Manufacturing
The collaboration and interaction of multiple robots have become integral aspects of smart manufacturing. Effective planning and management play a crucial role in achieving energy savings and minimising overall costs. This paper addresses the real-time Dynamic Multiple Sources to Single Destination (DMS-SD) navigation problem, particularly with a material distribution case for multiple intelligent robots in smart manufacturing. Enumerated solutions, such as in \cite{xiao2022efficient}, tackle t…

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Shashank Balla
https://arxiv.org/abs/2506.11586 https://arxiv.org/pd…

SecONNds: Secure Outsourced Neural Network Inference on ImageNet
The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. …

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

[2025-06-16 Mon (UTC), 7 new articles found for cs.IT Information Theory]
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Soft Photon, Gluon and Graviton Theorems in (A)dS from Conformal Invariance
Jiajie Mei, Yuyu Mo
https://arxiv.org/abs/2506.11766 https://

Soft Photon, Gluon and Graviton Theorems in (A)dS from Conformal Invariance
We present new soft theorems for photon, gluon, and graviton correlators at tree level in (Anti)-de Sitter space. The results are derived by applying conformal Ward identities to constrain the structure of Witten diagrams.

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…

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models
Jinming Wen, Xinyi Wu, Shuai Zhao, Yanhao Jia, Yuwen Li
https://arxiv.org/abs/2506.11521

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models
Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computational resources necessitates the utilization of third-party data and open-source MLLMs, a trend that is increasingly observed in contemporary research. This prosperity masks significant security risks. E…

[2025-06-16 Mon (UTC), 8 new articles found for cs.IR Information Retrieval]
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Duality and Infinite Distance Limits in Asymmetric Freely Acting Orbifolds
George Gkountoumis, Chris Hull, Guo-En Nian, Stefan Vandoren
https://arxiv.org/abs/2506.11699

Duality and Infinite Distance Limits in Asymmetric Freely Acting Orbifolds
We use freely acting asymmetric orbifolds of type IIB string theory to construct a class of theories in four dimensions with eight supercharges. Their low energy effective field theories resemble $STU$ models, but have different duality groups: the orbifold's free action reduce the duality groups to congruence subgroups of the modular group. The fundamental domain is consequently larger and contains new interesting points at infinite distance on the real axis bounding the upper half plane. We v…

[2025-06-16 Mon (UTC), 9 new articles found for cs.CY Computers and Society]
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Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning
Dustin Ray, Caroline El Jazmi
https://arxiv.org/abs/2506.11458 https…

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning
Machine-learning systems continue to advance at a rapid pace, demonstrating remarkable utility in various fields and disciplines. As these systems continue to grow in size and complexity, a nascent industry is emerging which aims to bring machine-learning-as-a-service (MLaaS) to market. Outsourcing the operation and training of these systems to powerful hardware carries numerous advantages, but challenges arise when privacy and the correctness of work carried out must be ensured. Recent advance…

GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
Kecen Li, Zhicong Huang, Xinwen Hou, Cheng Hong
https://arxiv.org/abs/2506.11444 https://

GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
As Diffusion Models (DM) generate increasingly realistic images, related issues such as copyright and misuse have become a growing concern. Watermarking is one of the promising solutions. Existing methods inject the watermark into the single-domain of initial Gaussian noise for generation, which suffers from unsatisfactory robustness. This paper presents the first dual-domain DM watermarking approach using a pipelined injector to consistently embed watermarks in both the spatial and frequency d…

Exploring Entanglement Entropy for a Particle on a Torus with constant Metric and constant $U(1)$-Gauge Field
Helmuth Huffel, Gerald Kelnhofer
https://arxiv.org/abs/2506.11292

Exploring Entanglement Entropy for a Particle on a Torus with constant Metric and constant $U(1)$-Gauge Field
In this paper, we focus on the entanglement entropy associated with a particle confined to a torus with constant metric and $θ$-terms related to a constant external $U(1)$-gauge field. Through this investigation, we aim to elucidate the interplay between geometric properties, topological terms, and entanglement entropy. Remarkably, we find that the presence of non-vanishing entanglement entropy is closely tied to fixing the metric. This insight highlights the significant influence of quantum e…

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure
Timothy Dubber, Seth Lazar
https://arxiv.org/abs/2506.12094 https:/…

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure
This paper argues that autonomous AI cyber-weapons - Military-AI Cyber Agents (MAICAs) - create a credible pathway to catastrophic risk. It sets out the technical feasibility of MAICAs, explains why geopolitics and the nature of cyberspace make MAICAs a catastrophic risk, and proposes political, defensive-AI and analogue-resilience measures to blunt the threat.

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Manish Bhatt
https://arxiv.org/abs/2506.11423 https://

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Debates about whether Large Language or Reasoning Models (LLMs/LRMs) truly reason or merely pattern-match suffer from shifting goal posts. In my personal opinion, two analytic--hence "tautological"--benchmarks cut through that fog in my mental model. In this paper, I attempt to write down my mental model in concrete terms.

The building blocks of asymptotically flat spacetimes
Shadi Ali Ahmad
https://arxiv.org/abs/2506.11190 https://arxiv.org/pdf/2506.111…

The building blocks of asymptotically flat spacetimes
The classification of all possible induced representations arising from theories admitting a Poincaré symmetry has molded our very conception of particles in flat space. In this note, we argue that if one takes the same viewpoint on the asymptotic symmetry group instead of the global one, the familiar relativistic classification is enhanced to provide a new notion of excitations in asymptotically flat spacetimes. These excitations blur the line between the quantum description of a particle and…

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Evangelos Froudakis, Athanasios Avgetidis, Sean Tyler Frankum, Roberto Perdisci, Manos Antonakakis, Angelos Keromytis
https://arxiv.org/abs/2506.11325

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Indicators of Compromise (IoCs) are critical for threat detection and response, marking malicious activity across networks and systems. Yet, the effectiveness of automated IoC extraction systems is fundamentally limited by one key issue: the lack of high-quality ground truth. Current extraction tools rely either on manually extracted ground truth, which is labor-intensive and costly, or on automated ground truth creation methods that include non-malicious artifacts, leading to inflated false po…

[2025-06-16 Mon (UTC), 10 new articles found for hep-th High Energy Physics - Theory]
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User Perceptions and Attitudes Toward Untraceability in Messaging Platforms
Carla F. Griggio, Boel Nelson, Zefan Sramek, Aslan Askarov
https://arxiv.org/abs/2506.11212

User Perceptions and Attitudes Toward Untraceability in Messaging Platforms
Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as disappearing messages, password-protected chats, and end-to-end encryption (E2EE), which primarily protect message contents. Beyond contents, the transmission of messages generates metadata that can reveal who communicates with whom, when and how often. In this paper, we study user perceptions of "untraceability", i.e., preventing third parties from tracing who communicates with whom, with the g…

[2025-06-16 Mon (UTC), 15 new articles found for cs.CR Cryptography and Security]
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Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
Revisiting Main Memory-Based Covert and Side Channel Attacks in the Context of Processing-in-Memory

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…