Tootfinder

Lightweight Relevance Grader in RAG
Taehee Jeong
https://arxiv.org/abs/2506.14084 https://arxiv.org/pdf/2506.14084

Lightweight Relevance Grader in RAG
Retrieval-Augmented Generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more accurate and up-to-date information. When a user submits a query, RAG executes a vector search to find relevant documents, which are then used to generate a response. However, ensuring the relevance of retrieved documents with a query would be a big challenge. To address this, a secondary model, known as a relevant grader, can be served to verify its relevan…

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Naomi Fridman, Bubby Solway, Tomer Fridman, Itamar Barnea, Anat Goldshtein
https://arxiv.org/abs/2506.12190

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized…

GAF: Gaussian Action Field as a Dvnamic World Model for Robotic Mlanipulation
Ying Chai, Litao Deng, Ruizhi Shao, Jiajun Zhang, Liangjun Xing, Hongwen Zhang, Yebin Liu
https://arxiv.org/abs/2506.14135

GAF: Gaussian Action Field as a Dvnamic World Model for Robotic Mlanipulation
Accurate action inference is critical for vision-based robotic manipulation. Existing approaches typically follow either a Vision-to-Action (V-A) paradigm, predicting actions directly from visual inputs, or a Vision-to-3D-to-Action (V-3D-A) paradigm, leveraging intermediate 3D representations. However, these methods often struggle with action inaccuracies due to the complexity and dynamic nature of manipulation scenes. In this paper, we propose a V-4D-A framework that enables direct action reas…

Locating-dominating partitions for some classes of graphs
Florent Foucaud, Paras Vinubhai Maniya, Kaustav Paul, Dinabandhu Pradhan
https://arxiv.org/abs/2506.12933

Locating-dominating partitions for some classes of graphs
A dominating set of a graph $G$ is a set $D \subseteq V(G)$ such that every vertex in $V(G) \setminus D$ is adjacent to at least one vertex in $D$. A set $L\subseteq V(G)$ is a locating set of $G$ if every vertex in $V(G) \setminus L$ has pairwise distinct open neighborhoods in $L$. A set $D\subseteq V(G)$ is a locating-dominating set of $G$ if $D$ is a dominating set and a locating set of $G$. The location-domination number of $G$, denoted by $γ_{LD}(G)$, is the minimum cardinality among all …

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour
Jack Contro, Simrat Deol, Yulan He, Martim Brand\~ao
https://arxiv.org/abs/2506.12090

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour
This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated b…

Done some more work with the VR animation engine today.
Seems to now be working that I can lay in the path that the character will walk, and then puppet their limbs as they walk it.
Done more work to standardize how all the slapdash episode structure is so now there's also a #wip #starshipsd #gamedev

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…

Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing
Burak Aydin, Emre Parmaksiz, Ronnie Sircar
https://arxiv.org/abs/2506.13088

Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing
We study the dynamic pricing of discrete goods over a finite selling horizon. One way to capture both the elastic and stochastic reaction of purchases to price is through a model where sellers control the intensity of a counting process, representing the number of sales thus far. The intensity describes the probabilistic likelihood of a sale, and is a decreasing function of the price a seller sets. A classical model for ticket pricing, which assumes a single seller and infinite time horizon, is…

A Universe with large Continuum, global $\Sigma$-Uniformization and a projective Well-Order of its Reals
Stefan Hoffelner
https://arxiv.org/abs/2506.12393 …

A Universe with large Continuum, global $Σ$-Uniformization and a projective Well-Order of its Reals
We construct a model in which the continuum has size $κ$ for a regular cardinal $κ$ and in which the $Σ^1_n$-uniformization property holds simultaneously for every $n \ge 2$. Additionally this model has a $Δ^1_3$- definable well-order of its reals.

Statistical Description of Fermi System over a Surface in a Uniform External Field
Yu. M. Poluektov, A. A. Soroka
https://arxiv.org/abs/2506.12608 https://…

Statistical Description of Fermi System over a Surface in a Uniform External Field
A statistical approach to the description of the thermodynamic properties of the Fermi particle system occupying a half-space over a plane of finite size in a uniform external field is proposed. The number of particles per unit area is assumed to be arbitrary, in particular, small. General formulas are obtained for entropy, energy, thermodynamic potential, heat capacities under various conditions and the distribution of the particle number density over the surface. In the continuum limit of a l…

Level structures on parahoric torsors and complete integrability
Georgios Kydonakis, Lutian Zhao
https://arxiv.org/abs/2506.12302 https://

Level structures on parahoric torsors and complete integrability
For a smooth complex algebraic curve $X$ and a reduced effective divisor $D$ on $X$, we introduce a notion of $D$-level structure on parahoric $\mathcal{G}_{\boldsymbol θ}$-torsors over $X$, for any connected complex reductive Lie group $G$. A moduli space of parahoric $\mathcal{G}_{\boldsymbol θ}$-torsors equipped with a $D$-level structure is constructed and we identify a canonical moment map with respect to the action of a level group on this moduli space. This action extends to a Poisson …

A Transfer Learning Framework for Multilayer Networks via Model Averaging
Yongqin Qiu, Xinyu Zhang
https://arxiv.org/abs/2506.12455 https://

A Transfer Learning Framework for Multilayer Networks via Model Averaging
Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A $K$-fold cross-validation criterion based on edg…

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

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

Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure
Zheyuan Yang, Zexi Kuang, Xue Xia, Yilun Zhao
https://arxiv.org/abs/2506.12278

Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure
We introduce TestCase-Eval, a new benchmark for systematic evaluation of LLMs in test-case generation. TestCase-Eval includes 500 algorithm problems and 100,000 human-crafted solutions from the Codeforces platform. It focuses on two pivotal tasks: (1) Fault Coverage, which measures how well LLM-generated test sets probe diverse input scenarios and cover a wide range of potential failure modes. (2) Fault Exposure, which evaluates whether LLMs can craft a tailored test input that reveals a specif…

Improving Public Service Chatbot Design and Civic Impact: Investigation of Citizens' Perceptions of a Metro City 311 Chatbot
Jieyu Zhou, Rui Shen, Yue You, Carl DiSalvo, Lynn Dombrowski, Christopher MacLellan
https://arxiv.org/abs/2506.12259

Improving Public Service Chatbot Design and Civic Impact: Investigation of Citizens' Perceptions of a Metro City 311 Chatbot
As governments increasingly adopt digital tools, public service chatbots have emerged as a growing communication channel. This paper explores the design considerations and engagement opportunities of public service chatbots, using a 311 chatbot from a metropolitan city as a case study. Our qualitative study consisted of official survey data and 16 interviews examining stakeholder experiences and design preferences for the chatbot. We found two key areas of concern regarding these public chatbot…

Flocking as a second-order phase transition in self-aligning active crystals
Marco Musacchio, Alexander P. Antonov, Hartmut L\"owen, Lorenzo Caprini
https://arxiv.org/abs/2506.12967

Flocking as a second-order phase transition in self-aligning active crystals
We study a two-dimensional crystal composed of active units governed by self-alignment. This mechanism induces a torque that aligns a particle's orientation with its velocity and leads to a phase transition from a disordered to a flocking crystal. Here, we provide the first microscopic theory that analytically maps the crystal dynamics onto a Landau-Ginzburg model, in which the velocity-dependent effective free energy undergoes a transition from a single-well shape to a Mexican-hat profile. As …

A degree-counting formula for a Keller-Segel equation on a surface with boundary
Mohameden Ahmedou, Zhengni Hu, Heming Wang
https://arxiv.org/abs/2506.12783

A degree-counting formula for a Keller-Segel equation on a surface with boundary
In this paper, we consider the following Keller-Segel equation on a compact Riemann surface $(Σ, g)$ with smooth boundary $\partialΣ$: \[ -Δ_g u = ρ\Big(\frac{V e^u}{\int_Σ V e^u \mathrm{d} v_g} - \frac{1}{|Σ|_g}\Big) \text{ in } Σ, \quad \text{ with } \partial_{ν_g} u = 0 \text{ on } \partial Σ, \] where $V$ is a smooth positive function on $Σ$ and $ρ> 0$ is a parameter. We perform a refined blow-up analysis of bubbling solutions and establish sharper a priori esti…

Freer Arrows and Why You Need Them in Haskell
Grant VanDomelen, Gan Shen, Lindsey Kuper, Yao Li
https://arxiv.org/abs/2506.12212 https://

Freer Arrows and Why You Need Them in Haskell
Freer monads are a useful structure commonly used in various domains due to their expressiveness. However, a known issue with freer monads is that they are not amenable to static analysis. This paper explores freer arrows, a relatively expressive structure that is amenable to static analysis. We propose several variants of freer arrows. We conduct a case study on choreographic programming to demonstrate the usefulness of freer arrows in Haskell.

Collaborative Beamforming for Communication Applications Using a Two-Element Fully-Wireless Open-Loop Coherent Distributed Array
Jason M. Merlo, Jeffrey A. Nanzer
https://arxiv.org/abs/2506.13014

Collaborative Beamforming for Communication Applications Using a Two-Element Fully-Wireless Open-Loop Coherent Distributed Array
In this work we demonstrate a proof of concept of a fully-wireless two-node open-loop coherent distributed communication system and evaluate its performance by transmitting QPSK , 64-, and 256-QAM constellations at a symbol rate of 2 MBd over a 58 m link in an urban environment. The system is implemented in a distributed manner with on-node processing using software-defined radios (SDRs) and wireless internode communication to share coordination information and does not rely on external time or…

POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
Pengyu Liu, Jatin Arora, Mingkuan Xu, Umut A. Acar
https://arxiv.org/abs/2506.13720 ht…

POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
Optimization of quantum programs or circuits is a fundamental problem in quantum computing and remains a major challenge. State-of-the-art quantum circuit optimizers rely on heuristics and typically require superlinear, and even exponential, time. Recent work proposed a new approach that pursues a weaker form of optimality called local optimality. Parameterized by a natural number $Ω$, local optimality insists that each and every $Ω$-segment of the circuit is optimal with respect to an extern…

Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models
David Kohns, Tibor Szendrei
https://arxiv.org/abs/2506.13257 …

Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models
Crossing of fitted conditional quantiles is a prevalent problem for quantile regression models. We propose a new Bayesian modelling framework that penalises multiple quantile regression functions toward the desired non-crossing space. We achieve this by estimating multiple quantiles jointly with a prior on variation across quantiles, a fused shrinkage prior with quantile adaptivity. The posterior is derived from a decision-theoretic general Bayes perspective, whose form yields a natural state-s…

Purely quantum memory in closed systems observed via imperfect measurements
Jorge Tabanera-Bravo, Alja\v{z} Godec
https://arxiv.org/abs/2506.13689 https://…

Purely quantum memory in closed systems observed via imperfect measurements
The detection and quantification of non-Markovianity, a.k.a. memory, in quantum systems is a central problem in the theory of open quantum systems. There memory is as a result of the interaction between the system and its environment. Little is known, however, about memory effects induced by imperfect measurements on closed systems, where an entanglement with the environment is not possible. We investigate the emergence and characteristics of memory in closed systems observed via imperfect stro…

MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Bi Yuda, Jia Sihan, Gao Yutong, Abrol Anees, Fu Zening, Calhoun Vince
https://arxiv.org/abs/2506.13667

MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions and improve outcomes. This study focuses on a neuroimaging prediction framework based on both structural and functional neuroimaging data. We propose a next-generation prediction model, \textbf{MultiViT2}, which combines a pretrained representative learning base model with a vision transformer backbone for prediction o…

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

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

A look at California's 52-page California Report on Frontier Policy, which proposes an AI regulatory framework based on transparency, risk assessments, and more (Hayden Field/The Verge)
https://www.theverge.com/ai-artificial-int

California is trying to regulate its AI giants — again
California has released a safety report proposing new legislation after Governor Gavin Newsom vetoed SB 1047.

Cosmographical analysis of sign-switching dark energy
Mariam Bouhmadi-L\'opez, Be\~nat Ibarra-Uriondo
https://arxiv.org/abs/2506.12139 https://

Cosmographical analysis of sign-switching dark energy
Inspired by the well-studied $Λ_{\rm s}$CDM model, we propose and investigate a class of dynamical dark energy models that evolve from a negative cosmological constant, transitioning to a positive value at low redshifts. Specifically, we introduce a dark energy framework based on a generalised ladder-step function for the cosmological constant. Furthermore, we present two additional models in which the cosmological constant undergoes a smooth sign change at a specified redshift. We provide a d…

A polynomial delay algorithm generating all potential maximal cliques in triconnected planar graphs
Alexander Grigoriev, Yasuaki Kobayashi, Hisao Tamaki, Tom C. van der Zanden
https://arxiv.org/abs/2506.12635

A polynomial delay algorithm generating all potential maximal cliques in triconnected planar graphs
We develop a new characterization of potential maximal cliques of a triconnected planar graph and, using this characterization, give a polynomial delay algorithm generating all potential maximal cliques of a given triconnected planar graph. Combined with the dynamic programming algorithms due to Bouchitt{é} and Todinca, this algorithm leads to a treewidth algorithm for general planar graphs that runs in time linear in the number of potential maximal cliques and polynomial in the number of vert…

Tight Routing and Spanning Ratios of Arbitrary Triangle Delaunay Graphs
Prosenjit Bose, Jean-Lou De Carufel, John Stuart
https://arxiv.org/abs/2506.12625 h…

Tight Routing and Spanning Ratios of Arbitrary Triangle Delaunay Graphs
A Delaunay graph built on a planar point set has an edge between two vertices when there exists a disk with the two vertices on its boundary and no vertices in its interior. When the disk is replaced with an equilateral triangle, the resulting graph is known as a Triangle-Distance Delaunay Graph or TD-Delaunay for short. A generalized $\text{TD}_{θ_1,θ_2}$-Delaunay graph is a TD-Delaunay graph whose empty region is a scaled translate of a triangle with angles of $θ_1,θ_2,θ_3:=π-θ_1-θ_2$…

Self-organized criticality in a relativistic Yukawa theory with Luttinger fermions
Holger Gies, Marta Picciau
https://arxiv.org/abs/2506.13441 https://

Self-organized criticality in a relativistic Yukawa theory with Luttinger fermions
We propose and investigate a Yukawa model featuring a dynamical scalar field coupled to relativistic Luttinger fermions. Using the functional renormalization group (RG) as well as large-$N_{\text{f}}$ or perturbative expansions, we observe the emergence of an infrared attractive partial fixed point in all interactions at which all couplings become RG irrelevant. At the partial fixed point, the scalar mass parameter is RG marginal, featuring a slow logarithmic running towards the regime of spont…

The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness
Gnankan Landry Regis N'guessan, Issa Karambal
https://arxiv.org/abs/2506.13825

The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness
Research on artificial consciousness lacks the equivalent of the perceptron: a small, trainable module that can be copied, benchmarked, and iteratively improved. We introduce the Reflexive Integrated Information Unit (RIIU), a recurrent cell that augments its hidden state $h$ with two additional vectors: (i) a meta-state $μ$ that records the cell's own causal footprint, and (ii) a broadcast buffer $B$ that exposes that footprint to the rest of the network. A sliding-window covariance and a dif…

Three-dimensional Deep Shape Optimization with a Limited Dataset
Yongmin Kwon, Namwoo Kang
https://arxiv.org/abs/2506.12326 https://a…

Three-dimensional Deep Shape Optimization with a Limited Dataset
Generative models have attracted considerable attention for their ability to produce novel shapes. However, their application in mechanical design remains constrained due to the limited size and variability of available datasets. This study proposes a deep learning-based optimization framework specifically tailored for shape optimization with limited datasets, leveraging positional encoding and a Lipschitz regularization term to robustly learn geometric characteristics and maintain a meaningful…

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Tianlin Zhang, Linzhu Yue, Hongbo Zhang, Lingwei Zhang, Xuanqi Zeng, Zhitao Song, Yun-Hui Liu
https://arxiv.org/abs/2506.14278

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Humanoid robots often face significant balance issues due to the motion of their heavy limbs. These challenges are particularly pronounced when attempting dynamic motion or operating in environments with irregular terrain. To address this challenge, this manuscript proposes a whole-body control framework for humanoid robots with heavy limbs, using a model-based approach that combines a kino-dynamics planner and a hierarchical optimization problem. The kino-dynamics planner is designed as a mode…

On the structure of perfectly divisible graphs
Ch\'inh T. Ho\`ang
https://arxiv.org/abs/2506.12660 https://arxiv.org/pdf/2506.126…

On the structure of perfectly divisible graphs
A graph $G$ is perfectly divisible if every induced subgraph $H$ of $G$ contains a set $X$ of vertices such that $X$ meets all largest cliques of $H$, and $X$ induces a perfect graph. The chromatic number of a perfectly divisible graph $G$ is bounded by $ω^2$ where $ω$ denotes the number of vertices in a largest clique of $G$. A graph $G$ is minimally non-perfectly divisible if $G$ is not perfectly divisible but each of its proper induced subgraph is. A set $C$ of vertices of $G$ is a clique …

A bang-bang solution with infinitely many switching points for a parabolic boundary control problem with terminal observation
Constantin Christof
https://arxiv.org/abs/2506.12768 …

A bang-bang solution with infinitely many switching points for a parabolic boundary control problem with terminal observation
We study a parabolic boundary control problem with one spatial dimension, control constraints of box type, and an objective function that measures the $L^2$-distance to a desired terminal state. It is shown that, for a certain choice of the desired state, the considered problem possesses an optimal control that is chattering, i.e., of bang-bang type with infinitely many switching points and a positive objective function value. Whether such a solution is possible has been an open question in the…

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

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

A Mapping Study About Training in Industry Context in Software Engineering
Breno Alves de Andrade, Rodrigo Siqueira, Lidiane Gomes, Antonio Oliveira, Danilo Monteiro Ribeiro
https://arxiv.org/abs/2506.12590

A Mapping Study About Training in Industry Context in Software Engineering
Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed, implemented, and evaluated within this domain. Objective: This study aims to map the current state of research on corporate training in software engineering in industry settings, using Eduardo Salas' training framework as an analytical lens. Method: A systematic…

Focusing on Students, not Machines: Grounded Question Generation and Automated Answer Grading
G\'er\^ome Meyer, Philip Breuer
https://arxiv.org/abs/2506.12066

Focusing on Students, not Machines: Grounded Question Generation and Automated Answer Grading
Digital technologies are increasingly used in education to reduce the workload of teachers and students. However, creating open-ended study or examination questions and grading their answers is still a tedious task. This thesis presents the foundation for a system that generates questions grounded in class materials and automatically grades student answers. It introduces a sophisticated method for chunking documents with a visual layout, specifically targeting PDF documents. This method enhance…

StacKAT: Infinite State Network Verification
Jules Jacobs, Nate Foster, Tobias Kapp\'e, Dexter Kozen, Lily Saada, Alexandra Silva, Jana Wagemaker
https://arxiv.org/abs/2506.13383

StacKAT: Infinite State Network Verification
We develop StacKAT, a network verification language featuring loops, finite state variables, nondeterminism, and - most importantly - access to a stack with accompanying push and pop operations. By viewing the variables and stack as the (parsed) headers and (to-be-parsed) contents of a network packet, StacKAT can express a wide range of network behaviors including parsing, source routing, and telemetry. These behaviors are difficult or impossible to model using existing languages like NetKAT. W…

Gromov-Witten theory of $\mathsf{Hilb}^n(\mathbb{C}^2)$ and Noether-Lefschetz theory of $\mathcal{A}_g$
Aitor Iribar Lopez, Rahul Pandharipande, Hsian-Hua Tseng
https://arxiv.org/abs/2506.12438

Gromov-Witten theory of $\mathsf{Hilb}^n(\mathbb{C}^2)$ and Noether-Lefschetz theory of $\mathcal{A}_g$
We calculate the genus 1 Gromov-Witten theory of the Hilbert scheme $\mathsf{Hilb}^n(\mathbb{C}^2)$ of points in the plane. The fundamental 1-point invariant (with a divisor insertion) is calculated using a correspondence with the families local curve Gromov-Witten theory over the moduli space $\overline{\mathcal{M}}_{1,1}$. The answer exactly matches a parallel calculation related to the Noether-Lefschetz geometry of the moduli space $\mathcal{A}_g$ of principally polarized abelian varieties. …

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

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

Variational Inference with Mixtures of Isotropic Gaussians
Marguerite Petit-Talamon, Marc Lambert, Anna Korba
https://arxiv.org/abs/2506.13613 https://

Variational Inference with Mixtures of Isotropic Gaussians
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a loss that is typically the (reverse) Kullback-Leibler (KL) divergence. In this paper, we focus on the following parametric family: mixtures of isotropic Gaussians (i.e., with diagonal covariance matrices proportional to the identity) and uniform weights. We develop a variational framework and provide efficient algori…

A new model of spontaneous scalarization of black holes induced by curvature and matter
Zakaria Belkhadria, Salvatore Mignemi
https://arxiv.org/abs/2506.12137

A new model of spontaneous scalarization of black holes induced by curvature and matter
We propose a new model of black hole spontaneous scalarization that combines a scalar--Gauss--Bonnet interaction with a non-minimal coupling to a $U(1)$ gauge field (a dark photon or an electromagnetic field). This construction generalizes earlier single-coupling setups and allows both curvature-induced and matter-induced scalarization within one framework, which allows us to overcome the limitations of each mechanism alone. We focus on charged, spherically symmetric black holes and demonstrate…

Extended Version of Paper Presented at ICISSP, Porto 20-22 February, 2025 A Value-Driven Approach to the Online Consent Conundrum -- A Study with the Unemployed
Paul van Schaik, Karen Renaud
https://arxiv.org/abs/2506.12244

Extended Version of Paper Presented at ICISSP, Porto 20-22 February, 2025 A Value-Driven Approach to the Online Consent Conundrum -- A Study with the Unemployed
Online services are required to gain informed consent from users to collect, store and analyse their personal data, both intentionally divulged and derived during their use of the service. There are many issues with these forms: they are too long, too complex and demand the user's attention too frequently. Many users consent without reading so do not know what they are agreeing to. As such,granted consent is effectively uninformed. In this paper, we report on two studies we carried out to arriv…

Distributed Computing From First Principles
Kenneth Odoh
https://arxiv.org/abs/2506.12959 https://arxiv.org/pdf/2506.12959

Distributed Computing From First Principles
This book on Distributed Computing aims to benefit a diverse audience, ranging from aspiring engineers, and seasoned researchers, to a wide range of professionals. Driven by my passion for making the core concepts of distributed computing accessible, this work is a significant undertaking designed to empower individuals from all backgrounds to gain valuable insight. Have you ever wondered how a typical distributed system works under the hood? Are you looking for a pedagogical guide with complet…

Relative Error Fair Clustering in the Weak-Strong Oracle Model
Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H. -C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou
https://arxiv.org/abs/2506.12287

Relative Error Fair Clustering in the Weak-Strong Oracle Model
We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances, but at a high cost, and a weak oracle providing potentially inaccurate distance estimates at a low cost. The goal is to produce a near-optimal fair clustering on $n$ input points with a minimum number of strong oracle queries. This models the increasingly common trade-off between accurate but expensive similarity measures (e.g., large-scale embedding…

A Two-stage Optimization Method for Wide-range Single-electron Quantum Magnetic Sensing
Shiqian Guo, Jianqing Liu, Thinh Le, Huaiyu Dai
https://arxiv.org/abs/2506.13469

A Two-stage Optimization Method for Wide-range Single-electron Quantum Magnetic Sensing
Quantum magnetic sensing based on spin systems has emerged as a new paradigm for detecting ultra-weak magnetic fields with unprecedented sensitivity, revitalizing applications in navigation, geo-localization, biology, and beyond. At the heart of quantum magnetic sensing, from the protocol perspective, lies the design of optimal sensing parameters to manifest and then estimate the underlying signals of interest (SoI). Existing studies on this front mainly rely on adaptive algorithms based on bla…

A note on valued fields with finite group actions
Piotr B{\l}aszkiewicz, Jakub Gogolok
https://arxiv.org/abs/2506.13742 https://arxiv…

A note on valued fields with finite group actions
In this preliminary note we prove that the theory of valued fields equipped with an action of a given finite group has a model companion.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Miguel A. Lago, Ghada Zamzmi, Brandon Eich, Jana G. Delfino
https://arxiv.org/abs/2506.13917

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Explainability features are intended to provide insight into the internal mechanisms of an AI device, but there is a lack of evaluation techniques for assessing the quality of provided explanations. We propose a framework to assess and report explainable AI features. Our evaluation framework for AI explainability is based on four criteria: 1) Consistency quantifies the variability of explanations to similar inputs, 2) Plausibility estimates how close the explanation is to the ground truth, 3) F…

Comparative Analysis of Deep Learning Strategies for Hypertensive Retinopathy Detection from Fundus Images: From Scratch and Pre-trained Models
Yanqiao Zhu
https://arxiv.org/abs/2506.12492

Comparative Analysis of Deep Learning Strategies for Hypertensive Retinopathy Detection from Fundus Images: From Scratch and Pre-trained Models
This paper presents a comparative analysis of deep learning strategies for detecting hypertensive retinopathy from fundus images, a central task in the HRDC challenge~\cite{qian2025hrdc}. We investigate three distinct approaches: a custom CNN, a suite of pre-trained transformer-based models, and an AutoML solution. Our findings reveal a stark, architecture-dependent response to data augmentation. Augmentation significantly boosts the performance of pure Vision Transformers (ViTs), which we hypo…

Finding a long cycle in a percolated vertex expander
Lawrence Hollom
https://arxiv.org/abs/2506.12162 https://arxiv.org/pdf/2506.1216…

Finding a long cycle in a percolated vertex expander
Given a graph $G$, the percolated graph $G_p$ has each edge independently retained with probability $p$. Collares, Diskin, Erde, and Krivelevich initiated the study of large structures in percolated single-scale vertex expanders, wherein every set of exactly $k$ vertices of $G$ has at least $dk$ neighbours before percolation. We extend their result to a conjectured stronger form, proving that if $p = (1+\varepsilon)/d$ and $G$ expands as above, then $G_p$ fails to contain a cycle of length $Ω_…

Data Driven Approach to Input Shaping for Vibration Suppression in a Flexible Robot Arm
Jarkko Kotaniemi, Janne Saukkoriipi, Shuai Li, Markku Suomalainen
https://arxiv.org/abs/2506.14405

Data Driven Approach to Input Shaping for Vibration Suppression in a Flexible Robot Arm
This paper presents a simple and effective method for setting parameters for an input shaper to suppress the residual vibrations in flexible robot arms using a data-driven approach. The parameters are adaptively tuned in the workspace of the robot by interpolating previously measured data of the robot's residual vibrations. Input shaping is a simple and robust technique to generate vibration-reduced shaped commands by a convolution of an impulse sequence with the desired input command. The gene…

A New Approach for the Continuous Time Kyle-Back Strategic Insider Equilibrium Problem
Bixing Qiao, Jianfeng Zhang
https://arxiv.org/abs/2506.12281 https:/…

A New Approach for the Continuous Time Kyle-Back Strategic Insider Equilibrium Problem
This paper considers a continuous time Kyle-Back model which is a game problem between an insider and a market marker. The existing literature typically focuses on the existence of equilibrium by using the PDE approach, which requires certain Markovian structure and the equilibrium is in the bridge form. We shall provide a new approach which is used widely for stochastic controls and stochastic differential games. We characterize all equilibria through a coupled system of forward backward SDEs,…

A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages
Tatiana Ankinina, Jan Cegin, Jakub Simko, Simon Ostermann
https://arxiv.org/abs/2506.12158

A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages
Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategies for low-resource language settings is lacking. While various prompting strategies have been proposed, such as demonstrations, label-based summaries, and self-revision, their comparative effectiveness remains unclear, especially for low-resource languages. In this paper, we systematically evaluate the performance of …

A valuative approach to the anticanonical minimal model program
Sung Rak Choi, Sungwook Jang, Donghyeon Kim, Dae-Won Lee
https://arxiv.org/abs/2506.13637 h…

A valuative approach to the anticanonical minimal model program
In this paper, we show that the log canonical threshold of a potentially klt triple can be computed by a quasi-monomial valuation. The notion of potential triples provides a larger and more flexible framework to work with than that of generalized pairs. Our main result can be considered as an extension to the result of Xu on klt pairs. As an application of the main result, we show that we can run the MMP on any potentially klt triples and $-(K_X+Δ)$-MMP on the potentially klt pairs.

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

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

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Oscar Boullosa Dapena
https://arxiv.org/abs/2506.12111

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Real-time continuous learning over streaming data remains a central challenge in deep learning and AI systems. Traditional gradient-based models such as backpropagation through time (BPTT) face computational and stability limitations when dealing with temporally unbounded data. In this paper, we introduce a novel architecture, Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs), which leverages the Feynman technique of differentiation under the integral sign to formulate neural u…

On the existence of consistent adversarial attacks in high-dimensional linear classification
Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
https://arxiv.org/abs/2506.12454

On the existence of consistent adversarial attacks in high-dimensional linear classification
What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth l…

From Flat to Feeling: A Feasibility and Impact Study on Dynamic Facial Emotions in AI-Generated Avatars
Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita, P{\aa}l Halvorsen
https://arxiv.org/abs/2506.13477

From Flat to Feeling: A Feasibility and Impact Study on Dynamic Facial Emotions in AI-Generated Avatars
Dynamic facial emotion is essential for believable AI-generated avatars; however, most systems remain visually inert, limiting their utility in high-stakes simulations such as virtual training for investigative interviews with abused children. We introduce and evaluate a real-time architecture fusing Unreal Engine 5 MetaHuman rendering with NVIDIA Omniverse Audio2Face to translate vocal prosody into high-fidelity facial expressions on photorealistic child avatars. We implemented a distributed t…

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo, George Konidaris, Doina Precup, Marlos C. Machado
https://arxiv.org/abs/2506.14045

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might …

A note on the second-largest number of dissociation sets in connected graphs
Pingshan Li, Ke Yang, Wei Jin
https://arxiv.org/abs/2506.12740 https://…

A note on the second-largest number of dissociation sets in connected graphs
A subset of vertices is called a dissociation set if it induces a subgraph with vertex degree at most one. Recently, Yuan et al. established the upper bound of the maximum number of dissociation sets among all connected graphs of order n and characterized the corresponding extremal graphs.They also proposed a question regarding the second-largest number of dissociation sets among all connected graphs of order n and the corresponding extremal graphs. In this paper, we give a positive answer to t…

UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
Yuantao Wang, Haowei Yang, Wei Zhang, Shijian Lu
https://arxiv.org/abs/2506.12324 …

UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
Real-world object detection is a challenging task where the captured images/videos often suffer from complex degradations due to various adverse weather conditions such as rain, fog, snow, low-light, etc. Despite extensive prior efforts, most existing methods are designed for one specific type of adverse weather with constraints of poor generalization, under-utilization of visual features while handling various image degradations. Leveraging a theoretical analysis on how critical visual details…

Socially-aware Object Transportation by a Mobile Manipulator in Static Planar Environments with Obstacles
Caio C. G. Ribeiro, Leonardo R. D. Paes, Douglas G. Macharet
https://arxiv.org/abs/2506.13953

Socially-aware Object Transportation by a Mobile Manipulator in Static Planar Environments with Obstacles
Socially-aware robotic navigation is essential in environments where humans and robots coexist, ensuring both safety and comfort. However, most existing approaches have been primarily developed for mobile robots, leaving a significant gap in research that addresses the unique challenges posed by mobile manipulators. In this paper, we tackle the challenge of navigating a robotic mobile manipulator, carrying a non-negligible load, within a static human-populated environment while adhering to soci…

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
Hitomi Yanaka, Xinqi He, Jie Lu, Namgi Han, Sunjin Oh, Ryoma Kumon, Yuma Matsuoka, Katsuhiko Watabe, Yuko Itatsu
https://arxiv.org/abs/2506.12327

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
An growing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias …

Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
Fernando Galaz Prieto, Antti Lassila, Maryam Samavaki, Sampsa Pursiainen
https://arxiv.org/abs/2506.13452

Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
As DBS technology advances toward directional leads and optimization-based current steering, this study aims to improve the selection of electrode contact configurations using the recently developed L1-norm regularized L1-norm fitting (L1L1) method. The focus is in particular on L1L1's capability to incorporate a priori lead field uncertainty, offering a potential advantage over conventional approaches that do not account for such variability. Our optimization framework incorporates uncertainty…

The Rigidity of Constraint: A Spencer-Hodge Theoretic Approach to the Hodge Conjecture
Dongzhe Zheng
https://arxiv.org/abs/2506.12720 https://

The Rigidity of Constraint: A Spencer-Hodge Theoretic Approach to the Hodge Conjecture
This paper proposes a new theoretical perspective for studying the Hodge conjecture through an analytical framework based on constraint geometry. Our theory begins with a key observation: in compatible pair Spencer theory, a "differential degeneration" mechanism simplifies Spencer differential operators to classical exterior differential under specific algebraic conditions, bridging constraint geometry and de Rham cohomology. This bridge alone is insufficient to filter rare Hodge classes with a…

Experimental Design for Semiparametric Bandits
Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh
https://arxiv.org/abs/2506.13390 https://arxiv.o…

Experimental Design for Semiparametric Bandits
We study finite-armed semiparametric bandits, where each arm's reward combines a linear component with an unknown, potentially adversarial shift. This model strictly generalizes classical linear bandits and reflects complexities common in practice. We propose the first experimental-design approach that simultaneously offers a sharp regret bound, a PAC bound, and a best-arm identification guarantee. Our method attains the minimax regret $\tilde{O}(\sqrt{dT})$, matching the known lower bound for …

The Journey of CodeLab: How University Hackathons Built a Community of Engaged Students
Renato Cordeiro Ferreira (University of S\~ao Paulo), Renata Santos Miranda (University of S\~ao Paulo), Alfredo Goldman (University of S\~ao Paulo)
https://arxiv.org/abs/2506.12840

The Journey of CodeLab: How University Hackathons Built a Community of Engaged Students
This paper presents the journey of CodeLab: a student-organized initiative from the University of São Paulo that has grown thanks to university hackathons. It summarizes patterns, challenges, and lessons learned over 15 competitions organized by the group from 2015 to 2020. By describing these experiences, this report aims to help CodeLab to resume its events after the COVID-19 pandemic, and foster similar initiatives around the world.

LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?
Muhammad Atta Ur Rahman, Melanie Schranz
https://arxiv.org/abs/2506.14496 https://

LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?
Swarm intelligence traditionally refers to systems of simple, decentralized agents whose local interactions lead to emergent, collective behavior. Recently, the term 'swarm' has been extended to describe AI systems like OpenAI's Swarm, where large language models (LLMs) act as collaborative agents. This paper contrasts traditional swarm algorithms with LLM-driven swarms exploring how decentralization, scalability, and emergence are redefined in modern artificial intelligence (AI). We implement …

The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
Michele Alberti (LSL), Fran\c{c}ois Bobot (LSL), Julien Girard-Satabin (LSL), Alban Grastien (LSL), Aymeric Varasse (LSL), Zakaria Chihani (LSL)
https://arxiv.org/abs/2506.12084

The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
The formal specification and verification of machine learning programs saw remarkable progress in less than a decade, leading to a profusion of tools. However, diversity may lead to fragmentation, resulting in tools that are difficult to compare, except for very specific benchmarks. Furthermore, this progress is heavily geared towards the specification and verification of a certain class of property, that is, local robustness properties. But while provers are becoming more and more efficient at…

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho Eom
https://arxiv.org/abs/2506.12413

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and l…

Lasso Gripper: A String Shooting-Retracting Mechanism for Shape-Adaptive Grasping
Qiyuan Qiao, Yu Wang, Xiyu Fan, Peng Lu
https://arxiv.org/abs/2506.14163 …

Lasso Gripper: A String Shooting-Retracting Mechanism for Shape-Adaptive Grasping
Handling oversized, variable-shaped, or delicate objects in transportation, grasping tasks is extremely challenging, mainly due to the limitations of the gripper's shape and size. This paper proposes a novel gripper, Lasso Gripper. Inspired by traditional tools like the lasso and the uurga, Lasso Gripper captures objects by launching and retracting a string. Contrary to antipodal grippers, which concentrate force on a limited area, Lasso Gripper applies uniform pressure along the length of the …

On the Vertices of Delta-modular Polyhedra
Bludov Mikhail, Gribanov Dmitry, Klimenko Maxim, Kupavskii Andrey, L\'angi Zsolt, Rogozin Alexander, Voronov Vsevolod
https://arxiv.org/abs/2506.12774

On the Vertices of Delta-modular Polyhedra
Let $P$ be a polytope defined by the system $A x \leq b$, where $A \in R^{m \times n}$, $b \in R^m$, and $\text{rank}(A) = n$. We give a short geometric proof of the following tight upper bound on the number of vertices of $P$: $$ n! \cdot \fracΔ{Δ_{\text{average}}} \cdot \text{vol}(B_2) \sim \frac{1}{\sqrt{πn}} \cdot \left(\frac{2 π}{e}\right)^{n/2} \cdot n^{n/2} \cdot \fracΔ{Δ_{\text{average}}}, $$ where $Δ$ is the maximum absolute value of $n \times n$ subdeterminants of $A$, and $�…

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…

ICE-ID: A Novel Historical Census Data Benchmark Comparing NARS against LLMs, \& a ML Ensemble on Longitudinal Identity Resolution
Gon\c{c}alo Hora de Carvalho, Lazar S. Popov, Sander Kaatee, Kristinn R. Th\'orisson, Tangrui Li, P\'etur H\'uni Bj\"ornsson, Jilles S. Dibangoye
https://arxiv.org/abs/2506.13792…

ICE-ID: A Novel Historical Census Data Benchmark Comparing NARS against LLMs, \& a ML Ensemble on Longitudinal Identity Resolution
We introduce ICE-ID, a novel benchmark dataset for historical identity resolution, comprising 220 years (1703-1920) of Icelandic census records. ICE-ID spans multiple generations of longitudinal data, capturing name variations, demographic changes, and rich genealogical links. To the best of our knowledge, this is the first large-scale, open tabular dataset specifically designed to study long-term person-entity matching in a real-world population. We define identity resolution tasks (within and…

CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images
Ilya Ilyankou, Natchapon Jongwiriyanurak, Tao Cheng, James Haworth
https://arxiv.org/abs/2506.12214

CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images
We present a CLIP-based, multi-modal, multi-label classifier for predicting geographical context tags from landscape photos in the Geograph dataset--a crowdsourced image archive spanning the British Isles, including remote regions lacking POIs and street-level imagery. Our approach addresses a Kaggle competition\footnote{https://www.kaggle.com/competitions/predict-geographic-context-from-landscape-photos} task based on a subset of Geograph's 8M images, with strict evaluation: exact match accura…

The User Perspective on Island-Ready 6G Communication: A Survey of Future Smartphone Usage in Crisis-Struck Areas with Local Cellular Connectivity
Leon Janzen, Florentin Putz, Marc-Andr\'e Kaufhold, Kolja Straub, Matthias Hollick
https://arxiv.org/abs/2506.13466

The User Perspective on Island-Ready 6G Communication: A Survey of Future Smartphone Usage in Crisis-Struck Areas with Local Cellular Connectivity
Using smartphone apps during crises is well-established, proving critical for efficient crisis response. However, such apps become futile without an Internet connection, which is a common issue during crises. The ongoing 6G standardization explores the capability to provide local cellular connectivity for areas cut off from the Internet in crises. This paper introduces to the HCI community the concept of cellular island connectivity in isolated areas, promising a seamless transition from normal…

Beyond the Plane: A 3D Representation of Human Personal Space for Socially-Aware Robotics
Caio C. G. Ribeiro, Douglas G. Macharet
https://arxiv.org/abs/2506.13937

Beyond the Plane: A 3D Representation of Human Personal Space for Socially-Aware Robotics
The increasing presence of robots in human environments requires them to exhibit socially appropriate behavior, adhering to social norms. A critical aspect in this context is the concept of personal space, a psychological boundary around an individual that influences their comfort based on proximity. This concept extends to human-robot interaction, where robots must respect personal space to avoid causing discomfort. While much research has focused on modeling personal space in two dimensions, …

Lexicographic products and lexicographic powers of graphs -- a walk matrix approach
Domingos M. Cardoso, Paula Carvalho, Helena Gomes, Sofia J. Pinheiro, Paula Rama
https://arxiv.org/abs/2506.12168

Lexicographic products and lexicographic powers of graphs -- a walk matrix approach
The characteristic polynomial and the spectrum of the lexicographic product of graphs $H[G]$, a specific instance of the generalized composition (also called $H$-join), are explicitly determined for arbitrary graphs $H$ and $G$, in terms of the eigenvalues of $G$ and an $H[G]$ associated matrix $\widetilde{\bf W}$, which relates $H$ with $G$. This study also establishes conditions under which a main eigenvalue of $G$ is a main or non-main eigenvalue of the matrix $\widetilde{\bf W}$, when the n…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
Olivier Saidi
https://arxiv.org/abs/2506.13810 https:…

Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study
NP hard optimization problems like the Traveling Salesman Problem (TSP) defy efficient solutions in the worst case, yet real-world instances often exhibit exploitable patterns. We propose a novel patternaware complexity framework that quantifies and leverages structural regularities e.g., clustering, symmetry to reduce effective computational complexity across domains, including financial forecasting and LLM optimization. With rigorous definitions, theorems, and a meta learning driven solver pi…

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
Arjun R. Akula, Kazuma Hashimoto, Krishna Srinivasan, Aditi Chaudhary, Karthik Raman, Michael Bendersky
https://arxiv.org/abs/2506.12346

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously impractical regime. This paper investigates whether traditional ICL selection strategies, which balance the similarity of ICL examples to the test input (using a text retriever) with diversity within the ICL set, remain effective when utilizing a large number of demonstrations. Our experiments demonstrate that, while longer…

Multiple Object Tracking in Video SAR: A Benchmark and Tracking Baseline
Haoxiang Chen, Wei Zhao, Rufei Zhang, Nannan Li, Dongjin Li
https://arxiv.org/abs/2506.12105

Multiple Object Tracking in Video SAR: A Benchmark and Tracking Baseline
In the context of multi-object tracking using video synthetic aperture radar (Video SAR), Doppler shifts induced by target motion result in artifacts that are easily mistaken for shadows caused by static occlusions. Moreover, appearance changes of the target caused by Doppler mismatch may lead to association failures and disrupt trajectory continuity. A major limitation in this field is the lack of public benchmark datasets for standardized algorithm evaluation. To address the above challenges,…

A Point Cloud Completion Approach for the Grasping of Partially Occluded Objects and Its Applications in Robotic Strawberry Harvesting
Ali Abouzeid, Malak Mansour, Chengsong Hu, Dezhen Song
https://arxiv.org/abs/2506.14066

A Point Cloud Completion Approach for the Grasping of Partially Occluded Objects and Its Applications in Robotic Strawberry Harvesting
In robotic fruit picking applications, managing object occlusion in unstructured settings poses a substantial challenge for designing grasping algorithms. Using strawberry harvesting as a case study, we present an end-to-end framework for effective object detection, segmentation, and grasp planning to tackle this issue caused by partially occluded objects. Our strategy begins with point cloud denoising and segmentation to accurately locate fruits. To compensate for incomplete scans due to occlu…

Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
Louis Vervoort, Vitaly Nikolaev
https://arxiv.org/abs/2506.14239

Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustrate the test on advanced Large Language Models (ChatGPT, DeepSeek and Gemini). Remarkably, these chatbots are already capable of correctly identifying causes in cases that are hotly debated in the literature. In order to assess the results of these LLMs and future dedicated AI, we propose a definition of cause in neur…

On kernel isomorphisms of $m$-Cayley digraphs and finite $2$PCI-groups
Xing Zhang, Yan-Quan Feng, Jin-Xin Zhou, Fu-Gang Yin
https://arxiv.org/abs/2506.12306

On kernel isomorphisms of $m$-Cayley digraphs and finite $2$PCI-groups
The isomorphism problem for digraphs is a fundamental problem in graph theory. In this paper, we consider this problem for $m$-Cayley digraphs which are generalization of Cayley digraphs. Let $m$ be a positive integer. A digraph admitting a group $G$ of automorphisms acting semiregularly on the vertices with exactly $m$ orbits is called an $m$-Cayley digraph of $G$. In our previous paper, we developed a theory for $m$-Cayley isomorphisms of $m$-Cayley digraphs, and classified finite $m$CI-group…

Binarization-Aware Adjuster: Bridging Continuous Optimization and Binary Inference in Edge Detection
Hao Shu
https://arxiv.org/abs/2506.12460 https://

Binarization-Aware Adjuster: Bridging Continuous Optimization and Binary Inference in Edge Detection
Image edge detection (ED) faces a fundamental mismatch between training and inference: models are trained using continuous-valued outputs but evaluated using binary predictions. This misalignment, caused by the non-differentiability of binarization, weakens the link between learning objectives and actual task performance. In this paper, we propose a theoretical method to design a Binarization-Aware Adjuster (BAA), which explicitly incorporates binarization behavior into gradient-based optimizat…

A Cooperative Contactless Object Transport with Acoustic Robots
Narsimlu Kemsaram, Akin Delibasi, James Hardwick, Bonot Gautam, Diego Martinez Plasencia, Sriram Subramanian
https://arxiv.org/abs/2506.13957

A Cooperative Contactless Object Transport with Acoustic Robots
Cooperative transport, the simultaneous movement of an object by multiple agents, has been widely observed in biological systems such as ant colonies, which improve efficiency and adaptability in dynamic environments. Inspired by these natural phenomena, we present a novel acoustic robotic system for the transport of contactless objects in mid-air. Our system leverages phased ultrasonic transducers and a robotic control system onboard to generate localized acoustic pressure fields, enabling pre…

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu
https://arxiv.org/abs/2506.14125

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome li…

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu, Jack Pittenger, Mingyo Seo, Yuchen Cui, Yonatan Bisk, Roberto Mart\'in-Mart\'in, Yuke Zhu
https://arxiv.org/abs/2506.14727

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs and to assist users with correct actions. Existing methods are either confined to simple, predefined scenarios or restricted to task-specific data distributions at training, limit…

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng
https://arxiv.org/abs/2506.13795

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models. However, effective transfer faces two critical challenges. Firstly, the network heterophily problem, which is caused by bots hiding malicious behaviors via indiscriminately interacting with human users, hinders the model's ability to learn sufficient and accurate bot-related knowledge from source domains…

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Hongbi Zhou, Zhangkai Ni
https://arxiv.org/abs/2506.12400 https://

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training proce…

TACS-Graphs: Traversability-Aware Consistent Scene Graphs for Ground Robot Indoor Localization and Mapping
Jeewon Kim, Minho Oh, Hyun Myung
https://arxiv.org/abs/2506.14178

TACS-Graphs: Traversability-Aware Consistent Scene Graphs for Ground Robot Indoor Localization and Mapping
Scene graphs have emerged as a powerful tool for robots, providing a structured representation of spatial and semantic relationships for advanced task planning. Despite their potential, conventional 3D indoor scene graphs face critical limitations, particularly under- and over-segmentation of room layers in structurally complex environments. Under-segmentation misclassifies non-traversable areas as part of a room, often in open spaces, while over-segmentation fragments a single room into overla…

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
Jingqi Yang, Zhilong Song, Jiawei Chen, Mingli Song, Sheng Zhou, linjun sun, Xiaogang Ouyang, Chun Chen, Can Wang
https://arxiv.org/abs/2506.14477

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
The development of high-quality datasets is crucial for benchmarking and advancing research in Graphical User Interface (GUI) agents. Despite their importance, existing datasets are often constructed under idealized conditions, overlooking the diverse anomalies frequently encountered in real-world deployments. To address this limitation, we introduce GUI-Robust, a novel dataset designed for comprehensive GUI agent evaluation, explicitly incorporating seven common types of anomalies observed in …

Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Runhao Zeng, Qi Deng, Ronghao Zhang, Shuaicheng Niu, Jian Chen, Xiping Hu, Victor C. M. Leung
https://arxiv.org/abs/2506.12481

Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Test-time adaptation (TTA) aims to boost the generalization capability of a trained model by conducting self-/unsupervised learning during the testing phase. While most existing TTA methods for video primarily utilize visual supervisory signals, they often overlook the potential contribution of inherent audio data. To address this gap, we propose a novel approach that incorporates audio information into video TTA. Our method capitalizes on the rich semantic content of audio to generate audio-as…

FormGym: Doing Paperwork with Agents
Matthew Toles, Rattandeep Singh, Isaac Song Zhou Yu
https://arxiv.org/abs/2506.14079 https://arx…

FormGym: Doing Paperwork with Agents
Completing paperwork is a challenging and time-consuming problem. Form filling is especially challenging in the pure-image domain without access to OCR, typeset PDF text, or a DOM. For computer agents, it requires multiple abilities, including multi-modal understanding, information retrieval, and tool-use. We present a novel form-filling benchmark consisting of 432 fields spread across 55 documents and 3 tasks, requiring knowledge of 236 features per user. We find that baseline VLAs achieve les…

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…

Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models
Haonan Yin, Shai Vardi, Vidyanand Choudhary
https://arxiv.org/abs/2506.14092 h…

Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models
Large language models (LLMs) are increasingly used in decision-support systems across high-stakes domains such as hiring and university admissions, where decisions often involve selecting among competing alternatives. While prior work has noted positional order biases in LLM-driven comparisons, these biases have not been systematically dissected or linked to underlying preference structures. We provide the first comprehensive investigation of positional biases across multiple LLM architectures …

Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
Mbithe Nzomo, Deshendran Moodley
https://arxiv.org/abs/2506.13920

Integrating Knowledge Graphs and Bayesian Networks: A Hybrid Approach for Explainable Disease Risk Prediction
Multimodal electronic health record (EHR) data is useful for disease risk prediction based on medical domain knowledge. However, general medical knowledge must be adapted to specific healthcare settings and patient populations to achieve practical clinical use. Additionally, risk prediction systems must handle uncertainty from incomplete data and non-deterministic health outcomes while remaining explainable. These challenges can be alleviated by the integration of knowledge graphs (KGs) and Bay…

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

AviationLLM: An LLM-based Knowledge System for Aviation Training
Jia'ang Wan, Feng Shen, Fujuan Li, Yanjin Sun, Yan Li, Shiwen Zhang
https://arxiv.org/abs/2506.14336

AviationLLM: An LLM-based Knowledge System for Aviation Training
Aviation training is a core link in ensuring flight safety, improving industry efficiency and promoting sustainable development. It not only involves flight simulation but also requires the learning of a great deal of professional aviation theory knowledge. In the existing training system, the knowledge is mainly imparted by the the instructors. However, the number of instructors is limited and the professional answers obtained from the Internet are not accurate enough, resulting in low trainin…

ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems
Fanzhi Zeng, Siqi Wang, Chuzhao Zhu, Li Li
https://arxiv.org/abs/2506.14299 https:…

ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems
How to construct an interpretable autonomous driving decision-making system has become a focal point in academic research. In this study, we propose a novel approach that leverages large language models (LLMs) to generate executable, rule-based decision systems to address this challenge. Specifically, harnessing the strong reasoning and programming capabilities of LLMs, we introduce the ADRD(LLM-Driven Autonomous Driving Based on Rule-based Decision Systems) framework, which integrates three co…