arXiv Papers

Published: 2026-03-30 16:56:54

Authors: Khalid Adnan Alsayed

Categories: cs.CV, cs.AI, cs.LG

Abstract:
Facial recognition systems are increasingly deployed in law enforcement and security contexts, where algorithmic decisions can carry significant societal consequences. Despite high reported accuracy, growing evidence demonstrates that such systems often exhibit uneven performance across demographic groups, leading to disproportionate error rates and potential harm. This paper argues that aggregate accuracy is an insufficient metric for evaluating the fairness and reliability of facial recognition systems in high-stakes environments. Through analysis of subgroup-level error distribution, including false positive rate (FPR) and false negative rate (FNR), the paper demonstrates how aggregate performance metrics can obscure critical disparities across demographic groups. Empirical observations show that systems with similar overall accuracy can exhibit substantially different fairness profiles, with subgroup error rates varying significantly despite a single aggregate metric. The paper further examines the operational risks associated with accuracy-centric evaluation practices in law enforcement applications, where misclassification may result in wrongful suspicion or missed identification. It highlights the importance of fairness-aware evaluation approaches and model-agnostic auditing strategies that enable post-deployment assessment of real-world systems. The findings emphasise the need to move beyond accuracy as a primary metric and adopt more comprehensive evaluation frameworks for responsible AI deployment.

            {
  "tldr": "This paper critiques the use of aggregate accuracy for evaluating fairness in facial recognition systems used in law enforcement, highlighting significant demographic disparities in error rates. It advocates for fairness-aware evaluation methods that consider subgroup performance metrics.",
  "whats_new": [
    "• Argues that aggregate accuracy obscures critical disparities in error rates across demographic groups.",
    "• Highlights operational risks of misclassification in law enforcement contexts.",
    "• Proposes model-agnostic fairness auditing approaches for post-deployment evaluation.",
    "• Discusses the trade-offs between fairness and accuracy in high-stakes applications."
  ],
  "method": "The paper analyzes subgroup-level error distributions, including false positive and false negative rates, to demonstrate the inadequacy of aggregate accuracy. It draws on existing literature and empirical observations from classification-based systems to highlight operational risks and the need for fairness-aware evaluation.",
  "results": [
    "• Aggregate accuracy can mask significant disparities in error rates across demographic groups.",
    "• False positive rates for certain demographics can lead to wrongful suspicion or investigation.",
    "• False negative rates may reduce the effectiveness of identification processes.",
    "• Empirical studies show higher error rates for underrepresented demographic groups."
  ],
  "limitations": [
    "• The paper does not provide specific numerical data on the extent of disparities.",
    "• Limited discussion on the practical implementation of proposed fairness-aware evaluation methods.",
    "• The trade-offs between fairness and accuracy are acknowledged but not quantitatively analyzed."
  ],
  "why_useful": "This paper is crucial for understanding the limitations of current evaluation practices in facial recognition systems, particularly in law enforcement. It emphasizes the need for more equitable evaluation frameworks that can prevent systemic bias and ensure accountability in AI applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:56:54

Authors: Yulie Arad, Stav Ashur, Marta Markowicz, James D. Motes, Marco Morales, Nancy M. Amato

Categories: cs.RO

Abstract:
Motion planning in dynamic environments, such as robotic warehouses, requires fast adaptation to frequent changes in obstacle poses. Traditional roadmap-based methods struggle in such settings, relying on inefficient reconstruction of a roadmap or expensive collision detection to update the existing roadmap. To address these challenges we introduce the Red-Green-Gray (RGG) framework, a method that builds on SPITE to quickly classify roadmap edges as invalid (red), valid (green), or uncertain (gray) using conservative geometric approximations. Serial RGG provides a high-performance variant leveraging batch serialization and vectorization to enable efficient GPU acceleration. Empirical results demonstrate that while RGG effectively reduces the number of unknown edges requiring full validation, SerRGG achieves a 2-9x speedup compared to the sequential implementation. This combination of geometric precision and computational speed makes SerRGG highly effective for time-critical robotic applications.

            {
  "tldr": "This paper presents the Serialized Red-Green-Gray (SerRGG) framework, which enhances edge validation in dynamic roadmap graphs for motion planning, achieving a 2-9x speedup over traditional methods. It combines geometric approximations with GPU acceleration for efficient real-time updates.",
  "whats_new": [
    "• Introduces the Red-Green-Gray (RGG) framework for edge classification in dynamic environments.",
    "• Develops SerRGG, a serialized and vectorized implementation that significantly speeds up edge validation.",
    "• Implements a spatially filtered GPU validation strategy to optimize computational resources.",
    "• Provides empirical results demonstrating substantial performance improvements over existing methods."
  ],
  "method": "The RGG framework classifies edges as valid, invalid, or uncertain using geometric approximations. SerRGG enhances this by employing serialization and vectorization for GPU acceleration, allowing for efficient batch processing of edge validations in dynamic environments.",
  "results": [
    "• SerRGG achieves a 2-9x speedup compared to the sequential implementation of SPITE.",
    "• RGG reduces the number of unknown edges needing full validation by 12-30% compared to SPITE.",
    "• SerRGG maintains millisecond-scale updates even with increasing roadmap sizes.",
    "• Performance improvements are consistent across various dynamic environments and robot types."
  ],
  "limitations": [
    "• The effectiveness of SerRGG may be limited in very small graphs due to GPU transfer overhead.",
    "• The approach relies on geometric approximations, which may introduce some inaccuracies.",
    "• Further integration into full motion planning pipelines is needed for comprehensive evaluation."
  ],
  "why_useful": "This paper is significant for real-time robotic applications where environments change frequently. The SerRGG framework allows for efficient updates to motion planning roadmaps, making it suitable for dynamic settings like robotic warehouses.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:56:38

Authors: Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, Jamal Bentahar

Categories: cs.LG, cs.CR, cs.DC

Abstract:
Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to manipulate model behavior by injecting poisoned data with hidden triggers. These attacks can lead to severe consequences, especially in critical applications such as autonomous driving, healthcare, and finance. Detecting and mitigating backdoor attacks is crucial across the lifespan of model's phases, including pre-training, in-training, and post-training. In this paper, we propose Pre-Training Backdoor Mitigation for Federated Learning (FL-PBM), a novel defense mechanism that proactively filters poisoned data on the client side before model training in a federated learning (FL) environment. The approach consists of three stages: (1) inserting a benign trigger into the data to establish a controlled baseline, (2) applying Principal Component Analysis (PCA) to extract discriminative features and assess the separability of the data, (3) performing Gaussian Mixture Model (GMM) clustering to identify potentially malicious data samples based on their distribution in the PCA-transformed space, and (4) applying a targeted blurring technique to disrupt potential backdoor triggers. Together, these steps ensure that suspicious data is detected early and sanitized effectively, thereby minimizing the influence of backdoor triggers on the global model. Experimental evaluations on image-based datasets demonstrate that FL-PBM reduces attack success rates by up to 95% compared to baseline federated learning (FedAvg) and by 30 to 80% relative to state-of-the-art defenses (RDFL and LPSF). At the same time, it maintains over 90% clean model accuracy in most experiments, achieving better mitigation without degrading model performance.

            {
  "tldr": "The paper presents FL-PBM, a proactive defense mechanism for federated learning that mitigates backdoor attacks by filtering poisoned data before training. It combines PCA, GMM clustering, and targeted blurring to effectively reduce attack success rates while maintaining high model accuracy.",
  "whats_new": [
    "• Introduces a client-side pre-training backdoor mitigation technique in federated learning.",
    "• Combines PCA for feature extraction with GMM clustering and benign trigger insertion.",
    "• Applies selective data-driven blurring to weaken potential backdoor triggers.",
    "• Demonstrates effectiveness against diverse backdoor attack strategies."
  ],
  "method": "FL-PBM operates in three stages: it first inserts a benign trigger into data, then uses PCA to extract features and GMM for clustering to identify suspicious samples. Finally, it applies targeted blurring to mitigate the effects of potential backdoor triggers.",
  "results": [
    "• Reduces attack success rates by up to 95% compared to baseline methods.",
    "• Achieves a 30-80% reduction in attack success rates relative to state-of-the-art defenses.",
    "• Maintains over 90% clean model accuracy in most experiments.",
    "• Effective in both IID and non-IID scenarios."
  ],
  "limitations": [
    "• The approach may not generalize to all types of backdoor attacks.",
    "• Performance in highly heterogeneous data distributions is not fully explored.",
    "• Future work needed to integrate advanced feature extraction methods."
  ],
  "why_useful": "This paper addresses a critical vulnerability in federated learning by introducing a proactive defense mechanism that mitigates backdoor attacks at the data level. Its findings are relevant for enhancing the security of AI models in sensitive applications like healthcare and autonomous driving.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:55:29

Authors: Oscar Adamuz-Hinojosa, Jonathan Prados-Garzon, Sara Vaquero-Gil, Juan M. Lopez-Soler

Categories: cs.NI

Abstract:
Quantum networks (QNs) enable the transfer of qubits between distant nodes using quantum teleportation, which reproduces a qubit state at a remote location by consuming a shared Bell pair. After teleportation, qubits are stored in quantum memories, where decoherence progressively degrades their quantum states. This degradation is quantified by the fidelity, defined as the overlap between the stored quantum state and the ideal target state. Some quantum applications (QApps) require the teleportation of multiple qubits and can only operate if all teleported qubits simultaneously maintain a fidelity above a given threshold. In this paper, we study how many qubits can be teleported under such fidelity-constrained operation in a two-node QN. To that end, we define a QApp-level reliability metric as the probability that all end-to-end Bell pairs satisfy the target fidelity upon completion of the multi-qubit teleportation stage. We design a Monte Carlo-based simulator that captures stochastic Bell-pair generation, Quantum Repeater (QR)-assisted entanglement distribution, and fidelity degradation. Fiber-based and terrestrial free-space optical (FSO) quantum links and representative NV-center- and trapped-ion-based quantum memories are considered. Results show that memory coherence is the main scalability bottleneck under stringent fidelity targets, while parallel entanglement generation is essential for multi-qubit teleportation.

            {
  "tldr": "This paper explores the scalability of multi-qubit teleportation in quantum networks under fidelity constraints, focusing on the reliability of teleportation processes. It introduces a new reliability metric and a Monte Carlo simulation framework to assess how many qubits can be teleported while maintaining required fidelity levels.",
  "whats_new": [
    "• Introduces a QApp-level reliability metric for multi-qubit teleportation.",
    "• Develops a Monte Carlo simulator capturing stochastic Bell-pair generation and fidelity degradation.",
    "• Analyzes the impact of memory coherence time and parallel entanglement generation on teleportation reliability.",
    "• Characterizes feasible regions for teleporting qubits based on technology and transmission medium."
  ],
  "method": "The authors define a reliability metric that quantifies the probability of successful multi-qubit teleportation under fidelity constraints. They use a Monte Carlo simulation to model stochastic Bell-pair generation, entanglement distribution, and fidelity degradation in quantum memories, assessing the reliability of teleportation processes across different technologies.",
  "results": [
    "• Memory coherence time is identified as the main bottleneck for scalability under stringent fidelity targets.",
    "• Parallel entanglement generation significantly improves reliability for multi-qubit teleportation.",
    "• Feasibility regions for teleporting qubits vary based on quantum memory technology and transmission medium.",
    "• Trapped-ion memories enable reliable teleportation over hundreds of kilometers, while NV-center memories are limited to shorter distances."
  ],
  "limitations": [
    "• The analysis is limited to two-node quantum networks and may not generalize to larger networks.",
    "• The Monte Carlo simulation approach may be computationally intensive for large-scale applications.",
    "• Future work is needed to develop analytical models for faster feasibility evaluations."
  ],
  "why_useful": "This paper provides insights into the challenges of multi-qubit teleportation in quantum networks, which is crucial for the development of scalable quantum applications. Understanding the reliability and constraints of teleportation processes can inform the design of future quantum communication systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at URL"
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:51:37

Authors: Michał Marczenko, Larry McLerran, Krzysztof Redlich

Categories: hep-ph, nucl-th

Abstract:
We show that hadrons in QCD follow a spectrum determined by string dynamics characterized by a universal Hagedorn temperature linked to the string tension. While this behavior was recently established for light hadrons and glueballs, we demonstrate that the same dynamics describes the heavy-flavor sector. After separating the current quark masses, the resulting spectrum reproduces lattice QCD thermodynamics of charmed hadrons and the observed spectra of hadrons across quark flavors without additional parameters. These results reflect the universal confining dynamics of QCD through the string tension.

            {
  "tldr": "This paper demonstrates that hadronic spectra in QCD can be described by a universal Hagedorn temperature across all quark flavors, linking thermodynamics and string dynamics without additional parameters.",
  "whats_new": [
    "• Establishes a universal Hagedorn temperature for all quark flavors.",
    "• Shows that heavy-flavor hadrons follow the same spectral behavior as light hadrons.",
    "• Provides a parameter-free description of thermodynamic observables in the charm and bottom sectors.",
    "• Confirms the exponential growth of hadronic states governed by string dynamics."
  ],
  "method": "The authors use a string-based density of states to analyze hadronic spectra, separating current quark masses to derive a universal description. They confront thermodynamic observables and spectral abundances in charm and bottom sectors using this framework.",
  "results": [
    "• The Hagedorn temperature is found to be T_H = 323(3) MeV, consistent across quark flavors.",
    "• The model reproduces lattice QCD thermodynamics for charmed hadrons without additional parameters.",
    "• Cumulative spectra for open-flavor mesons and baryons align with experimental data from the PDG.",
    "• The pressure of charmed baryons matches lattice QCD results, indicating missing states in the spectrum."
  ],
  "limitations": [
    "• The study does not account for short-distance interactions affecting heavy quark states.",
    "• Experimental incompleteness in high-mass spectra may affect results.",
    "• The model's predictions for hidden-flavor mesons slightly underestimate observed states."
  ],
  "why_useful": "This research provides a unified framework for understanding hadronic spectra and thermodynamics in QCD, which is crucial for interpreting heavy-ion collision data. It may lead to improved predictions for heavy-flavor hadron production in experiments.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:41:47

Authors: Satoshi Usami

Categories: stat.ME, stat.AP

Abstract:
The cross-lagged panel model (CLPM) has been widely used, particularly in psychology, to infer longitudinal relations among variables. At the same time, controlling for between-person heterogeneity and capturing within-person relations as processes of within-person change are regarded as key components to causal inference based on longitudinal data. Since Hamaker, Kuiper, and Grasman (2015) criticized the CLPM for its limitations in inferring within-person relations, the random intercept cross-lagged panel model (RI-CLPM), which incorporates stable trait factors representing stable individual differences, has rapidly spread, especially in psychology. At the same time, although many statistical models are available for inferring within-person relations, the distinctions among them have not been clearly delineated, and discussions over the interpretation and selection of statistical models remain active. In this paper, I position the RI-CLPM as one useful method for inferring within-person relations, explain its practical issues, and organize its mathematical and conceptual relationships with other statistical models, as well as potential problems that may arise in their application. In particular, I point out that a distinctive feature of the stable trait factors in the RI-CLPM, in representing between-person heterogeneity, is the assumption that they are uncorrelated with within-person variability, and that this point serves as an important link to the mathematical relationship with the dynamic panel model, another promising alternative.

            {
  "tldr": "This paper discusses the Random Intercept Cross-Lagged Panel Model (RI-CLPM) as a method for inferring within-person relations in longitudinal data, highlighting its advantages over traditional models like the Cross-Lagged Panel Model (CLPM). It also addresses practical issues and comparisons with other statistical models.",
  "whats_new": [
    "• Introduces the RI-CLPM as a superior method for inferring within-person relations.",
    "• Discusses the mathematical and conceptual relationships between RI-CLPM and other models.",
    "• Highlights practical issues in applying the RI-CLPM and its implications for causal inference.",
    "• Provides a unified framework for comparing various statistical models."
  ],
  "method": "The paper positions the RI-CLPM as a method that incorporates stable trait factors to control for between-person heterogeneity while inferring within-person relations. It organizes mathematical relationships with other models and discusses practical considerations for its application.",
  "results": [
    "• The RI-CLPM often produces significantly different results from the CLPM in terms of direction and effect size.",
    "• Empirical studies show that the RI-CLPM provides better fit and more stable estimates than the CLPM.",
    "• The RI-CLPM requires at least three time points for identification, unlike the CLPM which requires only two.",
    "• Variance estimates of stable trait factors in the RI-CLPM are often non-negligible."
  ],
  "limitations": [
    "• The RI-CLPM may not adequately control for unobserved time-invariant confounders.",
    "• The assumptions regarding the independence of stable trait factors and within-person variability may not hold in all cases.",
    "• The paper does not provide a definitive conclusion on the best model for inferring within-person relations.",
    "• The complexity of model selection and interpretation remains a challenge."
  ],
  "why_useful": "This paper is significant for researchers in psychology and related fields as it clarifies the use of the RI-CLPM for causal inference in longitudinal studies. It provides insights into model selection and the implications of different statistical approaches for understanding within-person dynamics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:40:55

Authors: Md Raz, Venkata Sai Charan Putrevu, Meet Udeshi, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri

Categories: cs.CR

Abstract:
AI-powered malware increasingly exploits cloud-hosted generative-AI services and large language models (LLMs) as analysis engines for reconnaissance and code generation. Simultaneously, enterprise uploads expose sensitive documents to third-party AI vendors. Both threats converge at the AI service ingestion boundary, yet existing defenses focus on endpoints and network perimeters, leaving organizations with limited visibility once plaintext reaches an LLM service. To address this, we present a framework based on steganographic canary files: realistic documents carrying cryptographically derived identifiers embedded via complementary encoding channels. A pre-ingestion filter extracts and verifies these identifiers before LLM processing, enabling passive, format-agnostic detection without semantic classification. We support two modes of operation where Mode A marks existing sensitive documents with layered symbolic encodings (whitespace substitution, zero-width character insertion, homoglyph substitution), while Mode B generates synthetic canary documents using linguistic steganography (arithmetic coding over GPT-2), augmented with compatible symbolic layers. We model increasing document pre-processing and adversarial capability for both modes via a four-tier transport-transform taxonomy: All methods achieve 100% identifier recovery under benign and sanitization workflows (Tiers 1-2). The hybrid Mode B maintains 97% through targeted adversarial transforms (Tier 3). An end-to-end case study against an LLM-orchestrated ransomware pipeline confirms that both modes detect and block canary-bearing uploads before file encryption begins. To our knowledge, this is the first framework to systematically combine symbolic and linguistic text steganography into layered canary documents for detecting unauthorized LLM processing, evaluated against a transport-threat taxonomy tailored to AI malware.

            {
  "tldr": "This paper introduces a framework using steganographic canary files to detect unauthorized submissions of sensitive documents to AI services, addressing vulnerabilities in existing defenses against AI-driven malware. It combines symbolic and linguistic steganography for robust detection before LLM processing.",
  "whats_new": [
    "• First framework to combine symbolic and linguistic text steganography into layered canary documents.",
    "• Achieves 100% identifier recovery under benign conditions and 97% under targeted adversarial transforms.",
    "• Provides a defense-in-depth approach that survives common document handling and sanitization.",
    "• Validated through an end-to-end case study against an LLM-orchestrated ransomware attack."
  ],
  "method": "The framework embeds cryptographically verifiable identifiers into canary files using two modes: Mode A marks existing documents with symbolic encodings, while Mode B generates synthetic documents with linguistic steganography. A pre-ingestion filter at the AI service extracts and verifies these identifiers.",
  "results": [
    "• Achieves 100% identifier recovery under benign conditions and 97% under targeted adversarial transforms.",
    "• Zero false positives in verification using HMAC and EdDSA schemes.",
    "• Successfully detects and blocks canary-bearing uploads during the reconnaissance phase of a ransomware attack.",
    "• Demonstrates a layered approach that maintains detection through various transport conditions."
  ],
  "limitations": [
    "• Semantic rewriting defeats all embedding methods, posing a significant challenge.",
    "• The linguistic method's performance is affected by GPT-2's vocabulary artifacts, leading to lower recovery rates under certain conditions.",
    "• Requires a cooperating AI vendor for effective detection, limiting generalizability."
  ],
  "why_useful": "This paper addresses a critical gap in AI security by providing a method to detect unauthorized document submissions to AI services, which is increasingly relevant as organizations adopt generative AI. The framework can help prevent data leaks and enhance organizational security against AI-driven malware.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:25:37

Authors: Lara Russell-Lasalandra, Hudson Golino, Luis Eduardo Garrido, Alexander P. Christensen

Categories: cs.AI, cs.CL, cs.HC

Abstract:
Psychological scale development has traditionally required extensive expert involvement, iterative revision, and large-scale pilot testing before psychometric evaluation can begin. The `AIGENIE` R package implements the AI-GENIE framework (Automatic Item Generation with Network-Integrated Evaluation), which integrates large language model (LLM) text generation with network psychometric methods to automate the early stages of this process. The package generates candidate item pools using LLMs, transforms them into high-dimensional embeddings, and applies a multi-step reduction pipeline -- Exploratory Graph Analysis (EGA), Unique Variable Analysis (UVA), and bootstrap EGA -- to produce structurally validated item pools entirely *in silico*. This tutorial introduces the package across six parts: installation and setup, understanding Application Programming Interfaces (APIs), text generation, item generation, the `AIGENIE` function, and the `GENIE` function. Two running examples illustrate the package's use: the Big Five personality model (a well-established construct) and AI Anxiety (an emerging construct). The package supports multiple LLM providers (OpenAI, Anthropic, Groq, HuggingFace, and local models), offers a fully offline mode with no external API calls, and provides the `GENIE()` function for researchers who wish to apply the psychometric reduction pipeline to existing item pools regardless of their origin. The `AIGENIE` package is freely available on R-universe at https://laralee.r-universe.dev/AIGENIE.

            {
  "tldr": "The paper introduces AIGENIE, an R package that automates psychological scale development using AI and network psychometrics, significantly reducing time and costs. It provides a comprehensive tutorial on its functionalities and applications.",
  "whats_new": [
    "• AIGENIE integrates large language model (LLM) text generation with network psychometric methods.",
    "• The package allows for in silico structural validation of item pools without human data collection.",
    "• It supports multiple LLM providers and offers an offline mode.",
    "• The tutorial includes practical examples for established and emerging constructs."
  ],
  "method": "AIGENIE automates scale development by generating candidate item pools using LLMs, embedding them into high-dimensional vectors, and applying a multi-step psychometric pipeline. This includes Exploratory Graph Analysis (EGA) and Unique Variable Analysis (UVA) for structural validation.",
  "results": [
    "• AIGENIE demonstrated consistent improvements in structural validity across various conditions in Monte Carlo simulations.",
    "• The package can generate and validate item pools in minutes, compared to traditional methods that take months.",
    "• Structural validity achieved through AIGENIE matches that obtained from human response data in some cases."
  ],
  "limitations": [
    "• AIGENIE does not replace the need for empirical validation with human participants.",
    "• The quality of generated items is dependent on the LLM used.",
    "• The package is still under active development, and its capabilities may evolve."
  ],
  "why_useful": "AIGENIE streamlines the traditionally labor-intensive process of scale development, making it accessible for researchers. It enables the creation of diverse psychological assessments, particularly for underrepresented constructs, and allows for rigorous structural validation of existing item pools.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://laralee.r-universe.dev/AIGENIE"
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:09:26

Authors: Mohamed Elgouhary, Amr S. El-Wakeel

Categories: cs.RO, cs.AI, eess.SY

Abstract:
Pure Pursuit (PP) is a widely used path-tracking algorithm in autonomous vehicles due to its simplicity and real-time performance. However, its effectiveness is sensitive to the choice of lookahead distance: shorter values improve cornering but can cause instability on straights, while longer values improve smoothness but reduce accuracy in curves. We propose a hybrid control framework that integrates Proximal Policy Optimization (PPO) with the classical Pure Pursuit controller to adjust the lookahead distance dynamically during racing. The PPO agent maps vehicle speed and multi-horizon curvature features to an online lookahead command. It is trained using Stable-Baselines3 in the F1TENTH Gym simulator with a KL penalty and learning-rate decay for stability, then deployed in a ROS2 environment to guide the controller. Experiments in simulation compare the proposed method against both fixed-lookahead Pure Pursuit and an adaptive Pure Pursuit baseline. Additional real-car experiments compare the learned controller against a fixed-lookahead Pure Pursuit controller. Results show that the learned policy improves lap-time performance and repeated lap completion on unseen tracks, while also transferring zero-shot to hardware. The learned controller adapts the lookahead by increasing it on straights and reducing it in curves, demonstrating effectiveness in augmenting a classical controller by online adaptation of a single interpretable parameter. On unseen tracks, the proposed method achieved 33.16 s on Montreal and 46.05 s on Yas Marina, while tolerating more aggressive speed-profile scaling than the baselines and achieving the best lap times among the tested settings. Initial real-car experiments further support sim-to-real transfer on a 1:10-scale autonomous racing platform

            {
  "tldr": "This paper presents a reinforcement learning approach to dynamically adjust the lookahead distance in the Pure Pursuit controller for autonomous racing, improving lap times and adaptability on unseen tracks.",
  "whats_new": [
    "• Introduces a hybrid control framework combining Proximal Policy Optimization (PPO) with Pure Pursuit.",
    "• Demonstrates zero-shot transfer of the learned policy to real-world racing scenarios.",
    "• Achieves improved lap times on unseen tracks compared to fixed and adaptive lookahead baselines.",
    "• Maintains the interpretability and low computational cost of the classical controller."
  ],
  "method": "The approach uses PPO to dynamically adjust the lookahead distance based on vehicle speed and track curvature. The policy is trained in simulation and then validated in real-world scenarios, ensuring adaptability and robustness.",
  "results": [
    "• Achieved a mean lap time of 33.16 s on the Montreal track under a +13% speed profile.",
    "• Recorded a mean lap time of 46.05 s on the Yas Marina track under a +15% speed profile.",
    "• Completed 10 laps on both tracks with improved performance compared to fixed-lookahead and adaptive baselines.",
    "• The learned policy successfully transferred to a real 1:10-scale racing vehicle, completing laps reliably."
  ],
  "limitations": [
    "• The method's performance may vary with different vehicle models and track conditions.",
    "• Further validation on a wider range of tracks and conditions is needed.",
    "• The study primarily focuses on a single parameter adaptation, leaving other potential improvements unexplored."
  ],
  "why_useful": "This research enhances the Pure Pursuit controller's adaptability, making it suitable for high-speed autonomous racing. It offers insights into integrating reinforcement learning with classical control methods, potentially benefiting various robotics applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 16:04:02

Authors: Ari Krishna

Categories: math.AG

Abstract:
We study the natural action of $\mathrm{PGL}(V)$ on the Grassmannian $G=\operatorname{Gr}(2,\operatorname{Sym}^2 V^\vee)$, where $\dim V=4$ and points of $G$ are pencils of quadrics in $\mathbb{P}(V)\cong \mathbb{P}^3$. Here $\dim G=16$ while $\dim \mathrm{PGL}(V)=15$, so the generic orbit has codimension one and one expects a one-parameter family of generic orbits. We construct this family via the $j$-invariant of the discriminant binary quartic of a pencil. We then determine the codimension-one orbit closures and compute their Chow classes. The smooth codimension-one orbit closures are the reduced fibers of the $j$-map on the smooth locus, while the unique boundary divisor is the closure of the orbit of a nodal quartic complete intersection of arithmetic genus $1$ and geometric genus $0$. Every divisorial fiber of the rational $j$-map has class $12σ_1$ in $A^1(G)$. For the reduced codimension-one orbit closures one has $[\overline{O_a}]=12σ_1$ for $a\neq 0,1728,\infty$, $[\overline{O_{1728}}]=6σ_1$, $[\overline{O_0}]=4σ_1$, and $[T]=12σ_1$.

            {
  "tldr": "This paper investigates the codimension-1 orbit closures of the PGL4 action on the Grassmannian Gr(2,10), constructing a family of generic orbits and computing their Chow classes.",
  "whats_new": [
    "• Establishes a rational PGL(V)-invariant map related to the j-invariant of discriminant binary quartics.",
    "• Identifies unique codimension-one orbit closures and their classes in the Chow ring.",
    "• Provides a detailed analysis of the special fiber and its unique divisorial component.",
    "• Compares findings with previous work on PGL3 actions, highlighting geometric differences."
  ],
  "method": "The study constructs a family of codimension-one orbit closures using the j-invariant of discriminant binary quartics. It employs intersection theory and Schubert calculus to compute Chow classes and analyze the structure of special fibers.",
  "results": [
    "• The reduced fiber classes are [Oa] = 12σ1 for a ≠ {0, 1728, ∞}, [O1728] = 6σ1, [O0] = 4σ1, and [T] = 12σ1.",
    "• The special fiber F∞ has a unique divisorial component corresponding to a nodal quartic complete intersection.",
    "• Every divisorial fiber of the rational j-map has class 12σ1 in A1(G).",
    "• The paper confirms that the generic orbit has codimension one in G."
  ],
  "limitations": [
    "• The study does not address higher codimension orbit closures beyond the primary focus.",
    "• The implications of the findings for broader contexts in algebraic geometry remain unexplored."
  ],
  "why_useful": "This paper contributes to the understanding of orbit closures in algebraic geometry, particularly in the context of PGL actions. Its findings have implications for the study of moduli spaces and intersection theory, making it relevant for researchers in these fields.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-30 15:30:38

Authors: Euclid Collaboration, L. R. Ecker, M. Fabricius, S. Seitz, R. Saglia, N. E. P. Lines, P. Holloway, T. Li, A. Verma, F. Balzer, Q. Jin, A. Manjón-García, S. H. Vincken, J. Wilde, J. A. Acevedo Barroso, J. W. Nightingale, K. Rojas, S. Schuldt, M. Walmsley, T. E. Collett, G. Despali, A. Sonnenfeld, C. Tortora, R. B. Metcalf, R. Bender, C. Saulder, E. Baeten, C. Cornen, D. Delley, K. Finner, A. Galan, R. Gavazzi, L. C. Johnson, L. Leuzzi, C. Macmillan, P. J. Marshall, M. Millon, A. More, L. A. Moustakas, J. Pearson, J. -N. Pippert, C. Scarlata, D. Sluse, C. Spiniello, T. T. Thai, L. Ulivi, Han. Wang, X. Xu, F. Courbin, M. Meneghetti, N. Aghanim, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, A. Costille, H. M. Courtois, M. Cropper, A. Da Silva, H. Degaudenzi, G. De Lucia, C. Dolding, H. Dole, F. Dubath, X. Dupac, S. Dusini, A. Ealet, S. Escoffier, M. Farina, R. Farinelli, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, E. Keihänen, S. Kermiche, A. Kiessling, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, D. Le Mignant, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, Y. Mellier, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, R. C. Nichol, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, H. -W. Rix, E. Romelli, M. Roncarelli, E. Rossetti, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, P. Simon, C. Sirignano, G. Sirri, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, F. M. Zerbi, E. Zucca, M. Ballardini, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, T. Castro, B. Clément, J. A. Escartin Vigo, L. Gabarra, J. García-Bellido, V. Gautard, S. Hemmati, M. Huertas-Company, J. Macias-Perez, R. Maoli, J. Martín-Fleitas, M. Maturi, N. Mauri, P. Monaco, A. Pezzotta, M. Pöntinen, C. Porciani, I. Risso, V. Scottez, M. Sereno, M. Tenti, M. Tucci, M. Viel, M. Wiesmann, Y. Akrami, I. T. Andika, G. Angora, S. Anselmi, M. Archidiacono, F. Atrio-Barandela, L. Bazzanini, P. Bergamini, D. Bertacca, M. Bethermin, F. Beutler, A. Blanchard, L. Blot, M. Bonici, S. Borgani, M. L. Brown, S. Bruton, A. Calabro, B. Camacho Quevedo, F. Caro, C. S. Carvalho, Y. Charles, F. Cogato, S. Conseil, A. R. Cooray, O. Cucciati, S. Davini, F. De Paolis, G. Desprez, A. Díaz-Sánchez, S. Di Domizio, J. M. Diego, P. -A. Duc, V. Duret, M. Y. Elkhashab, A. Enia, Y. Fang, A. Finoguenov, A. Fontana, A. Franco, K. Ganga, T. Gasparetto, E. Gaztanaga, F. Giacomini, F. Gianotti, G. Gozaliasl, A. Gruppuso, M. Guidi, C. M. Gutierrez, A. Hall, H. Hildebrandt, J. Hjorth, L. K. Hunt, J. J. E. Kajava, Y. Kang, V. Kansal, D. Karagiannis, K. Kiiveri, J. Kim, C. C. Kirkpatrick, S. Kruk, M. Lattanzi, L. Legrand, F. Lepori, G. Leroy, G. F. Lesci, J. Lesgourgues, T. I. Liaudat, A. Loureiro, M. Magliocchetti, F. Mannucci, C. J. A. P. Martins, L. Maurin, M. Miluzio, C. Moretti, G. Morgante, K. Naidoo, P. Natoli, A. Navarro-Alsina, S. Nesseris, D. Paoletti, F. Passalacqua, K. Paterson, L. Patrizii, A. Pisani, D. Potter, G. W. Pratt, S. Quai, M. Radovich, G. Rodighiero, W. Roster, S. Sacquegna, M. Sahlén, D. B. Sanders, E. Sarpa, A. Schneider, D. Sciotti, E. Sellentin, L. C. Smith, J. G. Sorce, K. Tanidis, C. Tao, F. Tarsitano, G. Testera, R. Teyssier, S. Tosi, A. Troja, A. Venhola, D. Vergani, G. Vernardos, G. Verza, P. Vielzeuf, S. Vinciguerra, N. A. Walton, A. H. Wright

Categories: astro-ph.GA

Abstract:
We present 72 additional galaxy-galaxy strong lenses that complement the sample discovered in the Euclid Quick Release 1 data (63.1 deg^2) of the Strong Lens Discovery Engine (SLDE) papers A-E. It is shown that previous pre-selection of potential lenses, which excluded objects from the Gaia catalogue, led to missing several bright and low-redshift strong lenses, adding more than 10% new strong lens candidates compared to the previous search. In total, the catalogue includes 38 "grade A" (confident) and 34 "grade B" (probable) candidates. These lenses are identified through a combination of two independent searches for bright nearby objects: one based on machine-learning models followed by expert visual inspection, and the other based solely on expert visual inspection, targeting objects not included in the initial machine-learning selection (a limitation identified only after extensive visual inspection). With these additional strong lens candidates, we augment the expected number of high-confidence candidates in the Euclid Wide Survey from previous forecasts to 120000. Detailed semi-automated lens modelling confirms at least 41 systems out of 72, a fraction consistent with that found in SLDE A (315 out of 488). These include: multiple edge-on disc lenses; sources with arcs near the lens centre; "red sources"; and an edge-on disk galaxy lensing a galaxy merger, producing two sets of lensed features, an Einstein ring and a doubly imaged component. The median redshift of these systems is $Δ$ z ~ 0.3 lower than that of the SLDE A sample.

arXiv Page | PDF

Published: 2026-03-30 15:26:00

Authors: Yi Yu, Guangquan Hu, Chenghuang Shen, Xingyan Liu, Jing Gu, Hangyi Sun, Junzhuo Ma, Weiting Liu, Jianfeng Liu, Mingyue Pu, Yu Wang, Zhengdong Xiao, Rui Xie, Longjiu Luo, Qianrong Wang, Gurong Cui, Honglin Qiao, Wenlian Lu

Categories: cs.LG, cs.AI, cs.IR, cs.PF

Abstract:
The increasing agentic capabilities of Large Language Models (LLMs) have enabled their deployment in real-world applications, such as cloud services, where customer-assistant interactions exhibit high technical complexity and long-horizon dependencies, making robustness and resolution efficiency critical for customer satisfaction. However, existing benchmarks for LLM-based agents largely rely on synthetic environments that fail to capture the diversity and unpredictability of authentic customer inputs, often ignoring the resolution efficiency essential for real-world deployment. To bridge this gap, we introduce CirrusBench, a novel evaluation framework distinguished by its foundation in real-world data from authentic cloud service tickets. CirrusBench preserves the intricate multi-turn logical chains and realistic tool dependencies inherent to technical service environments. Moving beyond execution correctness, we introduce novel Customer-Centric metrics to define agent success, quantifying service quality through metrics such as the Normalized Efficiency Index and Multi-Turn Latency to explicitly measure resolution efficiency. Experiments utilizing our framework reveal that while state-of-the-art models demonstrate strong reasoning capabilities, they frequently struggle in complex, realistic multi-turn tasks and fail to meet the high-efficiency standards required for customer service, highlighting critical directions for the future development of LLM-based agents in practical technical service applications. CirrusBench evaluation framework is released at: https://github.com/CirrusAI

arXiv Page | PDF

Published: 2026-03-30 15:22:58

Authors: Luke Rickard, Paola Falugi, Eric C. Kerrigan

Categories: cs.GT, eess.SY

Abstract:
Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to cooperatively reduce costs; however, existing bottom-up coalition-formation methods typically require full information sharing, raising privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition-formation algorithm that requires only limited aggregate information exchange among agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real-world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full-information approaches.

arXiv Page | PDF

Published: 2026-03-30 15:08:32

Authors: Zhangqi Jiang, Zheng Sun, Xianfang Zeng, Yufeng Yang, Xuanyang Zhang, Yongliang Wu, Wei Cheng, Gang Yu, Xu Yang, Bihan Wen

Categories: cs.CV

Abstract:
Recent advances in image editing have enabled models to handle complex instructions with impressive realism. However, existing evaluation frameworks lag behind: current benchmarks suffer from narrow task coverage, while standard metrics fail to adequately capture visual consistency, i.e., the preservation of identity, structure and semantic coherence between edited and original images. To address these limitations, we introduce GEditBench v2, a comprehensive benchmark with 1,200 real-world user queries spanning 23 tasks, including a dedicated open-set category for unconstrained, out-of-distribution editing instructions beyond predefined tasks. Furthermore, we propose PVC-Judge, an open-source pairwise assessment model for visual consistency, trained via two novel region-decoupled preference data synthesis pipelines. Besides, we construct VCReward-Bench using expert-annotated preference pairs to assess the alignment of PVC-Judge with human judgments on visual consistency evaluation. Experiments show that our PVC-Judge achieves state-of-the-art evaluation performance among open-source models and even surpasses GPT-5.1 on average. Finally, by benchmarking 16 frontier editing models, we show that GEditBench v2 enables more human-aligned evaluation, revealing critical limitations of current models, and providing a reliable foundation for advancing precise image editing.

arXiv Page | PDF

Published: 2026-03-30 14:55:45

Authors: James Hornick, Dmitry E. Pelinovsky

Categories: math.AP, math.DS, nlin.PS, nlin.SI, physics.flu-dyn

Abstract:
We consider families of solitary waves in the Korteweg--de Vries (KdV) equation coupled with the linear Schrödinger (LS) equation. This model has been used to describe interactions between long and short waves. To characterize families of solitary waves, we consider a sequence of local (pitchfork) bifurcations of the uncoupled KdV solitons. The first member of the sequence is the KdV soliton coupled with the ground state of the LS equation, which is proven to be the constrained minimizer of energy for fixed mass and momentum. The other members of the sequence are the KdV solitons coupled with the excited states of the LS equation. We connect the first two bifurcations with the exact solutions of the KdV--LS system frequently used in the literature.

arXiv Page | PDF

Published: 2026-03-30 14:39:04

Authors: Jin Bai, Huiyao Zhang, Qi Wen, Ningyang Li, Shengyang Li, Atta ur Rahman, Xiaolin Tian

Categories: cs.CV

Abstract:
The segmentation of thin linear structures is inherently topology allowbreak-critical, where minor local errors can sever long-range connectivity. While recent State-Space Models (SSMs) offer efficient long-range modeling, their isotropic serialization (e.g., raster scanning) creates a geometry mismatch for anisotropic targets, causing state propagation across rather than along the structure trajectories. To address this, we propose FGOS-Net, a framework based on frequency allowbreak-geometric disentanglement. We first decompose features into a stable topology carrier and directional high-frequency bands, leveraging the latter to explicitly correct spatial misalignments induced by downsampling. Building on this calibrated topology, we introduce frequency-aligned scanning that elevates serialization to a geometry-conditioned decision, preserving direction-consistent traces. Coupled with an active probing strategy to selectively inject high-frequency details and suppress texture ambiguity, FGOS-Net consistently outperforms strong baselines across four challenging benchmarks. Notably, it achieves 91.3% mIoU and 97.1% clDice on DeepCrack while running at 80 FPS with only 7.87 GFLOPs.

arXiv Page | PDF

Published: 2026-03-30 14:15:05

Authors: Wenxiong Chen, Yahong Guo, Congming Li, Yugao Ouyang

Categories: math.AP

Abstract:
In this paper, we establish a convergence result for the fully fractional heat operator $\ma{s}$, also known as the master operator, stated as follows: \[\mbox{If\ }u_i\to u\ \mbox{in}\ C^{2,1}_{x,t,loc}(\R^n\times\R),\ \mbox{then}\ \ma{s} u_i\to \ma{s}u-b\ \mbox{a.e. in}\ \R^n\times\R,\] for some nonnegative constant $b$. This result addresses a fundamental question in the blow-up and rescaling analysis, which are essential for establishing a priori estimates for solutions of master equations. Additionally, we present examples demonstrating that in certain cases, the constant $b$ can indeed be positive. This highlights a key distinction between nonlocal and local operators: for a local heat operator, such as $\partial_t - \lap$, it is well-known that $b \equiv 0$.

arXiv Page | PDF

Published: 2026-03-30 14:14:37

Authors: Ningyu Yan, Shuai Wang, Xing Shen, Hui Wang, Hanqing Wang, Yang Xiang, Jiangmiao Pang

Categories: cs.RO

Abstract:
Visuotactile sensors are indispensable for contact-rich robotic manipulation tasks. However, policy learning with tactile feedback in simulation, especially for online reinforcement learning (RL), remains a critical challenge, as it demands a delicate balance between physics fidelity and computational efficiency. To address this challenge, we present Tac2Real, a lightweight visuotactile simulation framework designed to enable efficient online RL training. Tac2Real integrates the Preconditioned Nonlinear Conjugate Gradient Incremental Potential Contact (PNCG-IPC) method with a multi-node, multi-GPU high-throughput parallel simulation architecture, which can generate marker displacement fields at interactive rates. Meanwhile, we propose a systematic approach, TacAlign, to narrow both structured and stochastic sources of domain gap, ensuring a reliable zero-shot sim-to-real transfer. We further evaluate Tac2Real on the contact-rich peg insertion task. The zero-shot transfer results achieve a high success rate in the real-world scenario, verifying the effectiveness and robustness of our framework. The project page is: https://ningyurichard.github.io/tac2real-project-page/

arXiv Page | PDF

Published: 2026-03-30 14:06:50

Authors: Georg Keilbar, Sonja Greven

Categories: econ.EM, stat.ME

Abstract:
We propose a novel framework for conducting causal inference based on counterfactual densities. While the current paradigm of causal inference is mostly focused on estimating average treatment effects (ATEs), which restricts the analysis to the first moment of the outcome variable, our density-based approach is able to detect causal effects based on general distributional characteristics. Following the Oaxaca-Blinder decomposition approach, we consider two types of counterfactual density effects that together explain observed discrepancies between the densities of the treated and control group. First, the distribution effect is the counterfactual effect of changing the conditional density of the control group to that of the treatment group, while keeping the covariates fixed at the treatment group distribution. Second, the covariate effect represents the effect of a hypothetical change in the covariate distribution. Both effects have a causal interpretation under the classical unconfoundedness and overlap assumptions. Methodologically, our approach is based on analyzing the conditional densities as elements of a Bayes Hilbert space, which preserves the non-negativity and integration-to-one constraints. We specify a flexible functional additive regression model estimating the conditional densities. We apply our method to analyze the German East--West income gap, i.e., the observed differences in wages between East Germans and West Germans. While most of the existing studies focus on the average differences and neglect other distributional characteristics, our density-based approach is suited to detect all nuances of the counterfactual distributions, including differences in probability masses at zero.

arXiv Page | PDF

Published: 2026-03-30 14:01:22

Authors: Himanshu Dongre, Lane G. Gunderman

Categories: quant-ph

Abstract:
Protecting information in systems that have more than two basis states (qudits) not only offers a promising route for reducing the number of individual quantum locations that must be protected, while more accurately reflecting the structure of realistic quantum hardware, but also has some possibly enticing foundational strengths. While work in the past has largely focused on protecting information in quantum devices with locations that are some consistent local structure, this work considers coding-theoretic constraints on devices constructed from locations which may vary in their local structures -- these are mixed-register quantum devices. In this work we provide some general results for mixed-register Pauli operators, then identify some stabilizer encoded information forms that are forbidden. Building on these insights, we construct coding-theoretically optimal mixed-register stabilizer codes from sets of codes defined on coprime local-dimensions. The construction of such codes results in codes with logical subspaces that do not directly correspond to any of the constituent local-dimensions.

arXiv Page | PDF

Published: 2026-03-30 13:58:57

Authors: LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, Z. B. Bai, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Baron, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bavarchee, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, I. Belov, I. Belyaev, G. Bencivenni, E. Ben-Haim, J. L. M. Berkey, R. Bernet, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, C. Bolognani, R. Bolzonella, R. B. Bonacci, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, C. Breitfeld, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. C. Campos, A. F. Campoverde Quezada, Y. Cao, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, S. Chernyshenko, X. Chiotopoulos, G. Chizhik, V. Chobanova, M. Chrzaszcz, V. Chulikov, P. Ciambrone, X. Cid Vidal, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, G. C. Costantino, J. Cottee Meldrum, B. Couturier, D. C. Craik, N. Crepet, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C. L. Da Silva, X. Dai, J. Dalseno, C. D'Ambrosio, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, S. Ding, Y. Ding, L. Dittmann, A. D. Docheva, A. Doheny, C. Dong, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, K. Duwe, A. Dziurda, S. Easo, E. Eckstein, U. Egede, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, C. Feng, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, A. Golutvin, S. Gomez Fernandez, W. Gomulka, F. Goncalves Abrantes, I. Gonçales Vaz, M. Goncerz, G. Gong, J. A. Gooding, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, Q. Han, S. Han, X. Han, S. Hansmann-Menzemer, N. Harnew, T. J. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, D. Hutchcroft, M. Idzik, P. Ilten, A. Iohner, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, A. Jawahery, C. Jayaweera, A. Jelavic, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, S. Kholodenko, G. Khreich, F. Kiraz, T. Kirn, V. S. Kirsebom, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, O. Kravcov, M. Kreps, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, M. Lupberger, E. Luppi, K. Lynch, S. Lyu, X. -R. Lyu, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, J. J. Malczewski, S. Malde, L. Malentacca, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, A. Mangalasseri, F. M. Manganella, D. Manuzzi, S. Mao, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, C. Martinez, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, D. Mazzanti Tarancon, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. Mu, N. Muangkod, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, G. Napoletano, I. Nasteva, M. Needham, N. Neri, S. Neubert, N. Neufeld, J. Nicolini, D. Nicotra, E. M. Niel, L. Nisi, Q. Niu, B. K. Njoki, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, H. N. Nur, A. Oblakowska-Mucha, T. Oeser, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, B. Rachwal, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, M. S. Rudolph, M. Ruiz Diaz, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, R. S. Sharma, L. Shchutska, T. Shears, J. Shen, Z. Shen, S. Sheng, B. Shi, J. Shi, Q. Shi, W. S. Shi, E. Shmanin, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, I. Siral, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, F. Suljik, J. Sun, L. Sun, M. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, A. Upadhyay, B. Urbach, A. Usachov, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, K. Vos, C. Vrahas, J. Wagner, J. Walsh, N. Walter, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, R. Xu, Z. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, Z. Yang, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, S. H Zeng, C. Zhang, D. Zhang, D. Zhang, J. Zhang, L. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Z. Zhang, J. Zhao, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, D. Zuliani, G. Zunica

Categories: hep-ex

Abstract:
The first observation of the doubly charmed baryon $\itΞ_{cc}^+$ is reported through its decay to the $\itΛ_c^+ K^-π^+$ final state, with a statistical significance exceeding seven standard deviations. The observation is made using proton-proton collision data collected in 2024 with the LHCb Run 3 detector at a center-of-mass energy of 13.6 TeV, corresponding to a total integrated luminosity of $6.9\,\mathrm{fb}^{-1}$. The $\itΞ_{cc}^+$ mass is measured to be $3619.97 \pm 0.83 \pm 0.26 \,^{+1.90}_{-1.30}\,\mathrm{MeV}/c^2$, where the first uncertainty is statistical, the second is systematic, and the third is due to the unknown $\itΞ_{cc}^+$ lifetime, which is assumed to lie in the range 15-160 fs with a baseline value of 45 fs. The difference between the masses of the $\itΞ_{cc}^+$ and $\itΞ_{cc}^{++}$ baryons is determined to be $-1.77 \pm 0.84 \pm 0.15 \,^{+1.90}_{-1.30}\,\mathrm{MeV}/c^2$. This is the first observation of a new particle made with the LHCb Run 3 detector.

arXiv Page | PDF

Published: 2026-03-30 13:58:36

Authors: Tiantian Wang, Xiang Xiang, Simon S. Du

Categories: cs.LG, cs.AI, cs.CV, cs.DC, stat.ML

Abstract:
In federated healthcare systems, Federated Class-Incremental Learning (FCIL) has emerged as a key paradigm, enabling continuous adaptive model learning among distributed clients while safeguarding data privacy. However, in practical applications, data across agent nodes within the distributed framework often exhibits non-independent and identically distributed (non-IID) characteristics, rendering traditional continual learning methods inapplicable. To address these challenges, this paper covers more comprehensive incremental task scenarios and proposes a dynamic memory allocation strategy for exemplar storage based on the data replay mechanism. This strategy fully taps into the inherent potential of data heterogeneity, while taking into account the performance fairness of all participating clients, thereby establishing a balanced and adaptive solution to mitigate catastrophic forgetting. Unlike the fixed allocation of client exemplar memory, the proposed scheme emphasizes the rational allocation of limited storage resources among clients to improve model performance. Furthermore, extensive experiments are conducted on three medical image datasets, and the results demonstrate significant performance improvements compared to existing baseline models.

arXiv Page | PDF

Published: 2026-03-30 13:46:31

Authors: Yubo Zhang, Zhiguo Hao, Songhao Yang, Baohui Zhang

Categories: eess.SY

Abstract:
Additional active power control (AAPC) of wind turbines (WTs) is essential to improve the transient frequency stability of low-inertia power systems. Most of the existing research has focused on imitating the frequency response of the synchronous generator (SG), known as virtual inertia control (VIC), but are such control laws optimal for the power systems? Inspired by this question, this paper proposes an optimal AAPC of WTs to maximize the frequency nadir post a major power deficit. By decoupling the WT response and the frequency dynamics, the optimal frequency trajectory is solved based on the trajectory model, and its universality is strictly proven. Then the optimal AAPC of WTs is constructed reversely based on the average system frequency (ASF) model with the optimal frequency trajectory as the desired control results. The proposed method can significantly improve the system frequency nadir. Meanwhile, the event insensitivity makes it can be deployed based on the on-line rolling update under a hypothetic disturbance, avoiding the heavy post-event computational burden. Finally, simulation results in a two-machine power system and the IEEE 39 bus power system verify the effectiveness of the optimal AAPC of WTs.

arXiv Page | PDF

Published: 2026-03-30 13:46:29

Authors: Stephane Ngnepiepaye Wembe, Vincent Rousseau, Johann Laconte, Roland Lenain

Categories: cs.RO

Abstract:
Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enable precise, efficient, and environmentally friendly operations. However, most existing path-following controllers focus solely on the robot's center of motion and neglect the spatial footprint and dynamics of attached implements. In practice, implements such as mechanical weeders or spring-tine cultivators are often large, rigidly mounted, and directly interacting with crops and soil; ignoring their position can degrade tracking performance and increase the risk of crop damage. To address this limitation, we propose a closed-form predictive control strategy extending the approach introduced in [1]. The method is developed specifically for Ackermann-type agricultural vehicles and explicitly models the implement as a rigid offset point, while accounting for lateral slip and lever-arm effects. The approach is benchmarked against state-of-the-art baseline controllers, including a reactive geometric method, a reactive backstepping method, and a model-based predictive scheme. Real-world agricultural experiments with two different implements show that the proposed method reduces the median tracking error by 24% to 56%, and decreases peak errors during curvature transitions by up to 70%. These improvements translate into enhanced operational safety, particularly in scenarios where the implement operates in close proximity to crop rows.

arXiv Page | PDF

Published: 2026-03-30 13:35:37

Authors: Xiaohang Nie, Zihan Guo, Kezhuo Yang, Zhichong Zheng, Bochen Ge, Shuai Pan, Zeyi Chen, Youling Xiang, Yu Zhang, Weiwen Liu, Yuanjian Zhou, Weinan Zhang

Categories: cs.CY, cs.MA

Abstract:
AI agents are rapidly expanding in both capability and population: they now write code, operate computers across platforms, manage cloud infrastructure, and make purchasing decisions, while open-source frameworks such as OpenClaw are putting personal agents in the hands of millions and embodied agents are spreading across smartphones, vehicles, and robots. As the internet prepares to host billions of such entities, it is shifting toward what we call Open Agentic Web, a decentralized digital ecosystem in which agents from different users, organizations, and runtimes can discover one another, negotiate task boundaries, and delegate work across open technical and social surfaces at scale. Yet most of today's agents remain isolated tools or closed-ecosystem orchestrators rather than socially integrated participants in open networks. We argue that the next generation of agents must become Agentic Citizens, defined by three requirements: Agentic-Web-Native Collaboration, participation in open collaboration networks rather than only closed internal orchestration; Agent Identity and Personhood, continuity as a social entity rather than a resettable function call; and Lifelong Evolution, improvement across task performance, communication, and collaboration over time. We present Synergy, a general-purpose agent architecture and runtime harness for persistent, collaborative, and evolving agents on Open Agentic Web, grounding collaboration in session-native orchestration, repository-backed workspaces, and social communication; identity in typed memory, notes, agenda, skills, and persistent social relationships; and evolution in an experience-centered learning mechanism that proactively recalls rewarded trajectories at inference time.

arXiv Page | PDF

Published: 2026-03-30 13:33:11

Authors: Kosei Fushimi, Kazunobu Serizawa, Junya Ikemoto, Kazumune Hashimoto

Categories: cs.CL, cs.SC

Abstract:
Signal Temporal Logic (STL) is widely used to specify timed and safety-critical tasks for cyber-physical systems, but writing STL formulas directly is difficult for non-expert users. Natural language (NL) provides a convenient interface, yet its inherent structural ambiguity makes one-to-one translation into STL unreliable. In this paper, we propose an \textit{ambiguity-preserving} method for translating NL task descriptions into STL candidate formulas. The key idea is to retain multiple plausible syntactic analyses instead of forcing a single interpretation at the parsing stage. To this end, we develop a three-stage pipeline based on Combinatory Categorial Grammar (CCG): ambiguity-preserving $n$-best parsing, STL-oriented template-based semantic composition, and canonicalization with score aggregation. The proposed method outputs a deduplicated set of STL candidates with plausibility scores, thereby explicitly representing multiple possible formal interpretations of an ambiguous instruction. In contrast to existing one-best NL-to-logic translation methods, the proposed approach is designed to preserve attachment and scope ambiguity. Case studies on representative task descriptions demonstrate that the method generates multiple STL candidates for genuinely ambiguous inputs while collapsing unambiguous or canonically equivalent derivations to a single STL formula.

arXiv Page | PDF

Published: 2026-03-30 13:31:54

Authors: Victoria Leonenkova, Ekaterina Shumitskaya, Dmitriy Vatolin, Anastasia Antsiferova

Categories: cs.CV

Abstract:
This demonstration presents Digital-Physical Adversarial Attacks (DiPA), a new class of practical adversarial attacks against pervasive camera-based authentication systems, where an attacker displays an adversarial patch directly on a smartphone screen instead of relying on printed artifacts. This digital-only physical presentation enables rapid deployment, removes the need for total-variation regularization, and improves patch transferability in black-box conditions. DiPA leverages an ensemble of state-of-the-art face-recognition models (ArcFace, MagFace, CosFace) to enhance transfer across unseen commercial systems. Our interactive demo shows a real-time dodging attack against a deployed face-recognition camera, preventing authorized users from being recognized while participants dynamically adjust patch patterns and observe immediate effects on the sensing pipeline. We further demonstrate DiPA's superiority over existing physical attacks in terms of success rate, feature-space distortion, and reductions in detection confidence, highlighting critical vulnerabilities at the intersection of mobile devices, pervasive vision, and sensor-driven authentication infrastructures.

arXiv Page | PDF

Published: 2026-03-30 13:10:57

Authors: Maria Carvalho, Fagner B. Rodrigues

Categories: math.DS

Abstract:
We introduce the notion of dynamical metric order of a continuous map on a compact metric space, %study its basic properties, and compute it for several classes of maps. This concept which is a counterpart of the metric mean dimension with the role of the box-counting dimension being played by the metric order. It is devised for maps acting on spaces with infinite box-counting dimension but finite metric order. For example, it brings forward new information about full shifts whose alphabets have infinite box-counting dimension; and provides a sharper estimate of complexity for the induced map determined by a continuous transformation on a compact metric space, whose upper metric mean dimension is known to admit only two values (zero or infinity). We also show that it satisfies a variational principle where maximization is taken over the space of invariant probability measures and whose equilibrium states always exist.

arXiv Page | PDF

Published: 2026-03-30 13:05:44

Authors: Luca Minnei, Cristian Manca, Giorgio Piras, Angelo Sotgiu, Maura Pintor, Daniele Ghiani, Davide Maiorca, Giorgio Giacinto, Battista Biggio

Categories: cs.LG, cs.CR

Abstract:
Machine Learning (ML)-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature of real-world settings, where both legitimate and malicious software evolve. This distribution drift causes models trained under static assumptions to degrade over time unless they are continuously updated. Regularly retraining these models, however, is expensive, since labeling new acquired data requires costly manual analysis by security experts. To reduce labeling costs and address distribution drift in malware detection, prior work explored active learning (AL) and semi-supervised learning (SSL) techniques. Yet, existing studies (i) are tightly coupled to specific detector architectures and restricted to a specific malware domain, resulting in non-uniform comparisons; and (ii) lack a consistent methodology for analyzing the distribution drift, despite the critical sensitivity of the malware domain to temporal changes. In this work, we bridge this gap by proposing a model-agnostic framework that evaluates an extensive set of AL and SSL techniques, isolated and combined, for Android and Windows malware detection. We show that these techniques, when combined, can reduce manual annotation costs by up to 90% across both domains while achieving comparable detection performance to full-labeling retraining. We also introduce a methodology for feature-level drift analysis that measures feature stability over time, showing its correlation with the detector performance. Overall, our study provides a detailed understanding of how AL and SSL behave under distribution drift and how they can be successfully combined, offering practical insights for the design of effective detectors over time.

arXiv Page | PDF

Published: 2026-03-30 12:56:54

Authors: Alkis Sygkounas, Rishi Hazra, Andreas Persson, Pedro Zuidberg Dos Martires, Amy Loutfi

Categories: cs.AI

Abstract:
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and generalization beyond the training distribution. We address this with COvolve, a co-evolutionary framework that leverages large language models (LLMs) to generate both environments and agent policies, expressed as executable Python code. We model the interaction between environment and policy designers as a two-player zero-sum game, ensuring adversarial co-evolution in which environments expose policy weaknesses and policies adapt in response. This process induces an automated curriculum in which environments and policies co-evolve toward increasing complexity. To guarantee robustness and prevent forgetting as the curriculum progresses, we compute the mixed-strategy Nash equilibrium (MSNE) of the zero-sum game, thereby yielding a meta-policy. This MSNE meta-policy ensures that the agent does not forget to solve previously seen environments while learning to solve previously unseen ones. Experiments in urban driving, symbolic maze-solving, and geometric navigation showcase that COvolve produces progressively more complex environments. Our results demonstrate the potential of LLM-driven co-evolution to achieve open-ended learning without predefined task distributions or manual intervention.

arXiv Page | PDF

Published: 2026-03-30 12:48:59

Authors: En-Ming Huang, Shih-Hao Hung

Categories: cs.DC

Abstract:
Static timing analysis (STA) is crucial for Electronic Design Automation (EDA) flows but remains a computational bottleneck. While existing GPU-based STA engines are faster than CPU, they suffer from inefficiencies, particularly intra-warp load imbalance caused by irregular circuit graphs. This paper introduces Warp-STAR, a novel GPU-accelerated STA engine that eliminates this imbalance by orchestrating parallel computations at the warp level. This approach achieves a 2.4X speedup over previous state-of-the-art (SoTA) GPU-based STA. When integrated into a timing-driven global placement framework, Warp-STAR delivers a 1.7X speedup over SoTA frameworks. The method also proves effective for differentiable gradient analysis with minimal overhead.

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Published: 2026-03-30 12:40:18

Authors: Kagan Akcay, MohammadJavad Salehi, Giuseppe Caire

Categories: cs.IT

Abstract:
Coded caching (CC) exploits cumulative cache memory at user devices and coding to transform unicast traffic into multicast transmissions. While information theoretic results show significant gains over uncoded caching for various network topologies, its practical benefits remain unclear. In this work, we investigate CC for on-demand video streaming over large wireless local area networks, where multiple users are served simultaneously by spatially distributed access points. Users asynchronously request video chunks from a content library. We propose a decentralized, asynchronous, and location-independent cache placement scheme combined with an "over IP" delivery mechanism operating at higher network layers, leaving the physical and MAC layers unchanged. For this scheme, we characterize the achievable goodput region, where goodput is defined as the number of video chunks per unit time delivered to users' playback buffers, and formulate the corresponding fairness problem as a convex maximization. We develop a dynamic scheduling algorithm that provably achieves the optimal fairness point under stationary conditions with reduced complexity, and introduce a heuristic to further lower complexity. Numerical results demonstrate significant gains over baseline schemes, including conventional prefix caching, orthogonal sub-channel allocation with spatial reuse, and a CSMA-inspired distributed coordination approach, showing that CC can be implemented as a scalable and compatible over IP solution for existing WLANs.

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Published: 2026-03-30 12:30:09

Authors: Zhihao Lv, Xiaoyong Zhang, Mengfan Zhang, Xiaoyu Song, Xingyue Liu, Yide Liu, Shaoxing Qu, Guoyong Mao

Categories: cs.RO, cond-mat.mtrl-sci, cond-mat.soft, physics.app-ph

Abstract:
Multimodal locomotion is crucial for an animal's adaptability in unstructured wild environments. Similarly, in the human gastrointestinal tract, characterized by viscoelastic mucus, complex rugae, and narrow sphincters like the cardia, multimodal locomotion is also essential for a small-scale soft robot to conduct tasks. Here, we introduce a small-scale compact, foldable, and robust soft electromagnetic robot (M-SEMR) with more than nine locomotion modes designed for such a scenario. Featuring a six-spoke elastomer body embedded with liquid metal channels and driven by Laplace forces under a static magnetic field, the M-SEMR is capable of rapid transitions (< 0.35 s) among different locomotion modes. It achieves exceptional agility, including high-speed rolling (818 mm/s, 26 BL/s), omnidirectional crawling, jumping, and swimming. Notably, the robot can fold to reduce its volume by 79%, enabling it to traverse confined spaces. We further validate its navigation capabilities on complex terrains, including discrete obstacles, viscoelastic gelatin surfaces, viscous fluids, and simulated biological tissues. This system offers a versatile strategy for developing high-mobility soft robots for future biomedical applications.

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Published: 2026-03-30 12:29:47

Authors: Yipeng Yu

Categories: cs.AI, cs.MA

Abstract:
With the advancement of large language models (LLMs) in their knowledge base and reasoning capabilities, their interactive modalities have evolved from pure text to multimodality and further to agentic tool use. Consequently, their applications have broadened from question answering to AI assistants and now to general-purpose agents. Deep research (DR) represents a prototypical vertical application for general-purpose agents, which represents an ideal approach for intelligent information processing and assisting humans in discovering and solving problems, with the goal of reaching or even surpassing the level of top human scientists. This paper provides a deep research of deep research. We articulate a clear and precise definition of deep research and unify perspectives from industry's deep research and academia's AI for Science (AI4S) within a developmental framework. We position LLMs and Stable Diffusion as the twin pillars of generative AI, and lay out a roadmap evolving from the Transformer to agents. We examine the progress of AI4S across various disciplines. We identify the predominant paradigms of human-AI interaction and prevailing system architectures, and discuss the major challenges and fundamental research issues that remain. AI supports scientific innovation, and science also can contribute to AI growth (Science for AI, S4AI). We hope this paper can help bridge the gap between the AI and AI4S communities.

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Published: 2026-03-30 12:28:26

Authors: Kangkang Sun, Jun Wu, Jianhua Li, Minyi Guo, Xiuzhen Che, Jianwei Huang

Categories: cs.AI

Abstract:
Uncertainty estimation in multi-LLM systems remains largely single-model-centric: existing methods quantify uncertainty within each model but do not adequately capture semantic disagreement across models. To address this gap, we propose Collaborative Entropy (CoE), a unified information-theoretic metric for semantic uncertainty in multi-LLM collaboration. CoE is defined on a shared semantic cluster space and combines two components: intra-model semantic entropy and inter-model divergence to the ensemble mean. CoE is not a weighted ensemble predictor; it is a system-level uncertainty measure that characterizes collaborative confidence and disagreement. We analyze several core properties of CoE, including non-negativity, zero-value certainty under perfect semantic consensus, and the behavior of CoE when individual models collapse to delta distributions. These results clarify when reducing per-model uncertainty is sufficient and when residual inter-model disagreement remains. We also present a simple CoE-guided, training-free post-hoc coordination heuristic as a practical application of the metric. Experiments on \textit{TriviaQA} and \textit{SQuAD} with LLaMA-3.1-8B-Instruct, Qwen-2.5-7B-Instruct, and Mistral-7B-Instruct show that CoE provides stronger uncertainty estimation than standard entropy- and divergence-based baselines, with gains becoming larger as additional heterogeneous models are introduced. Overall, CoE offers a useful uncertainty-aware perspective on multi-LLM collaboration.

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Published: 2026-03-30 12:22:22

Authors: Li-Heng Chen, Ke Cheng, Yahui Liu, Lei Shi, Shi-Sheng Huang, Hongbo Fu

Categories: cs.CV

Abstract:
Driving video generation has achieved much progress in controllability, video resolution, and length, but fails to support fine-grained object-level controllability for diverse driving videos, while preserving the spatiotemporal consistency, especially in long video generation. In this paper, we present a new driving video generation technique, called VistaGEN, which enables fine-grained control of specific entities, including 3D objects, images, and text descriptions, while maintaining spatiotemporal consistency in long video sequences. Our key innovation is the incorporation of multiview visual-language reasoning into the long driving video generation. To this end, we inject visual-language features into a multiview video generator to enable fine-grained controllability. More importantly, we propose a multiview vision-language evaluator (MV-VLM) to intelligently and automatically evaluate spatiotemporal consistency of the generated content, thus formulating a novel generation-evaluation-regeneration closed-loop generation mechanism. This mechanism ensures high-quality, coherent outputs, facilitating the creation of complex and reliable driving scenarios. Besides, within the closed-loop generation, we introduce an object-level refinement module to refine the unsatisfied results evaluated from the MV-VLM and then feed them back to the video generator for regeneration. Extensive evaluation shows that our VistaGEN achieves diverse driving video generation results with fine-grained controllability, especially for long-tail objects, and much better spatiotemporal consistency than previous approaches.

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Published: 2026-03-30 12:17:32

Authors: Giulia Pusceddu

Categories: cs.RO, cs.HC

Abstract:
Integrating social robots in our group-based society, beyond the technical challenges, requires considering the social group dynamics. Following the results from preliminary exploratory studies on the influence of social robots on group decisions, the proposed research investigates whether social robots can foster cooperation among group members. To achieve this, I propose a game theory approach, employing the Public Good Game to recreate a simplified and controlled social situation where the robot's influence can be evaluated. Clarifying the role of robots in promoting collaboration among humans might have a significant impact in educational environments, enhancing student learning, as well as in workplace settings, where they could facilitate problem-solving and lead to shared solutions.

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Published: 2026-03-30 12:14:24

Authors: Zihao Xu, Xiao Cheng, Ruijie Meng, Yuekang Li

Categories: cs.SE, cs.AI

Abstract:
LLM API calls are becoming a ubiquitous program construct, yet they create a boundary that no existing program analysis can cross: runtime values enter a natural-language prompt, undergo opaque processing inside the LLM, and re-emerge as code, SQL, JSON, or text that the program consumes. Every analysis that tracks data across function boundaries, including taint analysis, program slicing, dependency analysis, and change-impact analysis, relies on dataflow summaries of callee behavior. LLM calls have no such summaries, breaking all of these analyses at what we call the NL/PL boundary. We present the first information flow method to bridge this boundary. Grounded in quantitative information flow theory, our taxonomy defines 24 labels along two orthogonal dimensions: information preservation level (from lexically preserved to fully blocked) and output modality (natural language, structured format, executable artifact). We label 9,083 placeholder-output pairs from 4,154 real-world Python files and validate reliability with Cohen's $κ= 0.82$ and near-complete coverage (0.01\% unclassifiable). We demonstrate the taxonomy's utility on two downstream applications: (1)~a two-stage taint propagation pipeline combining taxonomy-based filtering with LLM verification achieves $F_1 = 0.923$ on 353 expert-annotated pairs, with cross-language validation on six real-world OpenClaw prompt injection cases further confirming effectiveness; (2)~taxonomy-informed backward slicing reduces slice size by a mean of 15\% in files containing non-propagating placeholders. Per-label analysis reveals that four blocked labels account for nearly all non-propagating cases, providing actionable filtering criteria for tool builders.

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Published: 2026-03-30 12:12:49

Authors: He Du, Qiming Ge, Jiakai Hu, Aijun Yang, Zheng Cai, Zixian Huang, Sheng Yuan, Qinxiu Cheng, Xinchen Xie, Yicheng Chen, Yining Li, Jiaxing Xie, Huanan Dong, Yaguang Wu, Xiangjun Huang, Jian Yang, Hui Wang, Bowen Zhou, Bowen Li, Qipeng Guo, Kai Chen

Categories: cs.CL, cs.LG

Abstract:
We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe. On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them using an archive of top-performing and diverse programs together with structured execution feedback on compilation, correctness, and speedup. To make this search reliable, we build backend-specific evaluation services for Triton on NVIDIA GPUs and Maca on MetaX GPUs. On the training side, we convert long-horizon evolution trajectories into step-centric supervision and reinforcement learning signals by retaining correctness-preserving, high-gain revisions, so that the model is optimized as a strong local improver inside the evolutionary loop rather than as a one-shot generator. Under a unified evolutionary protocol, Kernel-Smith-235B-RL achieves state-of-the-art overall performance on KernelBench with Nvidia Triton backend, attaining the best average speedup ratio and outperforming frontier proprietary models including Gemini-3.0-pro and Claude-4.6-opus. We further validate the framework on the MetaX MACA backend, where our Kernel-Smith-MACA-30B surpasses large-scale counterparts such as DeepSeek-V3.2-think and Qwen3-235B-2507-think, highlighting potential for seamless adaptation across heterogeneous platforms. Beyond benchmark results, the same workflow produces upstream contributions to production systems including SGLang and LMDeploy, demonstrating that LLM-driven kernel optimization can transfer from controlled evaluation to practical deployment.

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Published: 2026-03-30 12:12:29

Authors: Huynh Viet Khanh

Categories: math.GR

Abstract:
We prove that no cyclic division algebra (in the sense of Dickson) admits an abelian maximal subgroup in its multiplicative group. This settles a special case of a long-standing conjecture of Akbari--Mahdavi-Hezavehi--Mahmudi and complements earlier results on locally nilpotent maximal subgroups and provides a new malnormality criterion for maximal subgroups.

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Published: 2026-03-30 12:09:58

Authors: Ekaterina Torubarova, Jura Miniota, Andre Pereira

Categories: cs.HC, cs.RO

Abstract:
In this paper, we investigated how the choice of a Wizard-of-Oz (WoZ) interface affects communication with a robot from both the user's and the wizard's perspective. In a conversational setting, we used three WoZ interfaces with varying levels of dialogue input and output restrictions: a) a restricted perception GUI that showed fixed-view video and ASR transcripts and let the wizard trigger pre-scripted utterances and gestures; b) an unrestricted perception GUI that added real-time audio from the participant and the robot c) a VR telepresence interface that streamed immersive stereo video and audio to the wizard and forwarded the wizard's spontaneous speech, gaze and facial expressions to the robot. We found that the interaction mediated by the VR interface was preferred by users in terms of robot features and perceived social presence. For the wizards, the VR condition turned out to be the most demanding but elicited a higher social connection with the users. VR interface also induced the most connected interaction in terms of inter-speaker gaps and overlaps, while Restricted GUI induced the least connected flow and the largest silences. Given these results, we argue for more WoZ studies using telepresence interfaces. These studies better reflect the robots of tomorrow and offer a promising path to automation based on naturalistic contextualized verbal and non-verbal behavioral data.

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Published: 2026-03-30 12:05:08

Authors: Alberto Torralba, Jorryt Matthee, Andrea Weibel, Rohan P. Naidu, Yilun Ma, Aidan P. Cloonan, Aayush Desai, Anna de Graaff, Jenny E. Greene, Christian Kragh Jespersen, Ivan G. Kramarenko, Sara Mascia, Pascal A. Oesch, Wendy Q. Sun, Christina C. Williams

Categories: astro-ph.GA

Abstract:
Recent studies at high redshift have revealed an enigmatic class of Little Red Dots (LRDs) with extreme Balmer breaks, stronger than in any stellar atmosphere. However, it is unclear whether such objects exist at lower redshift, especially given the low number of LRDs reported at $z\lesssim 2$. Here we report the discovery of PAN-BH*-1, an LRD with an extreme Balmer break at $z=1.73$, identified from JWST/NIRCam pure-parallel imaging taken by the PANORAMIC survey, and confirmed by deep VLT/X-Shooter spectroscopy. The rest-optical to near-infrared spectral energy distribution of PAN-BH*-1 is consistent with a photospheric continuum with effective temperature $T_{\rm eff}\approx 4800$ K. The broad H$α$ emission line shows remarkably deep absorption, stronger than previously measured in any LRD. The absorption trough spans from $-520$ km/s to $+267$ km/s with respect to the systemic redshift. The presence of blue- and red-shifted absorption suggests complex dynamics of the obscuring gas along the line of sight. We speculate that the absorption trough can be produced by a thick wind launched from a thick, rotating photospheric disk, the latter being the source of the red optical continuum. While the source is unresolved in the rest-optical JWST data ($r_{\rm eff,UV}<47$ pc), the rest-NUV HST imaging shows an extended morphology with $r_{\rm eff,opt}=1.0^{+0.5}_{-0.3}$ kpc, that we interpret as a host galaxy with a stellar mass $\sim 10^8$ $M_\odot$, in line with the narrow H$α$ emission. The discovery of this object at cosmic noon highlights the feasibility of systematic searches for extreme LRDs with wide-area facilities such as Euclid and Roman.

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Published: 2026-03-30 11:59:35

Authors: Michele Fornea

Categories: math.NT

Abstract:
We introduce a new collection of partially global Galois cohomology classes subsuming both plectic Heegner points and mock plectic invariants. The former are recovered as localizations of plectic Heegner classes, while the latter arise as eigenspace projections with respect to a "partial Frobenius"-action. By overcoming some limitations of previous constructions, plectic Heegner classes are expected to provide finer control over the arithmetic of higher rank elliptic curves. We are able to perform our construction via a systematic use of certain automorphic functions whose coefficients are p-adic measures valued in Galois cohomology. As we produce these functions through the uniformization of Shimura curves -- rather than higher dimensional quaternionic Shimura varieties -- our results are compatible with a plectic refinement of Tate's conjectures.

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Published: 2026-03-30 11:38:07

Authors: Robert Whyte, Manni Cheung, Katharine Childs, Jane Waite, Sue Sentance

Categories: cs.CY, cs.AI

Abstract:
Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.

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Published: 2026-03-30 11:34:29

Authors: Tony Liimatainen, Shubham Jaiswal

Categories: math.AP

Abstract:
We initiate the study of inverse source problems for quasilinear elliptic equations of the form \[ \left\{ \begin{array}{ll} \nabla \cdot (γ(x,u,\nabla u) \nabla u) = F & \text{in } Ω, \\ u = f & \text{on } \partialΩ, \end{array} \right. \] where $Ω\subset \mathbb{R}^n$, $n \geq 2$, is a simply connected bounded domain. We consider the specific nonlinearity $γ(x,u,\nabla u) = σ(x) + q(x) u$, with $q$ assumed to be known. By exploiting the nonlinearity to break the gauge invariance of the problem, we establish unique recovery of both $σ$ and $F$ from the associated Dirichlet-to-Neumann (DN) map under the structural conditions $q$ and $\nabla(σ/q)$ are nowhere vanishing in $\overlineΩ$. In the absence of these conditions, in particular in the linear case, we demonstrate that the inverse problem admits a gauge obstructing the uniqueness. We use higher order linearizations to obtain a complicated coupled system for the unknowns. The complexity of this system arises in part from the gauge freedom of the linearized equation, which is new in this context. We solve the system by constructing suitable complex geometric optics solutions and applying the unique continuation principle for nonlinear elliptic systems. We anticipate that the solution method developed here will prove useful in other inverse problems as well.

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Published: 2026-03-30 11:33:32

Authors: Aymen Lassoued, Nacef Mbarek, Bechir Dardouri, Bassem Ouni, Qing Li, Fakhri Karray

Categories: cs.CR

Abstract:
Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection performance, their multi-billion-parameter scale makes them impractical for integration into development workflows requiring low latency and continuous analysis. We introduce VULNSCOUT-C, a compact transformer architecture with 693M total parameters (353M active during inference), derived from the Qwen model family and optimized for C code vulnerability detection. Alongside the model, we present VULNSCOUT, a new 33,565-sample curated dataset generated through a controlled multi-agent pipeline with formal verification, designed to fill coverage gaps in existing benchmarks across underrepresented CWE categories. Evaluated on a standardized C vulnerability detection benchmark, VULNSCOUT-C outperforms all evaluated baselines, including state-of-the-art reasoning LLMs and commercial static analysis tools, while offering a fraction of their inference cost. These results demonstrate that task-specialized compact architectures can match or even outperform the detection capability of models orders of magnitude larger, making continuous, low-latency vulnerability analysis practical within real-world development workflows.

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Published: 2026-03-30 11:26:27

Authors: Rakhimov Kamoladdin, Sobirov Zarifboy, Jabborov Nasridin

Categories: math.AP

Abstract:
In this work we investigate Cauchy problem and initial boundary value problem for time-fractional Airy equation on the graphs with infinite and finite bonds. We studied properties of potentials for this equation and using these properties found the solutions of the considered problems. The uniqueness theorem is proved using the analogue of Grönwall-Bellman inequality and a-priory estimate.

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Published: 2026-03-30 11:23:57

Authors: Kun Tang, Xinquan Yang, Mianjie Zheng, Xuefen Liu, Xuguang Li, Xiaoqi Guo, Ruihan Chen, Linlin Shen, He Meng

Categories: cs.CV

Abstract:
The scarcity and high cost of expert annotations in dental imaging present a significant challenge for the development of AI in dentistry. DINOv3, a state-of-the-art, self-supervised vision foundation model pre-trained on 1.7 billion images, offers a promising pathway to mitigate this issue. However, its reliability when transferred to the dental domain, with its unique imaging characteristics and clinical subtleties, remains unclear. To address this, we introduce DinoDental, a unified benchmark designed to systematically evaluate whether DINOv3 can serve as a reliable, off-the-shelf encoder for comprehensive dental image analysis without requiring domain-specific pre-training. Constructed from multiple public datasets, DinoDental covers a wide range of tasks, including classification, detection, and instance segmentation on both panoramic radiographs and intraoral photographs. We further analyze the model's transfer performance by scaling its size and input resolution, and by comparing different adaptation strategies, including frozen features, full fine-tuning, and the parameter-efficient Low-Rank Adaptation (LoRA) method. Our experiments show that DINOv3 can serve as a strong unified encoder for dental image analysis across both panoramic radiographs and intraoral photographs, remaining competitive across tasks while showing particularly clear advantages for intraoral image understanding and boundary-sensitive dense prediction. Collectively, DinoDental provides a systematic framework for comprehensively evaluating DINOv3 in dental analysis, establishing a foundational benchmark to guide efficient and effective model selection and adaptation for the dental AI community.

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Published: 2026-03-30 11:23:48

Authors: Henning Bahl, Johannes Braathen, Martin Gabelmann, Kateryna Radchenko, Georg Weiglein

Categories: hep-ph

Abstract:
A central objective of future collider experiments is to probe the structure of the Higgs potential, which requires access to trilinear scalar couplings, in particular the self-coupling of the observed Higgs boson. While this coupling is fixed in the Standard Model (SM), it can receive sizable modifications in many Beyond the SM (BSM) scenarios, often connected to solutions of open problems such as the origin of the matter-antimatter asymmetry of the Universe. In theories with extended scalar sectors, radiative corrections involving additional scalar states can significantly affect both the Higgs self-coupling and other trilinear scalar interactions, with important consequences for predictions of physical observables. Precise theoretical calculations are therefore essential for the interpretation of precision Higgs measurements and for identifying indirect signatures of new physics. This contribution presents the latest version of the public tool anyBSM, which provides automated calculations of all trilinear scalar couplings at full one-loop order in arbitrary renormalisable theories, including full momentum dependence and flexible renormalisation-scheme choices. In addition, the new module anyHH for di-Higgs production in gluon fusion is discussed in several exemplary BSM models, including scenarios with multiple resonances.

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Published: 2026-03-30 11:08:51

Authors: Mattia D'Urso, Yuxi Hu, Christian Sormann, Mattia Rossi, Friedrich Fraundorfer

Categories: cs.CV

Abstract:
Despite the growing need for data of more and more sophisticated 3D reconstruction pipelines, we can still observe a scarcity of suitable public datasets. Existing 3D datasets are either low resolution, limited to a small amount of scenes, based on images of varying quality because retrieved from the internet, or limited to specific capturing scenarios. Motivated by this lack of suitable 3D datasets, we captured TerraSky3D, a high-resolution large-scale 3D reconstruction dataset comprising 50,000 images divided into 150 ground, aerial, and mixed scenes. The dataset focuses on European landmarks and comes with curated calibration data, camera poses, and depth maps. TerraSky3D tries to answer the need for challenging dataset that can be used to train and evaluate 3D reconstruction-related pipelines.

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