Multi-agent systems under partial observation often struggle to maintain safety because each agent's locally chosen action does not, in general, determine the resulting joint action. Shielding addresses this by filtering actions based on the current state, but most existing techniques either assume access to a shared centralised global state or employ memoryless local filters that cannot consider interaction history. We introduce a shield process algebra with guarded choice and recursion for specifying safe global behaviour in communication-free Dec-POMDP settings. From a shield process, we compile a process automaton, then a global Mealy machine as a safe joint-action filter, and finally project it to local Mealy machines whose states are belief-style subsets of the global Mealy machine states consistent with each agent's observations, and which output per-agent safe action sets. We implement the pipeline in Rust and integrate PRISM, the Probabilistic Symbolic Model Checker, to compute best- and worst-case safety probabilities independently of the agents' policies. A multi-agent path-finding case study demonstrates how different shield processes substantially reduce collisions compared to the unshielded baseline while exhibiting varying levels of expressiveness and conservatism.

We present an asynchronous calculus for multiparty sessions with mixed choice, which extends the Simple MultiParty Session framework in order to support nondeterministic choices with both input and output prefixes. Global types -- equipped with a coinductively defined labelled transition system -- form the basis of a type system that exploits the key notion of coherence of communication label sets. Roughly, a coherent set contains either all the communications enabled in the session, or all the actions currently exhibited by a participant, provided that at least one input is enabled for each expected sender. Our approach to the typing of multiparty sessions is orthogonal to the well-established, projection-based approaches of the MultiParty Session Type framework. We prove fundamental theorems for typable multiparty sessions, including Subject Reduction and Session Fidelity. These properties imply that typable sessions are both Lock-Free and Orphan-Message-Free. We also investigate the Eventual Reception property for an extension of the type system. Some examples demonstrate the expressiveness of mixed choice in asynchronous multiparty protocols and the effectiveness of the proposed type discipline.

Secure communications with quantum key distribution over fiber-optic links is one of the few recognized applications of quantum physics at the level of individual quanta -- single C-band photons. Currently, the widely used sources of such photons are highly attenuated laser pulses, featured by a low probability of single photon occurrence. Here, we present an efficient source with an InAs/GaAs quantum dot on a metamorphic buffer layer inside a micropillar-shaped microcavity. The key innovation is the use of different semiconductor and dielectric materials to form the lower (GaAs/AlGaAs) and upper (Si/SiO$_2$) Bragg reflectors. Compatibility of these materials in a monolithic source is achieved by depositing a small amount of Si/SiO$_2$ pairs on an incomplete micropillar made from a coherent heterostructure grown by molecular beam epitaxy. This design enables resonant excitation with $π$-pulses and generation of polarized photons with a record-breaking end-to-end efficiency of 11%.

Correct-by-design synthesis provides a principled framework for establishing formal safety guarantees for stochastic multi-agent systems (MAS). However, conventional approaches based on finite abstractions often incur prohibitive computational costs as the number of agents and the complexity of temporal logic specifications increase. In this work, we study homogeneous stochastic MAS under counting linear temporal logic (cLTL) specifications, and show that the corresponding satisfaction probability admits a structured tensor decomposition via leveraging deterministic finite automata (DFA). Building on this structure, we develop a dual-tree-based value iteration framework that reduces redundant computation in the process of dynamic programming. Numerical results demonstrate the proposed approach's effectiveness and scalability for complex specifications and large-scale MAS.

We conducted a comprehensive and comparative muon-spin relaxation and rotation ($μ$SR) investigation on two fcc-lattice metallic compounds, HoCdCu$_4$ ($T_\mathrm{N}\approx 8$ K) and HoInCu$_4$ ($T_\mathrm{N}\approx 0.76$ K), to elucidate the nature of their magnetic ground states and the role of frustration in stabilizing them. Our $μ$SR results reveal that, in contrast to HoCdCu$_4$, strong magnetic frustration exists in HoInCu$_4$. Notably, in HoInCu$_{4}$, only 30% of the Ho-moments participate in the static magnetic ordering below $T_\mathrm{N}$, while the remaining 70% of the Ho-moments exhibit dynamic correlations and persistent spin dynamics down to 0.3 K, resembling a quantum spin-liquid (QSL) behavior. By contrast, in HoCdCu$_{4}$, all the Ho-moments contribute to the magnetic order below $T_\mathrm{N}$. Furthermore, in HoInCu$_{4}$, the temperature dependence of the relaxation rate indicates the presence of quantum critical fluctuations in the paramagnetic state near $T_\mathrm{N}$, suggesting the proximity to a quantum critical point (QCP). These observations suggest that the ground state of HoInCu$_{4}$ is a proximate quantum spin liquid (PQSL), a state that has not been reported before in frustrated metallic systems. Our $μ$SR findings are further corroborated by recent inelastic neutron results on HoInCu$_4$, which show similarities to other insulating PQSL candidates, thus reinforcing our conclusions.

Issue resolution aims to automatically generate patches from given issue descriptions and has attracted significant attention with the rapid advancement of large language models (LLMs). However, due to the complexity of software issues and codebases, LLM-generated patches often fail to resolve corresponding issues. Although various advanced techniques have been proposed with carefully designed tools and workflows, they typically treat issue descriptions as direct inputs and largely overlook their quality (e.g., missing critical context or containing ambiguous information), which hinders LLMs from accurate understanding and resolution. To address this limitation, we draw on principles from software requirements engineering and propose REAgent, a requirement-driven LLM agent framework that introduces issue-oriented requirements as structured task specifications to better guide patch generation. Specifically, REAgent automatically constructs structured and information-rich issue-oriented requirements, identifies low-quality requirements, and iteratively refines them to improve patch correctness. We conduct comprehensive experiments on three widely used benchmarks using two advanced LLMs, comparing against five representative or state-of-the-art baselines. The results demonstrate that REAgent consistently outperforms all baselines, achieving an average improvement of 17.40% in terms of the number of successfully-resolved issues (% Resolved).

We present \textbf{EGPF} (Equilibrium-Guided Personalization Framework), a mathematically rigorous architecture unifying Bayesian game theory, category theory, information theory, and generative AI for hyper-personalized physician engagement in the pharmaceutical domain. Our framework models the pharma--physician interaction as an incomplete-information Bayesian game where physician behavioral types are inferred via functorial mappings from observational categories, equilibrium strategies guide content generation through large language models (LLMs), and information-theoretic feedback loops ensure adaptive recalibration. We formalize behavior composition through category-theoretic functors, natural transformations, and monoidal structures, enabling modular, composable physician archetypes that respect structural invariants under domain shift. We introduce a novel \textit{Rate-Distortion Equilibrium} (RDE) criterion that bounds the personalization--privacy tradeoff, an \textit{Evolutionary Game Dynamics} layer for population-level behavior modeling, a \textit{Mechanism Design} module for incentive-compatible engagement, and a \textit{Sheaf-Theoretic} extension for multi-scale behavioral consistency. We prove convergence of our iterative belief-update mechanism at rate $O(\frac{K\log K}{t \cdot C_{\min}})$ and establish finite-sample regret bounds. Extensive experiments on synthetic pharma datasets and a real-world HCP engagement pilot demonstrate a 34\% improvement in engagement prediction (AUC) and 28\% lift in content relevance scores compared to state-of-the-art methods.

Probabilistic hyperproperties describe probabilistic relations between multiple sets of executions in a stochastic system. Prominent examples include information-theoretic characterizations of security and privacy policies. However, model checking for existing probabilistic hyperlogics, such as HyperPCTL and PHL, is undecidable in Markov decision processes (MDPs). In this paper, we study an underexplored problem: the verification of fragments of probabilistic hyperproperties that relate the probabilities of different events to each other, possibly across independent executions of an MDP. Representative verification questions include: Can two different target states be reached from the same initial state with the same probability? (different events), Can a given target state be reached from two different initial states with the same probability? (same event, independent executions), and natural combinations of these forms. Besides reachability, our relational probabilistic properties cover safety, Büchi, and coBüchi objectives. They can also be combined conjunctively, thereby generalizing standard multi-objective MDP properties. We provide efficient algorithms for relevant classes of relational properties, while proving computational hardness and completeness results for others. An implementation of our approach outperforms solvers for more general probabilistic hyperlogics by orders of magnitude on the subset of their benchmarks that lies within our fragment.

We investigate the impact of quantum gravitational memory-burden effects on high-energy neutrino signals from evaporating primordial black holes and the resulting constraints from IceCube observations. Treating the backreaction as an energy-dependent deformation of the Hawking emission spectrum, we show that the high-energy tail is suppressed while the infrared behaviour remains unchanged. We derive analytically that this modification reduces the total luminosity and extends the evaporation lifetime by a mass-independent factor determined solely by the suppression parameter. Using an effective treatment of cosmological redshift, we compute the diffuse neutrino flux from a primordial black hole population and compare it with the observed astrophysical neutrino spectrum to constrain the primordial black hole dark matter fraction. We find that the suppression onset lies within the IceCube sensitivity window, leading to a direct reduction of the observable signal and a systematic weakening of the inferred bounds. Our results provide a controlled phenomenological framework for assessing the impact of quantum gravitational corrections on neutrino probes of primordial black hole evaporation.

Forbush decreases (FDs) are short-term reductions in galactic cosmic ray flux caused by interplanetary disturbances. During some interplanetary coronal mass ejection (ICME) events, neutron monitor (NM) data also contain variations produced by geomagnetic storms. Earlier studies emphasized apparent effects near 10~GV, but storm-time changes in geomagnetic cutoff rigidity can either increase or decrease the ground-level count rate. Using a recently published hourly proton flux reconstructed from NM data for May 2011 through October 2019, the interval covered by the published AMS daily proton fluxes, we show that these localized anomalies can extend to lower rigidities and reach 1~GV in some events. Such effects can bias the rigidity dependence inferred from NM-based hourly proton spectra during disturbed intervals. Because AMS measures proton rigidity directly in space, its daily proton spectrum is not affected by cutoff variations at ground stations and provides a stable reference. We therefore use AMS to constrain corrections for selected events. The correction removes localized anomalies while preserving the broader FD evolution, and for a representative ICME event it brings the corrected daily averages closer to the AMS measurements. Our results show that short-timescale cosmic ray variability during FDs reflects both heliospheric modulation and storm-time changes in geomagnetic shielding.

In the EU project MARE, a novel plane was proposed and used in combination with intent-based networking (IBN), allowing the operator to focus on what, rather than on how. Recently, LLMs have been successfully employed to translate the high-level intents into low-level actions. The open challenge is to understand how IBN can be effectively enhanced with LLM and the emerging agentic AI for security purposes. Enhancing IBN with an agentic AI paradigm introduces significant challenges that existing solutions do not fully address. This paper proposes an enhanced IBN framework with a strong security focus toward agentic AI. We address the architectural and security requirements for a multi-agent intent-based system (IBS) architecture, including a multi-domain IBN. We propose a hierarchical multi-agent and multi-vendor architecture that can also be applied more broadly in 6G architectures and beyond, beyond the security architecture proposed in MARE. The architecture incorporates an interactive intent-processing pipeline using LLMs, and it also allows the IBS to connect to external security knowledge bases, such as MITRE ATT\&CK, MITRE FiGHT, and NIST.

Dynamic Metasurface Antennas (DMAs) have been recently proposed as a cost- and energy-efficient front-end solution for eXtremely Large (XL) antenna array systems, supporting scalable Analog and Digital (A/D) beamforming while using a reduced number of Radio-Frequency (RF) chains. This array architecture is commonly realized as partially connected hybrid A/D beamformers, in which non-overlapping subarrays are linked to separate RF chains, each attached to a waveguide hosting multiple metamaterials. In this work, we study uplink multi-user communications where each RF chain of an XL DMA receiver is equipped with a $b$-bit resolution Analog-to-Digital Converter (ADC). We cast a Mean Squared Error (MSE) minimization problem for the design of the hybrid A/D combiner aimed at multi-user symbol detection, which is intrinsically non-convex due to the structural constraints imposed by the DMA hardware. By exploiting the Bussgang decomposition and a tractable modeling framework, we propose an efficient joint design of the hybrid A/D combining parameters. Our numerical evaluations showcase that XL DMA receivers can perform highly accurate multi-user symbol detection, revealing attractive trade-offs between hardware complexity and MSE performance.

In this article, we conduct a comprehensive study of weighted Sobolev spaces with logarithmic weights, orginially introduced by Calanchi and Ruf to analyze the sharp exponential integrability of radial functions belonging to these spaces. By exploring the connection between these logarithmically weighted energies and the Leray energy, we expand the framework to incorporate non-radial functions. More precisely, we establish optimal exponential integrability for general functions in the spirit of optimal Leray-Trudinger inequalities established by Di Blasio, Pisante and Psaradakis. Furthermore, we prove sharp versions of these inequalities when restricted to radial functions. Notably, the inequalities presented here are fundamentally different in nature from those of Calanchi and Ruf, for which the non-radial extension fails to hold.

This paper considers a Fluid Antenna (FA) system comprising a single-antenna transmitter that communicates with a receiver equipped with an FA array with $N$ ports. The transmitter is assumed to deploy any of the modulation schemes: \textit{i}) two-sided $M$-ary amplitude-shift keying, \textit{ii}) $M$-ary phase-shift keying, iii) $M$-ary quadrature-amplitude modulation, and \textit{iv}) binary frequency-shift keying, the channels between its antenna and the receiver ports are subjected to Rayleigh fading, and the receiver chooses the best $K$ out of its $N$ ports for symbol detection. Considering that the receiver combines the signals from the best $K$ ports using maximal-ratio combining, the optimal reception structures for all the considered signaling schemes are obtained. We also present novel exact closed-form expressions for the respective symbol error probabilities (SEPs) of the FA system, as well as asymptotic approximations valid at high signal-to-noise ratios. The presented analysis is corroborated through comparisons with simulation results, showcasing the critical role of various system parameters on the SEP performance.

We present a method to construct irreducible symplectic varieties by studying terminalisations of quotient of hyper-Kähler manifolds by non-natural group actions. In particular, we construct irreducible symplectic varieties of dimension $4$ with $b_2 = 4$ and non-quotient singularities: this provides explicit examples of ISVs for which a global Torelli theorem is not known to hold.

Active matter classifies systems consisting of self-propelled units which convert the energy stored locally or extracted from their environment into directed motion. It has recently attracted considerable attention due to rich new physics it displays and potential applications in various fields including materials science. Active matter found in nature is inherently complex, so model systems are of interest where the main relevant features can be isolated and studied in laboratory experiments. An interesting instance of active matter is a suspension of active particles (e.g., the so-called Janus particles, where the two halves have different properties) in a gas discharge plasma. Such complex plasmas with active particles are excellent model systems which can enhance our understanding of natural active matter systems not easily amenable to experiment. In the present experimental study, micrometer-size plastic microspheres with thin gold coating on one side were suspended as a single layer in the plasma sheath of a radio-frequency discharge in argon and driven by a combination of laser-induced photophoretic force and asymmetric ion drag force. Enhanced particle activity in this highly driven, inertial active-matter system leads to collective particle dynamics characterized by extended self-similarity of the velocity field, intermittency, and the emergence of a direct energy cascade with non-universal scaling exponent.

We establish an asymptotic formula for the logarithmic mean value of a 1-bounded multiplicative function that is sharp in many cases of interest. We derive from it a variety of applications, making progress on several old problems. As a first application, we show that if $f$ is a completely multiplicative function taking values in $[-1,1]$ then there is a constant $c > 0$ such that for every $x \geq 3$, $$ L_f(x) := \sum_{n \leq x} \frac{f(n)}{n} > -\frac{c}{(\log x)^{1-2/π}}, $$ thus significantly improving on a 20-year-old result of Granville and Soundararajan. We also show that the exponent of $\log x$ in this result can be improved to $-1+o(1)$, as long as $f$ does not ``behave like'' the Liouville function $λ$ in a precise sense. As a second application, we show that for a Rademacher random completely multiplicative function $\mathbf{f}$, the probability that $L_{\mathbf{f}}(x)$ is negative is $O(\exp(-x^c))$ for some $c \in (0,1)$, thus establishing a previously conjectured bound. Finally, we obtain a converse theorem for small absolute values $|L_f(x)|$, and construct examples $f$ that show that it is (essentially) best possible.

Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for this task are capable of reliably capturing detailed surface material characteristics. By analyzing the reflected RF signal from a mmWave radar sensor, it is possible to extract information about the reflective material and its composition from a certain surface. We introduce a mmWave MIMO FMCW radar-based surface material classifier SMCNet, employing a complex-valued Convolutional Neural Network (CNN) and complex radar IQ signal input for classifying indoor surface materials. While current radar-based material estimation approaches rely on a fixed sensing distance and constrained setups, our approach incorporates a setup with multiple sensing distances. We trained SMCNet using data from three distinct distances and subsequently tested it on these distances, as well as on two more unseen distances. We reached an overall accuracy of 99.12-99.53 % on our test set. Notably, range FFT pre-processing improved accuracy on unknown distances from 25.25 % to 58.81 % without re-training.

Local polynomial smoothing is a widespread technique in data analysis, and Savitzky-Golay (SG) filters are one of its most well-known realizations. In real settings, the effectiveness of SG filtering depends critically on proper tuning of its parameters, constrained in turn by repeated polynomial fitting over large data windows and for varying polynomial degrees. Standard implementations based on monomial bases and Vandermonde matrix formulations are known to suffer from ill-conditioning and unfavorable scaling as the problem size increases. In this work, we present a fast and numerically stable method for computing polynomial fitting and differentiation matrices by reformulating the problem in terms of discrete orthogonal (Chebyshev) polynomials. Exploiting their recursive structure and the intrinsic symmetry properties of the resulting matrices, we derive two algorithms designed to reduce computational overhead. Both methods significantly reduce memory usage and improve scalability with respect to the polynomial degree and window length. A discussion of the performance demonstrates that the proposed algorithms achieve orders-of-magnitude improvements in numerical accuracy compared to standard matrix multiplication, while also providing potential gains in execution time for large-scale problems. These features make the approach particularly well suited for applications requiring repeated local polynomial fits, such as the optimization of SG filters in high-resolution spectral analyses, including axion dark matter searches and the ALPHA haloscope.

Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sensors' ability to accurately perceive and image the indoor environment. Nevertheless, this sensor offers distinct advantages over camera and LiDAR sensors. This encompasses the estimation of object reflectivity, known as radar cross-section (RCS), and the ability to penetrate through objects that are thin or have low reflectivity. This results in a 'through-the-wall' sensing capability. Due to the aforementioned disadvantages, most research in the field of imaging radar tends to exclude indoor areas. We introduce a machine learning-based mmWave MIMO FMCW imaging radar object classifier designed to identify small, hand-sized objects in indoor settings, utilizing only radar IQ samples as input. This system achieves 97-99 % accuracy on our test set and maintains approximately 50 % accuracy even under challenging conditions, such as increased background noise and occlusion of sample objects, without the need for adjusting training or pre-processing. This demonstrates the robustness of our approach and offers insights into what needs to be improved in the future to achieve generalization and very high accuracy even in the presence of significant indoor perturbations.

The Mostar index of a connected graph \(G\) is defined as \[ Mo(G)=\sum_{uv\in E(G)}\bigl|n_u(uv)-n_v(uv)\bigr|, \] where for an edge \(e=uv\), \(n_u(e)\) denotes the number of vertices of \(G\) that are closer to \(u\) than to \(v\). In this paper, we determine the maximum possible Mostar index among all connected graphs of order \(n\) with exactly \(k\) cut edges, where \(1\le k\le n-1\). We prove that the maximum value is given by \(k(n-2)+(n-k-1)k\), and the unique extremal graph is \(K_{n-k}^k\) (a complete graph on \(n-k\) vertices with \(k\) pendant edges attached to a single vertex). We also establish a sharp lower bound and characterise the extremal graphs for the minimum value. Furthermore, we extend the results to graphs with a given cyclomatic number and a given number of cut edges. Our findings complete the extremal characterisation of the Mostar index for this fundamental graph class.

CVTree is an alignment-free methodology for inferring species phylogeny and taxonomy. This method allows for the efficient and accurate resolution of evolutionary relationships among large numbers of species based on whole-genome sequence data. Since 2004, we have been continuously providing CVTree web services. Recently, the server has undergone a significant upgrade, culminating in the release of the WebCVTree4 platform. This upgrade encompasses a comprehensive update of the inbuilt genomic database. Concurrently, the core algorithm has been optimized to support online phylogenetic reconstruction for tens of thousands of species, thereby facilitating the construction of genome-based trees of life. Moreover, we have developed a novel algorithm for comparing phylogenetic trees with established taxonomic systems. This algorithm allows for rapid tree rooting, taxonomic annotation, and topology comparison. Through an interactive web-based visualization tool, users can dynamically adjust tree layouts and export high-quality phylogenetic tree figures. This functionality provides robust support for comparative analysis between CVTree-generated phylogeny and taxonomy. As genome sequencing costs continue to decline, research into microbial evolution and the revision of taxonomic frameworks will increasingly rely on whole-genome data. WebCVTree4 will serve as an efficient web-based platform to support studies in microbial phylogenetics and taxonomy, accessible at https://cvtree.online/.

Federated fine-tuning enables privacy-preserving LLM adaptation but faces a critical bottleneck: the disparity between LLMs' high memory demands and edge devices' limited capacity. To break the memory barrier, we propose Chain Federated Fine-Tuning (ChainFed), an innovative paradigm that forgoes end-to-end updates in favor of a sequential, layer-by-layer manner. It first trains the initial adapter to convergence, freezes its weights, and then proceeds to the next. This iterative train-and-freeze process forms an optimization chain, gradually enhancing the model's task-specific proficiency. ChainFed further integrates three core techniques: 1) Dynamic Layer Co-Tuning to bridge semantic gaps between sequentially tuned layers and facilitate information flow; 2) Globally Perceptive Optimization to endow each adapter with foresight beyond its local objective; 3) Function-Oriented Adaptive Tuning to automatically identify the optimal fine-tuning starting point. Extensive experiments on multiple benchmarks demonstrate the superiority of ChainFed over existing methods, boosting average accuracy by up to 46.46\%.

Motivated by resource defense models in networks, such as protecting territories with varying legion strengths, let $k \geq 2$ be an integer. Roman $k$-domination and strong Roman $k$-domination generalize Roman, double Roman, Italian, and double Italian domination to arbitrary number of legions. The main goal of this note is establishing sharp upper bounds for the Roman and strong Roman $k$-domination numbers of connected graphs. These bounds unify and extend prior results for $k=2$ and $k=3$. We also precisely characterize the graphs achieving these bounds.

The paper studies a time-nonlocal multiphysics finite element method with Crank-Nicolson scheme for poroelasticity model with secondary consolidation. For the case where the physical parameters $λ,λ^*$ and $c_0$ are all finite positive constants, by introducing two auxiliary variables-the fluid content $η$ and the generalized pressure $ξ$ -- the original strongly coupled poroelasticity model is reformulated into a generalized Stokes equation with time integral terms and a diffusion equation. The reformulated model not only reveals the underlying multiphysics processes in the original model, but also exhibits time-nonlocal characteristics. A time-nonlocal multiphysics finite element method is designed for the reformulated model: the spatial discretization employs high order Taylor-Hood mixed finite element method, and the temporal discretization adopts the Crank-Nicolson scheme. The time integral terms are approximated using the composite trapezoidal rule, and the integral terms $J_ξ^n$ and $J_η^n$ are introduced for real-time updates, which not only avoids repeated calculations and improves efficiency, but also maintains second-order temporal accuracy. The existence and uniqueness of weak solutions for the reformulated model are proved via energy estimate methods, the stability of the fully discrete time-nonlocal multiphysics finite element method is established, and optimal-order error estimates are derived using projection operator techniques. Finally, numerical example verified the theoretical results and compared the long-time convergence of the Crank-Nicolson scheme and the backward Euler scheme.

Coordinating robotic swarms in dynamic and communication-constrained environments remains a fundamental challenge for collective intelligence. This paper presents a novel framework for event-triggered organization, designed to achieve highly efficient and adaptive task allocation in a heterogeneous robotic swarm. Our approach is based on an adaptive consensus mechanism where communication for task negotiation is initiated only in response to significant events, eliminating unnecessary interactions. Furthermore, the swarm self-regulates its coordination pace based on the level of environmental conflict, and individual agent resilience is managed through a robust execution model based on Behavior Trees. This integrated architecture results in a collective system that is not only effective but also remarkably efficient and adaptive. We validate our framework through extensive simulations, benchmarking its performance against a range of coordination strategies. These include a non-communicating reactive behavior, a simple information-sharing protocol, the baseline Consensus-Based Bundle Algorithm (CBBA), and a periodic CBBA variant integrated within a Behavior Tree architecture. Furthermore, our approach is compared with Clustering-CBBA (C-CBBA), a state-of-the-art algorithm recognized for communication-efficient task management in heterogeneous clusters. Experimental results demonstrate that the proposed method significantly reduces network overhead when compared to communication-heavy strategies. Moreover, it maintains top-tier mission effectiveness regarding the number of tasks completed, showcasing high efficiency and practicality. The framework also exhibits significant resilience to both action execution and permanent agent failures, highlighting the effectiveness of our event-triggered model for designing adaptive and resource-efficient robotic swarms for complex scenarios.

We present the first observational test of the hybrid ring strategy, a general coordinated signaling scheme proposed by Seto (2025), which provides a practical Schelling-point realization for interstellar signaling. We use the exceptionally bright GRB 221009A as the anchoring flash for the scheme, together with the accurately measured distance to the Galactic center. This combination provides a high-precision relation linking sky position to a tightly constrained arrival-time window. TESS observed the region around the GRB nearly continuously for ~50 days in 2024, providing survey light curves that enable a direct test of this scheme with sharply predicted arrival-time windows of $\sim$3.4 days. Among 58 carefully selected stars, we identify two that show noticeable single-time-bin brightenings inside their predicted windows (where each time bin corresponds to a 200 s integrated TESS exposure). In both cases the brightenings coincide with excursions in at least one nearby star and are therefore most consistent with instrumental origins. This test demonstrates that the hybrid ring strategy is practical with existing survey data and could serve as a promising basis for future technosignature searches.

It is shown that algebraic techniques based on the Lie algebra so(4,2) provide efficient tools for evaluating Lamb shifts and radiative decay rates for hydrogenic energy eigenstates as they systematically exploit the intrinsic symmetry of the hydrogenic Hamiltonian. As a main result in lowest order perturbation theory with respect to the fine-structure constant integral representations are derived for the complex-valued energy shifts of hydrogen-like ions from which Lamb shifts and radiative decay rates can be evaluated in a unified way, thus generalizing a recently discussed algebraic approach of Maclay. In order to exemplify the usefulness of this algebraic approach numerical results are presented for Lamb shifts and radiative decay rates which transcend the dipole approximation and contain the dipole approximation as a limiting case.

Query rewriting, the process of transforming queries into semantically equivalent yet more efficient variants, is crucial for database optimization. Existing solutions predominantly rely on either rule-based heuristics or Large Language Models (LLMs). However, traditional rule-based methods lack adaptability, while LLM-based approaches incur prohibitive inference costs and privacy risks. In contrast, Small Language Models (SLMs) present a compelling middle ground, potentially offering both flexibility and efficiency. However, the development of such compact models is severely bottlenecked by the scarcity of high-quality, domain-specific training data. To bridge this gap, we introduce LASER, a data-centric framework designed to empower small models for robust SQL optimization. First, to address the scarcity of existing benchmarks and the limited optimization headroom of generic synthetic queries, we construct SQL-MCTS, a large-scale corpus of complex slow queries. We employ an MCTS-based hybrid expansion strategy that combines rule-guided anti-patterns with LLM mutations to evolve structurally expressive seeds into execution-verified slow variants. Second, to enable the model to autonomously discover latency-aware rewriting patterns, we propose SQL-GRPO, a specialized alignment strategy adapted from Group Relative Policy Optimization. By integrating Anchored Group Advantage to refine advantage estimation and Complexity-Adaptive Dynamic Rollout to efficiently allocate exploration budgets, this approach effectively empowers compact models to master execution-based optimization logic. Implemented on Qwen3 models, LASER significantly outperforms rule-based systems and LLMs in execution efficiency, while exhibiting robust zero-shot transferability with minimal overhead.

In this article, we consider extended tame persistence commutative differential graded algebras (CDGAs) associated with relative Sullivan algebras. In particular, if the relative Sullivan algebra is a model for a map between spaces, then the persistence CDGA is isomorphic to the persistence object obtained by a Postnikov tower for the map with the polynomial de Rham functor in the homotopy category of extended tame persistence CDGAs. Moreover, the interleaving distance in the homotopy category (IHC) in the sense of Lanari and Scoccola enables us to introduce a pseudodistance on the homotopy set of maps via the persistence CDGA models for maps. In contrast to persistence cochain complexes, the IHC of persistence CDGAs does not coincide with the cohomology interleaving distance in general. Due to the reason, we also discuss formalities of a persistence CDGA with interleavings. Computational examples of the pseudodistances between maps are showcased.