Dark-matter haloes do not form in isolation but within the surrounding cosmic web. By the time a halo begins to collapse, its larger-scale environment has typically collapsed along two axes, forming filaments that channel anisotropic infall toward the halo. In this work, we derive from first principles the characteristic Lagrangian scale ratio at which such an anisotropic tidal field most strongly influences halo formation. Specifically, we identify the inflection point of the conditional probability that the tidal field, smoothed on a scale Rsd, undergoes two-dimensional compression, given the presence of a density peak of rarity nu on a smaller scale Rpk. For a standard LambdaCDM cosmology, we find (Rsd/Rpk)infl = 2.2 + (nu-2.5) for Rpk corresponding to a tophat filter of 8Mpc/h. This result implies that the anisotropic tidal influence on a collapsing halo typically extends to 2-3 times the size of its Lagrangian patch. Recast as a function of formation redshift z, the characteristic filament scale around 2.5 sigma peaks can be approximated by Rsd(z) = 31 /(2+(1+z)**2)Mpc/h. We provide practical scaling laws for selecting dynamically relevant smoothing scales in large-scale surveys and for setting initial patch sizes in high-resolution zoom simulations.

Creating scalable and believable game societies requires balancing authorial control with computational cost. Existing scripted NPC systems scale efficiently but are often rigid, whereas fully LLM-driven agents can produce richer social behavior at a much higher runtime cost. We present CASCADE, a three-layer architecture for low-cost, controllable social coordination in sandbox-style game worlds. A Macro State Director (Level 1) maintains discrete-time world-state variables and macro-level causal updates, while a modular Coordination Hub decomposes state changes through domain-specific components (e.g., professional and social coordination) and routes the resulting directives to tag-defined groups. Then Tag-Driven NPCs (Level 3) execute responses through behavior trees and local state/utility functions, invoking large language models only for on-demand player-facing interactions. We evaluate CASCADE through multiple micro-scenario prototypes and trace-based analysis, showing how a shared macro event can produce differentiated yet logically constrained NPC behaviors without per-agent prompting in the main simulation loop. CASCADE provides a modular foundation for scalable social simulation and future open-world authoring tools.

Precision tests of the Standard Model using $β$ decay have always relied on a careful choice of transition to minimize residual nuclear structure uncertainties. Following breakthroughs in nucleon-level radiative corrections in the last decade, however, corrections due to nuclear structure are once more a limiting factor in several scenarios. Progress in ab initio nuclear theory provides a path forward, but common recoil-order approximations in traditional formalisms often go unnoticed. Here, we critically examine their origin and address recently resolved and identify open questions.

Edge-localized-mode (ELM) filaments are crucial for cross-field transport at the tokamak edge; yet, their dynamics are often analyzed using the cold-ion approximation, despite experimental data indicating that Ti~Te . This study employs a normalized three-dimensional fluid model to investigate the influence of finite ion temperature on the dynamics of unidirectional current-carrying ELM-like filaments. We demonstrate that increasing ion temperature substantially alters filament propagation and interaction, resulting in a delay of filament merging despite an increase in total kinetic energy due to a stronger pressure-gradient drive. The examination of single-filament dynamics indicates that finite ion temperature generates asymmetric potential structures, strong poloidal flows, and persistent rotational motion, which channel kinetic energy from radial propagation into vortical dynamics. A comprehensive examination of the ion-to-electron temperature ratio reveals a distinct transition from radially dominated to rotation-dominated behavior as ion temperature increases. These results provide a unified physical explanation for reduced radial transport and delayed merging in the warm-ion domain, emphasizing the necessity of incorporating ion temperature effects in the modeling of ELM filament dynamics and edge plasma transport.

A growing portion of operators workload is dedicated to Tertiary Voltage Control (TVC), namely the regulation of voltages by means of adjusting a series of setpoints and connection status. TVC may be framed as a Mixed Integer Non Linear Program, but state-of-the-art optimization methods scale poorly to large systems, making them impractical for real-scale and real-time decision support. Observing that TVC does not require any optimality guarantee, we frame it as an Amortized Optimization problem, addressed by the self-supervised training of a Graph Neural Network (GNN) to minimize voltage violations. As a first step, we consider the specific use case of post-processing the forecasting pipeline used by the French TSO, where the trained GNN would serve as a TVC proxy. After being trained on one year of full-scale HV-EHV French power grid day-ahead forecasts, our model manages to significantly reduce the average number of voltage violations.

Reasoning about consistency models for replicated data systems is a challenging task that requires a deep understanding of both the consistency models themselves and a large part of human inputs in mechanized verification approaches. In this work, we introduce an approach to reasoning about consistency models for replicated data systems. We introduce HistMSO, a monadic second-order logic (MSO) for histories and abstract executions, the formal models of executions of replicated data systems introduced by Burckhardt. We show that HistMSO can express 39 out of 42 consistency models from Viotti and Vukolic hierarchy. Moreover, we develop a method for reducing HistMSO satisfiability and model-checking to the same problems for MSO over words. While doing this, we leverage the MONA tool for automated reasoning on consistency models.

We embed general boundary value problems for the time-harmonic Maxwell equations into the elliptic boundary value theory. This is achieved by introducing two new scalar functions to the electromagnetic field and imposing additional boundary conditions, after which the problem becomes elliptic. The results are applied to general problems for Maxwell's equations in bounded and unbounded domains, as well as to the transmission problem with inhomogeneities on the right-hand side of the equations and at all boundaries. Relations between the inhomogeneities of the elliptic problem are established that provide a one-to-one correspondence between the solutions of Maxwell's problem and the solutions of the elliptic boundary value problem.

Modern blockchain ecosystems comprise many heterogeneous networks, creating a growing need for interoperability. Cross-chain bridges provide the core infrastructure for this interoperability by enabling verifiable state transitions that move assets and liquidity across chains. While prior work has focused mainly on bridge design and security, the system-level and economic consequences of cross-chain liquidity interoperability remain less understood. We present a large-scale empirical measurement study of cross-chain interoperability using a dataset spanning 20 blockchains and 16 major bridge protocols from 2022 to 2025. We model the multi-chain ecosystem as a time-varying weighted hypergraph and introduce two complementary metrics. Structural interoperability captures connectivity created by deployed bridge infrastructure, reflecting bridge coverage and redundancy independent of user behavior. Active interoperability captures realized cross-chain usage, measured by normalized transfer activity. This decomposition separates infrastructure capacity from actual utilization and yields several findings. The cross-chain network evolves from a sparse hub-and-spoke structure into a denser multi-hub core led by EVM-compatible chains. Bridge expansion and chain growth are uneven: some chains achieve broad structural access but limited realized usage, whereas others concentrate activity through a small set of routes. Overall, interoperability provision and interoperability use diverge substantially, showing that connectivity alone does not imply economically meaningful integration. These results provide a measurement framework for understanding how cross-chain infrastructure reshapes blockchain market structure and liquidity organization.

The plethora of spectra of OB-type stars in observatory archives and the much larger numbers to come from the WEAVE and 4MOST spectroscopic facilities require efficient, but also accurate and precise methods for (semi)automatic quantitative analyses. Neural networks were used to emulate the spectra of single- and multi-star systems, trained on hybrid non-local thermodynamic equilibrium (non-LTE) models that cover a wide range of atmospheric parameters and chemical compositions. To derive the full set of stellar atmospheric parameters and uncertainties, a Markov chain Monte Carlo algorithm was implemented to fit high-resolution spectra within 3000A-10500A. The neural networks and fitting algorithm were bundled into a programme called Spectral Analysis Tool Using Restricted Neural networks (SATURN). In its current implementation, SATURN facilitates the emulation of synthetic spectra for spectral types O7 to B9, which differ only negligibly from computed models. SATURN was tested on a number of benchmark stars that have been studied before, including single OB stars and a detached eclipsing binary (DEB) system. Excellent agreement of atmospheric parameters and elemental abundances for up to ten metal species is found with respect to the data in the literature, often with reduced uncertainties. For DEB components, the uncertainties are larger, in particular for the fainter secondaries when only a single-epoch spectrum is considered. Uncertainties of elemental abundances are typically <0.10dex. Some first applications of SATURN for analyses of new targets are shown to demonstrate its capabilities, such as fast rotators, including HD149757 (Zeta Ophiuchi). Consistent results are also found at reduced spectral resolutions relevant for observations with WEAVE and 4MOST.

As modern analogue/mixed-signal design increasingly relies on optimization-in-the-loop flows, such as AI and LLM-based sizing agents that repeatedly invoke SPICE-efficient, accurate high-performance simulators have become an indispensable foundation for modern integrated circuit (IC) design. However, the computational cost of evaluating nonlinear models, particularly for BSIM models, remains a significant bottleneck. In standard parallelization approaches, devices such as transistors are easily distributed across processors. The subsequent stamping phase, where each device's contributions are added to the shared system matrix, often creates a bottleneck. Because multiple processor cores compete to update the same matrix elements simultaneously, the system is forced to process tasks one at a time to avoid errors. This paper introduces EEspice, an open-source circuit simulation framework whose modular architecture decouples device model evaluation into independently replaceable kernels, enabling a parallel stamping strategy that overcomes this bottleneck. It partitions MOSFET instances into independent color groups, which can be processed in parallel. Our results show that on a 64-core workstation, the proposed approach achieves up to 45x speedup over single-thread performance when conflicts are low. Our analysis also explores how performance depends on circuit topology.

In this article, we introduce and study the Quadratic Bin Packing Problem (QBPP), which generalizes the classical bin packing problem by introducing a fixed cost for each used bin and a pairwise cost (or profit) incurred whenever two items are packed together. Beyond its theoretical relevance, the QBPP is of practical interest due to its numerous real-world applications, mainly related to cluster analysis. To address the QBPP, we propose three compact mixed-integer linear programming (MILP) formulations, along with a set-partitioning formulation. For each compact model, we present an enhanced version with a strengthened continuous relaxation, while, for the set-partitioning formulation, we develop a tailored Branch-and-Price algorithm. Computational experiments on benchmark instances demonstrated that, while the enhanced compact formulations can be effectively solved by a standard MILP solver for small-sized instances, the Branch-and-Price approach delivered superior performance overall, especially on larger and more challenging instances.

Constructing $C^r$ conforming finite element spaces in any dimension is a long-standing problem. For general triangulations, this problem was recently addressed by Hu-Lin-Wu (2024), under certain conditions on supersmoothness and polynomial degree. In this paper, a first unified construction on the Alfeld split in any dimension is given, where the supersmoothness conditions and the polynomial degree requirement are relaxed.

This paper investigates the impact of carbon pricing under the EU Emissions Trading System (EU ETS) on the Italian electricity market, focusing on the carbon cost pass-through rate (CPTR) across market zones during Phases 3 and 4 (2016-2024). Using daily data, the study applies an econometric framework based on a linear regression model with autoregressive dynamics to estimate the extent to which carbon costs are reflected in wholesale electricity prices. It further incorporates robustness checks and quantile regression to assess how the CPTR varies across different fuel spread levels. The results show that carbon costs are positively and significantly transmitted to electricity prices, confirming the relevance of carbon pricing as a key market driver. However, pass-through is incomplete, with CPTR values consistently below 100%. At the national level, the CPTR remains relatively stable at around 30% across the two phases. Substantial heterogeneity emerges across market zones: pass-through increases in the North, Centre-North, and Sardinia during Phase 4, while it declines in the Centre-South and Sicily, reflecting differences in generation mix, carbon intensity, and market conditions. Overall, the findings highlight the importance of market zones factors in shaping the effectiveness of carbon pricing in electricity markets.

As AI systems increasingly take on instructional roles - providing feedback, guiding practice, evaluating work - a fundamental question emerges: does it matter to learners who they believe is on the other side? We investigated this using a three-condition experiment (N=148) in which participants completed a creative coding tutorial and received feedback generated by the same large language model, attributed to either an AI system (with instant or delayed delivery) or a human teaching assistant (with matched delayed delivery). This three-condition design separates the effect of source attribution from the confound of delivery timing, which prior studies have not controlled. Source attribution and timing had distinct effects on different outcomes: participants who believed the human attribution spent more time on task than those receiving equivalently timed AI-attributed feedback (d=0.61, p=.013, uncorrected), while the delivery delay independently increased output complexity without affecting time measures. An exploratory analysis revealed that 46% of participants in the human-attributed condition did not believe the attribution, and these participants showed worse outcomes than those receiving transparent AI feedback (code complexity d=0.77, p=.003; time on task d=0.70, p=.007). These findings suggest that believed human presence may carry motivational value, but that this value depends on credibility. For computing educators, transparent AI attribution may be the lower-risk default in contexts where human attribution would not be credible.

In this paper, we consider the academic department ranking system of Italy, which is based on a performance index named Indice Standardizzato di Performance Dipartimentale (ISPD). While critiques to the ISPD have been moved for its marked tendency to polarization, we here formalize a yet unexplored determinant of this phenomenon, that is, the presence of within-department homogeneity among the standardized scores used to build the index. We account for this intra-departmental correlation by modeling it as a function of departments' size. The proposed model, estimated via Maximum Likelihood, allows to build a fairer ranking procedure via the definition of a properly adjusted version of the ISPD. The estimation framework is also adapted to fit publicly available data, which are coarsened by rounding and/or left-truncated. To this end, a novel probability distribution termed Betoidal is introduced. Empirical evidence in favor of the proposed model is found in the 2017 and 2022 data. Moreover, a simulation study shows that the adjusted index significantly overcomes not only the original ISPD, but also other more data-demanding competing proposals.

We analyze the one-pass stochastic gradient descent dynamics of a two-layer neural network with quadratic activations in a teacher--student framework. In the high-dimensional regime, where the input dimension $N$ and the number of samples $M$ diverge at fixed ratio $α= M/N$, and for finite hidden widths $(p,p^*)$ of the student and teacher, respectively, we study the low-dimensional ordinary differential equations that govern the evolution of the student--teacher and student--student overlap matrices. We show that overparameterization ($p>p^*$) only modestly accelerates escape from a plateau of poor generalization by modifying the prefactor of the exponential decay of the loss. We then examine how unconstrained weight norms introduce a continuous rotational symmetry that results in a nontrivial manifold of zero-loss solutions for $p>1$. From this manifold the dynamics consistently selects the closest solution to the random initialization, as enforced by a conserved quantity in the ODEs governing the evolution of the overlaps. Finally, a Hessian analysis of the population-loss landscape confirms that the plateau and the solution manifold correspond to saddles with at least one negative eigenvalue and to marginal minima in the population-loss geometry, respectively.

The Apollonius problem asks for a sphere tangent to $n+1$ given spheres or hyperplanes in $\mathbb{R}^n$. This problem has been widely studied for an isolated configuration of $n+1$ spheres. In this paper, we study relations among the solutions of the Apollonius problem arising from a common family of spheres within the framework of Lie sphere geometry. More precisely, we consider a configuration of $n+2$ spheres in $\mathbb{R}^n$ and the solutions of the Apollonius problem corresponding to all its subsets of size $n+1$. The first main result concerns lines passing through the centers of pairs of solutions to the Apollonius problem. We prove that all these lines intersect at a single point $P_X$. We then introduce a two--step construction of further Apollonius spheres and show that the lines determined by their centers also pass through $P_X$. This yields numerous applications in two and three dimensions and, at the same time, automatically extends them to $\mathbb{R}^n$. The second main result is an $n$--dimensional generalization of K. Morita's three-dimensional theorem on the inscribed sphere in a configuration of mutually tangent spheres. We show that Morita's theorem is a special case of our result for an arbitrary configuration of $n+2$ spheres in $\mathbb{R}^n$, not necessarily mutually tangent. Moreover, we connect this result with the preceding ones by proving that the center of the corresponding inscribed sphere is again the point $P_X$.

Remanufacturing is fundamentally more challenging than traditional manufacturing due to the significant uncertainty, variability, and incompleteness inherent in end-of-life (EoL) products. At the same time, it has become increasingly essential and urgent for facilitating a circular economy, driven by the growing volume of discarded electronic products and the escalating scarcity of critical materials. In this paper, we review the existing literature and examine the key challenges as well as emerging opportunities in intelligent automation for EoL electronics remanufacturing, providing a comprehensive overview of how robotics, control, and artificial intelligence (AI) can jointly enable scalable, safe, and intelligent remanufacturing systems. This paper starts with the definition, scope, and motivation of remanufacturing within the context of a circular economy, highlighting its societal and environmental significance. Then it delves into intelligent automation approaches for disassembly, inspection, sorting, and component reprocessing in this domain, covering advanced methods for multimodal perception, decision-making under uncertainty, flexible planning algorithms, and force-aware manipulation. The paper further reviews several emerging techniques, including large foundation models, human-in-the-loop integration, and digital twins that have the potential to support future research in this area. By integrating these topics, we aim to illustrate how next-generation remanufacturing systems can achieve robust, adaptable, and efficient operation in the face of complex real-world challenges.

This paper presents an experimental and theoretical study of the nonlinear behavior of imperfect interfaces in multilayer structures using an equivalent vibro-acoustic approach. The multilayer system is modeled through a Zig-Zag formulation, in which interfacial coupling conditions, stress continuity and displacement discontinuity, relate the kinematics of adjacent layers while preserving an independent description of each layer. This framework significantly reduces the number of kinematic unknowns without compromising the model accuracy. An equivalent Kirchhoff-Love plate formulation is then introduced to derive a frequency-dependent bending stiffness representative of the global structural response. Experimental measurements of the transverse displacement field are performed using laser vibrometry and processed via the Corrected Force Analysis Technique (CFAT).The results demonstrate that the dynamic response of a three-layer beam with imperfect interfaces depends on the excitation level. In particular, variations in the equivalent bending stiffness are observed, revealing the nonlinear nature of the interfacial behavior. The proposed methodology is applied to a glass-epoxy-glass multilayer beam under various excitation levels.

We give an example of proper smooth fourfold over a perfect field k of characteristic p > 0 with asymmetric Hodge--Witt numbers in total degree 3. Our example is sharp both in terms of dimension and total degree. We arrive at our example by computing and approximating the Hodge--Witt cohomology groups of the classifying stack B alpha_p.

In this paper, we study the asymptotic stability of smooth 1-solitons in the Degasperis-Procesi (DP) equation. Such solutions necessarily exist on a non-zero background, and their spectral and orbital stability has previously been verified by Li, Liu & Wu and by Lafortune & Pelinovsky. Using the complete integrability of the DP equation to establish the strong spectral stability of smooth solitary waves, namely that the origin is the only eigenvalue of the associated linearized operator acting on $L^2(\mathbb{R})$ and that, moreover, in appropriate exponentially weighted spaces the non-zero spectrum for the linearized operator admits a spectral gap away from the imaginary axis. This spectral gap result {is then} upgraded to an exponential decay estimate on the semigroup associated with the linearized operator, establishing a linear asymptotic stability result in exponentially weighted spaces. Finally, we outline analytical challenges with extending our result to the nonlinear level.

In this paper, we study a network formation game in which agents seek to maximize their influence by allocating constrained resources to choose connections with other agents. In particular, we use Katz centrality to model agents' influence in the network. Allocations are restricted to neighbors in a given unweighted network encoding topological constraints. The allocations by an agent correspond to the weights of its outgoing edges. Such allocation by all agents thereby induces a network. This models a strategic-form game in which agents' utilities are given by their Katz centralities. We characterize the Nash equilibrium networks of this game and analyze their properties. We propose a sequential best-response dynamics (BRD) to model the network formation process. We show that it converges to the set of Nash equilibria under very mild assumptions. For complete underlying topologies, we show that Katz centralities are proportional to agents' budgets at Nash equilibria. For general underlying topologies in which each agent has a self-loop, we show that hierarchical networks form at Nash equilibria. Finally, simulations illustrate our findings.

This article presents a mathematical study of the problem of identifying a time-dependent source term in transport processes described by a timefractional parabolic equation, based on noisy time-dependent measurements taken at an arbitrary position. The problem is analytically solved using Fourier techniques, and it is shown that the solution is unstable. To address this instability, three one-parameter families of regularization operators are proposed, each designed to counteract the factors responsible for the instability of the inverse operator. Additionally, a new rule for selecting the regularization parameter is introduced, and an error bound is derived for each estimate. Numerical examples with varying characteristics are provided to illustrate the advantages of the proposed strategies.

This paper presents a new Lemoine-type circle defined by a six-point configuration satisfying a cocyclicity criterion. We prove the result, establish a converse theorem, and relate the new circle to previously known Lemoine circles, in particular the one introduced by Q.T. Bui. We show that the new circle does not belong to the family of Tucker circles.

These proceedings contain the papers that were presented at the 7th Workshop on Models for Formal Analysis of Real Systems (MARS 2026), which took place on 12 April 2026 in Turin, Italy, as a satellite event of the 29th International Joint Conferences on Theory and Practice of Software (ETAPS 2026). The goal of MARS is to bring together researchers from different communities who are developing formal models of real systems in areas where complex models occur (e.g., networks, cyber-physical systems, hardware/software codesign, biology). The motivation for MARS stems from the following two observations: - Large case studies are essential to show that specification formalisms and modelling techniques are applicable to real systems, whereas many papers only consider toy examples or tiny case studies. - Developing an accurate model of a real system takes a large amount of time, often months or years. In most papers, however, salient details of the model need to be skipped due to lack of space, and to leave room for formal verification methodologies and results. MARS aims at remedying these issues, emphasising modelling over verification, so as to retain lessons learned from formal modelling, which are not usually discussed elsewhere, and which may lay the basis for future analysis and comparison.

We report a continuous-wave Ho:YAG thin-disk laser operating at the hundred-watt power level. In multimode operation, the laser delivers a maximum output power of 230 W, with a slope efficiency of 35.9% and an optical-to-optical conversion efficiency of 35.3%. In single-mode operation, an output power of 152.3 W is achieved, with beam quality factors M2 of 1.08 and 1.06 in the horizontal and vertical directions, respectively.

We use random matrix theory for the Circular Unitary Ensemble (CUE) to study moments of derivatives of the Riemann zeta function shifted a small distance from the critical line. The corresponding CUE moments are studied in the limit of large matrix size in two regimes: when the spectral parameter is (1) suitably far inside the unit disc, and (2) at a small distance from the unit circle. In case (1), we obtain an asymptotic formula as a combinatorial sum over contingency tables, while in case (2) we obtain a sum over certain determinants with multiplicative coefficients given by Kostka numbers. The latter result is also valid exactly on the unit circle. Then, we consider the analogous problem for mean values of derivatives of the zeta function with suitable shifts. Assuming the Lindelöf hypothesis, we show that this mean value gives rise to the same sum over contingency tables obtained in the CUE. For sufficiently low-order moments, we establish this result unconditionally.

Context: Since it is well-established that developers spend a substantial portion of their time understanding source code, the ability to automatically identify algorithms within source code presents a valuable opportunity. This capability can support program comprehension, facilitate maintenance, and enhance overall software quality. Objective: We empirically evaluate how combining LLMs with static code analysis can improve the automated recognition of algorithms, while also evaluating their standalone performance and dependence on identifier names. Method: We perform multiple experiments evaluating the combination of LLMs with static analysis using different filter patterns. We compare this combined approach against their standalone performance under various prompting strategies and investigate the impact of systematic identifier obfuscation on classification performance and runtime. Results: The combination of LLMs with lightweight static analysis performs surprisingly well, reducing required LLM calls by 72.39-97.50% depending on the filter pattern. This not only lowers runtime significantly but also improves F1-scores by up to 12 percentage points (pp) compared to the baseline. Regarding the different prompting strategies, in-context learning with two examples provides an effective trade-off between classification performance and runtime efficiency, achieving F1-scores of 75-77% with only a modest increase in inference time. Lastly, we find that LLMs are not solely dependent on name-information as they are still able to identify most algorithm implementations when identifiers are obfuscated. Conclusion: By combining LLMs with static analysis, we achieve substantial reductions in runtime while simultaneously improving F1-scores, underscoring the value of a hybrid approach.

The concept of positively invariant (PI) sets has proven effective in the formal verification of stability and safety properties for autonomous systems. However, the characterization of such sets is challenging for nonlinear systems in general, especially in the presence of constraints. In this work, we show that, for a class of feedback linearizable systems, called differentially flat systems, a PI set can be derived by leveraging a neural network approximation of the linearizing mapping. More specifically, for the class of flat systems, there exists a linearizing variable transformation that converts the nonlinear system into linear controllable dynamics, albeit at the cost of distorting the constraint set. We show that by approximating the distorted set using a rectified linear unit neural network, we can derive a PI set inside the admissible domain through its set-theoretic description. This offline characterization enables the synthesis of various efficient online control strategies, with different complexities and performances. Numerical simulations are provided to demonstrate the validity of the proposed framework.

Quantum dots (QDs) offer significant potential for applications in quantum information and optoelectronic devices; however, conventional time-resolved spectroscopy cannot generally simultaneously extract both long-lived relaxation dynamics and short-lived quantum beats from ensemble measurements. This limitation arises from the inherent trade-off between temporal resolution and total acquisition time. Here, we demonstrate that asynchronous optical sampling based on a fiber-delivered frequency comb enables simultaneous observation of QD dynamics across multiple timescales. By integrating a galvanometric scanner, we achieve spatial mapping over a $1 \times 1$-\si{\milli\meter}$^2$ area at 441 discrete points in 30.1~min, a measurement that would otherwise require more than 12~days. At each location, both quantum beats and relaxation lifetimes are resolved, giving physical insights into QD ensembles that were previously inaccessible and paving the way for rapid feedback in device fabrication.