We expand on recent work arXiv:2601.17199 demonstrating the existence of a novel entanglement radius $ξ_0$ characterizing flux tube entanglement entropy (FTE$^2$) in (2+1)D Yang-Mills theory. This physical scale corresponds to the intrinsic thickness of the flux tube that must be fully severed by an entangling region for color degrees of freedom in the flux tube to contribute non-zero FTE$^2$. We consider here geometries of the entanglement region $V$ on the lattice where the length of the region cross-cutting the flux tube is of the same magnitude as $ξ_0$. Our results further the conclusions of arXiv:2601.17199 by adding detailed new information on the topological structure of the entanglement radius of color flux tubes.

Chirotopes are a common combinatorial abstraction of (planar) point sets. In this paper we investigate decomposition methods for chirotopes, and their application to the problem of counting the number of triangulations supported by a given planar point set. In particular, we generalize the convex and concave sums operations defined by Rutschmann and Wettstein for a particular family of chirotopes (which they call chains), and obtain a precise asymptotic estimate for the number of triangulations of the double circle, using a functional equation and the kernel method.

This paper investigates the formation control problem of heterogeneous, autonomous agents that communicate over a wireless multiple access channel. Instead of avoiding interference through orthogonal node-to-node transmissions, we exploit the superposition property of the wireless channel to compute, at each receiver, normalized convex combinations of simultaneously broadcast neighbor signals. At every communication instant, agents update their reference positions from these aggregates, and track the references in continuous time between updates. The only assumption on the agent dynamics is that each agent tracks constant reference positions exponentially, which accommodates a broad class of platforms. Under this assumption, we analyze the resulting jump-flow system under time-varying communication graphs and unknown channel coefficients. We derive a communication-rate based sufficient condition that guarantees convergence to a prescribed formation. We then provide a geometry-aware refinement showing how favorable tracking transients can relax the required condition. Simulations with unicycle agents illustrate the theoretical results and demonstrate a substantial reduction in the number of required orthogonal transmissions compared to interference-avoiding node-to-node communication protocols.

The list update problem is one of the oldest and simplest problems in online algorithms: A set of items must be maintained in a list while requests to these items arrive over time. Whenever an item is requested, the algorithm pays a cost equal to the position of the item in the list. In the i.i.d. model, where requests are drawn independently from a fixed distribution, the static ordering by decreasing access probabilities $p_1\ge p_2\ge \dots \ge p_n$ achieves the minimal expected access cost OPT$=\sum_{i=1}^n ip_i$. However, $p$ is typically unknown, and approximating it by tracking access frequencies creates undesirable overheads. We prove that the Transposition rule (swap the requested item with its predecessor) has expected access cost at most OPT$+1$ in its stationary distribution. This confirms a 50-year-old conjecture by Rivest up to an unavoidable additive constant. More abstractly, it yields a purely memoryless procedure to approximately sort probabilities via sampling. Our proof is based on a decomposition of excess cost, and its technical core is a "sign-eliminating" combinatorial injection to witness nonnegativity of a constrained multivariate polynomial.

Existing high performance blockchains verify one signature per transaction on the critical path, which creates O(N) verification cost, high hardware pressure, and difficult post quantum migration. This paper presents ACE Runtime, a ZKP native execution layer built on identity authorization separation. We replace per transaction signature checks with lightweight HMAC attestations in the hot path, then generate one aggregated zero knowledge finality certificate per block in an asynchronous prove stage. The system is organized as an Attest Execute Prove pipeline with two tier finality: soft finality from BFT voting and hard finality from proof verification. Under standard cryptographic assumptions, we provide formal arguments for attestation unforgeability and hard finality irreversibility. We also define a two phase timeout and backup proving path with witness availability gossip for liveness under builder failure. Quantitative results combine analytical modeling with reference implementation measurements. The prototype shows low CPU orchestration overhead, while model driven analysis projects constant per block verification cost, lower validator hardware requirements for non builders, and better bandwidth efficiency than per transaction signature designs. These results indicate that identity authorization separation is a practical architecture for sub second cryptographic finality with a clear path toward stronger post quantum components.

In this article we study some properties of the discrete convolution of Liouville function $S(n):=\sum_{m_{1}+m_{2}=n}λ\left(m_{1}\right)λ\left(m_{2}\right)$, which is a Goldbach-type counting function of representations. In particular, using the general approach introduced in a recent paper \cite{CGZ}, we will give an explicit formula for weighted averages of $S(n)$ with a general weights $f(w)$ that verify suitable conditions. This formula allows us to obtain interesting information about the Dirichlet and power series of $S(n)$ and the discrete convolution with an arbitrary numbers of factors $λ(n)$.

For the Fermi-Hubbard model in the Mott insulator phase, we employ the hierarchy of correlations to study how doublon and holon quasi-particle excitations are affected by adding disorder to the system. We study two types of disorder: charge disorder, in the form of on-site potential randomness; and spin disorder, in the form of a fixed, randomly generated background spin arrangement. By analysing the quasi-particle eigen-spectra and quantifying the degree to which the corresponding eigen-states localise, we find both an energetic and spatial separation between localised and delocalised states in the charge disorder. In contrast, the spin disorder results in localised states throughout the quasi-particle bands. Finally, we repeat our calculations using strong-coupling perturbation theory, and compare the results obtained from both methods.

The set of isotopy classes of nontrivial torus knots $T(p,q)$ in $S^3$ is in bijection with the set of coprime integer pairs $(p,q)$ satisfying $|p|>q\geq 2$. We verify the AJ conjecture for the connected sums $T(p,q)\# T(a,b)$ when $p$ and $a$ have the same sign. Notably, in cases where $pq=ab$ but $p\ne a$, the recurrence polynomial $α(t,M,L)$ of $T(p,q)\#T(a,b)$ has repeated factors involving the variable $L$ after evaluation at $t=-1$. These appear to be the first examples of knots exhibiting this phenomenon. Therefore, the AJ conjecture requires a slight modification to accommodate this possibility.

We construct the Gamow Golden Rule of multichannel scattering, and use it to obtain the decay distributions, the partial decay constants, the partial decay widths, and the branching fractions of a resonance that has several decay modes. We exemplify the results using two coupled-channel square well potentials.

Enterprises and organizations today increasingly deploy in-house, cloud based applications and APIs for internal operations or external customers. These deployments deal with increasing number of threats, despite security features offered by cloud service providers. This work focus on threats that exploit application layer vulnerabilities of cloud workloads. Prevention and mitigation measures against such threats need to be cognizant of application semantics, posing a hurdle to existing solutions. In this work, we design and implement a security framework that allow cloud workload administrators to easily define and enforce policies capable of preventing (i) unrestricted resource consumption, (ii) unrestricted access to sensitive business flows, and (iii) broken authentication. Our framework, Paladin, leverages large language models to extract sufficient semantic meaning from API requests to provide cloud administrators with an application agnostic policy definition interface. Once defined, requests are automatically matched with relevant policies and enforced by high performance proxies. Evaluations with our prototype show that such a framework has broad applicability across applications, good policy identification accuracy, and reasonable overheads, making it substantially easier to define and enforce cross application policies.

In neutron star (NS) magnetospheres, plasma waves propagate as normal modes with distinct propagation dynamics that strongly influence observable signals. This letter presents a unified theory of linear mode conversion between Alfv'en (A), superluminal ordinary (O), and extraordinary (X) modes, incorporating the effect of magnetic-field geometry and local plasma response. Magnetic field-line curvature induces A-X conversion for low frequencies and O-X conversion at high frequencies, whereas plasma gradients alone do not drive X-mode coupling. We show that a single dimensionless parameter controls both conversion channels. The conversion efficiency follows the universal nonadiabatic transition probability of a multilevel quantum system. Efficient conversion occurs within a narrow angular window between the wave vector and magnetic field, localizing potential conversion sites in the NS magnetosphere. This linear mechanism naturally accounts for complex polarization features observed in pulsars and some fast radio bursts.

We present a simple, malleable and low-overhead approach for improving generic biased quantum error mitigation (QEM) methods, achieving up to 15% fidelity improvements over standard QEM on 100-qubit circuits with up to 2000 entangling gates. We do so by constructing verifiable benchmark circuits which mirror the application circuit's native-gate structure and thus noise profile. These circuits can be used to benchmark and mitigate the bias of the underlying error mitigation method, requiring only the application circuit and hardware native gate set. We present two methods for generating benchmark circuits; one is agnostic to the target hardware at the expense of a small overhead of single-qubit gates, while the other is specific to the IBM superconducting hardware and has no gate overhead. As a corollary, we introduce benchmarked-noise zero-noise extrapolation (bnZNE) as a simple adaptation of zero-noise extrapolation (ZNE), one of the most popular error mitigation methods. We consider as an example the bias-mitigated ZNE and bnZNE of Trotterized Hamiltonian simulations, observing that our approaches outperform standard ZNE using both small-scale classical simulations and 100-qubit utility-scale experiments on the IBM superconducting hardware. We consider the measurement of both single-site observables as well as two-site correlations along a one-dimensional qubit chain. We also provide a software package for implementing the error mitigation techniques used in this research.

We investigated the structural evolution and optical properties of beta-Ga2O3 crystals implanted with different rare-earth (RE) ions using channeling Rutherford Backscattering Spectrometry, Positron Annihilation, Photoluminescence, and Photoluminescence Excitation spectroscopies. The studies reveal that implantation-induced disorder, accompanying phase transitions, and post-annealing structural recovery are largely insensitive to the implanted RE species. The defect microstructure is also found to be similar for all implanted RE ions. Thermal annealing does not completely remove radiation-induced defects but instead drives their rearrangement into larger defect complexes. Unimplanted (virgin) beta-Ga2O3 exhibits strong UV-visible emission attributed to oxygen vacancies, whereas the introduction of RE ions produces additional emission lines originating from electronic transitions within RE3+ ions. The results indicate that RE3+ ions are excited through the host conduction band, followed by non-radiative relaxation to the 4f excited states and radiative decay to the respective ground states. Fluence-dependent studies of Yb3+ reveal the onset of concentration quenching, while RE-related emission remains efficient even in the presence of substantial lattice disorder. These findings provide new insight into defect evolution in ion-implanted beta-Ga2O3 and clarify the excitation mechanisms of RE3+ ions, offering guidance for optimizing the optical performance of beta-Ga2O3:RE materials.

Timing degradation in SRAM-based FPGAs arises from multiple physical mechanisms that manifest differently in the routing fabric, most notably power-distribution-network (PDN) marginality and configuration-induced routing perturbations. Existing in-situ timing monitors provide limited insight into the physical origin, spatial structure, or statistical characteristics of the degradation. This paper presents a scalable in-situ timing diagnosis architecture that enables fine-grained, routing-aware characterization of timing behavior directly within the FPGA fabric during normal operation. The proposed approach combines non-intrusive delay taps placed at routing switch-matrix boundaries with distributed phase-swept delay monitoring elements and centralized statistical analysis. By extracting probabilistic delay distributions rather than binary timing margins, the framework captures both mean delay shifts and timing variability across spatially distributed routing locations. Experimental results obtained on a modern SRAM-based FPGA show that PDN-induced timing degradation produces globally correlated delay shifts with minimal change in variance, whereas routing-induced perturbations exhibit localized, topology-dependent delay growth and increased timing dispersion. Spatial correlation analysis and two-dimensional correlation heatmaps further reveal distinct signatures that enable systematic differentiation between these mechanisms. The presented architecture operates concurrently with an active user design and does not require external instrumentation, radiation sources, or design modification. These results establish a practical foundation for in-situ timing diagnosis, reliability assessment, and architecture-aware timing management in large FPGA-based systems.

Many scientific fields compare two or more noisy time series that integrate the same underlying process but are recorded on different time scales. In paleoclimate studies, for example, proxy measurements are collected versus stratigraphic depth in a climate archive and then converted to calendar time. Synchronizing two proxy records often requires estimating an alignment that maps the depth (or preliminary age) of an input record onto the calendar--time scale of an absolutely--dated target record so that corresponding proxy signals line up. Existing alignment approaches are generally optimization--based and return a single transformation, providing limited formal uncertainty quantification. Here, we introduce BSync, a Bayesian synchronization framework that treats alignments as inference over a monotone time--mapping function to match an input to a target record. The alignment is expressed as a transformation of the input depth (or age) scale to match the target record, achieved through a link function that locally expands and compresses the input scale. The model is parameterized through interpretable local rate parameters, enabling the specification of priors on deposition times to regularize the alignment toward physically plausible deformations. BSync jointly infers the aligned chronology and provides posterior uncertainty for the time--warping function and the resulting age scale. In synthetic data experiments and a real--data case study, BSync yields well--calibrated credible intervals for the aligned time scale and achieves more accurate alignments than a state--of--the--art automated method, particularly when independent age constraints are sparse.

The paper is concerned with a scalar conservation law with discontinuous gradient-dependent flux. Namely, the flux is described by two different functions $f(u)$ or $g(u)$, when the gradient $u_x$ of the solution is positive or negative, respectively. In the stable case where $f(u)<g(u)$ for all $u\in R$, it was proved in [1] that the limits of vanishing viscosity approximations form a contractive semigroup w.r.t. the $L^1$ distance. Further, they coincide with the limits of a suitable family of front tracking approximations. In the present paper we introduce a simple condition that guarantees that every weak, entropy admissible solution of a Cauchy problem coincides with the corresponding semigroup trajectory, and hence is unique.

In this work, we study the non-autonomous dynamics generated by random iterations of the cubic family of the form $z^3 + cz$. The parameter sequence is chosen randomly from a bounded Borel subset of $\mathbb{C}$. We investigate topological properties of the corresponding Julia sets, with particular emphasis on conditions leading to total disconnectedness. We prove that the set of parameter sequences for which the Julia set is totally disconnected is dense in the parameter space. We also construct examples where the Julia set is totally disconnected but the associated non-autonomous system is not hyperbolic. Finally, under suitable probabilistic assumptions on the parameter distribution, we show that almost every sequence produces a totally disconnected Julia set.

We investigate single-cell directional sensing from diffusing chemoattractant signals released by a localized source. We focus on the low-concentration regime in which receptor activity is discrete and cellular decisions are made on timescales far shorter than those required for steady-state concentration profiles or receptor occupancy to emerge. We derive analytic expressions for the joint distribution of receptor binding times and binding locations, conditional on the position of the source. We show that early binding events carry disproportionately more information about source directionality than later arrivals. Motivated by this observation, we propose and analyze several source-localization estimates that exploit early receptor binding statistics. Our results demonstrate that, even with a small number of binding events, cells possess sufficient information to rapidly and accurately infer the directionality of a diffusing chemoattractant source.

The quantum trajectories in the de Broglie-Bohm formulation of quantum mechanics depend on an additional quantum potential derived from the full wave solution of Schrödinger's equation. The task of supplying collectively all the correct quantum results strongly alters the characteristics of the corresponding classical trajectories, which underlie semiclassical approximations to the evolving wave function. Both classical and quantum trajectories are here considered to be conservative with no influence of an external environment, even though this is the source of eventual classicality in quantum systems, that is, decoherence. The concept of integrability, closely correspondent in classical and quantum mechanics, is not preserved by the quantum trajectories. General systems, in which classical chaotic motion participates, are much harder to treat semiclassically, but quantum trajectories can be chaotic even for integrable systems. This discrepancy between the character of classical and quantum trajectories in the de Broglie-Bohm interpretation does not clarify the singular classical-quantum transition.

Personalized medicine aims to tailor treatments to individual patients, especially when people respond heterogeneously to therapies. A key objective is to learn individualized treatment rules that recommend optimal treatments from patient characteristics. Outcome weighted learning (OWL) is an important framework because it reformulates the task as a weighted classification problem targeting clinical benefit and using modern machine learning tools. Existing OWL theory has been focusing on specific surrogate losses and Gaussian kernels. Matern kernels, which allow adjustable smoothness and better match many real world data structures, are often more suitable and include the Gaussian kernel as a special case. This work develops a general relationship between population 0-1 risk and risks from a broad class of nonnegative surrogate losses using a constrained variational transformation. The transform simplifies for convex losses and provides simple expressions for certain nonconvex losses. A condition is established that ensures a nontrivial upper bound on the excess 0-1 risk. The paper establishes convergence rates for kernel based OWL under smoothness conditions with Matern kernels or geometric noise conditions with Gaussian kernels for both convex and nonconvex losses. It also proposes two iteratively reweighted convex optimization algorithms. Simulations and an application to ACTG 175 show strong performance.

In this paper we explore a connection between certain Almost Perfect Nonlinear Functions (APN functions) and relative difference sets. In particular, we show that the image set of certain 2-to-1 APN functions is a relative difference set. Through a result of Pott this further provides a connection between APN functions and bent functions.

Growth fronts of slime molds are characterized through a direct geometric analysis based on Loewner evolutions, using experimentally acquired time-resolved images. The associated Loewner driving functions reconstructed from expanding pseudopod boundaries display statistical properties consistent with Gaussian-like behavior. A geometric estimate of the diffusivity parameter~$κ$ is inferred from fractal scaling, while Brownian diagnostics are assessed on the reconstructed driving signal. These findings show that the boundaries of a growing living organism display statistical and geometric properties consistent with emergent Loewner dynamics over experimentally accessible scales. This study establishes a quantitative framework for analyzing biological growth interfaces and suggests new connections between morphogenesis, stochastic geometry, and network reorganization under varying environmental conditions. We provide, to our knowledge, the first explicit reconstruction of a Loewner driving function from a living growth interface, revealing an emergent Brownian-like conformal growth regime at expanding fronts.

Cygnus~X-3 is a Galactic X-ray binary with a 4.8-hr orbital period operating in the ultraluminous regime. Although the system is viewed at relatively low inclination ($i\approx28^\circ$), it exhibits a deep orbital modulation. Recent IXPE observations show strong linear polarization orthogonal to the radio jet, indicating that the X-ray emission is dominated by reflection from the inner walls of a supercritical outflow funnel. We propose a Hybrid Roche-lobe overflow (RLOF) scenario in which a massive Wolf-Rayet donor effectively fills its Roche lobe with a focused wind driving a super-Eddington accretion stream. Using a numerical synthesis of the folded light curve, we show that the modulation is reproduced when the central funnel is periodically occulted by a vertically extended, shock-heated Turbulent Wall formed by stream impact on the outer disk rim. This produces a phase lag ($Δϕ\approx0.11$) between X-ray minimum and binary conjunction, with extended attenuation by the WR wind defining a broader Suppression Region. This geometry explains the enhanced iron-line equivalent width during X-ray minimum via a coronagraphic effect. The large radial-velocity amplitude of FeXXVI measured by XRISM ($K_{\rm obs}\approx430$ km s$^{-1}$) and its zero-crossing at $ϕ_X=0.0$ arise naturally in the stream-impact region rather than from orbital motion of the compact object. Finally, we show that the observed secular orbital expansion ($\dot P>0$) follows directly from highly non-conservative mass transfer with inner-disk mass loss, indicating that Cygnus~X-3 is a stable, long-lived system in a supercritical accretion regime.

We study large-spin operators in conformal field theories (CFTs) in spacetime dimensions $d>2$ by placing the theory on appropriate pp-wave backgrounds. We show that these geometries admit Heisenberg-group symmetries, and that these symmetries, combined with locality of quantum fields on such spacetimes, impose strong constraints on the asymptotic spectrum in the large-spin limit. The pp-wave backgrounds probe both the small-twist regime, corresponding to the Regge or light-cone bootstrap, and a strongly coupled regime of large twist. Finally, we demonstrate that causality (or the requirement that the energy be bounded from below) leads to a new unitarity bound in $3+1$ dimensions.

This paper presents a new theory of locality and its compiler support. The theory is fully symbolic and derives locality as polynomials, and the compiler analysis supports affine loop nests. They derive cache-performance scaling in quadratic and reciprocal expressions and are more general and precise than empirical scaling rules. Evaluated on a benchmark suite of 41 scientific kernels and tensor operations, the compiler requires an average of 41 seconds to derive the locality polynomials. After derivation, predicting the cache miss count for any given input size and cache configuration takes less than a millisecond. Across all tests--with and without loop fusion--the accuracy in the data movement prediction is 99.6\%, compared to simulated set-associative L1 data cache.

Superconducting diodes, devices that allow Cooper-pair currents to flow more easily in one direction than the other, are set to become key building blocks for dissipationless electronics. Existing realizations, however, rely on magnetic fields, ferromagnets, or complex heterostructures that hinder integration and scalability. Here we demonstrate a diode effect for Cooper-pairs that arises solely from electron-electron interactions in nanoscale superconducting lead islands. When these islands are driven into the Coulomb blockade regime, Cooper-pair transport occurs through resonant charge states. By tuning the island's electrostatic environment, we controllably break particle-hole symmetry and induce nonreciprocal supercurrents, thereby achieving a gate-switchable superconducting diode without any external magnetic field. Our approach enables robust rectification of superconducting currents and microwave photoresponse, providing a scalable strategy to superconducting logic devices.

Belief-propagation (BP) decoding for quantum low-density parity-check (QLDPC) codes is appealing due to its low complexity, yet it often exhibits convergence issues due to quantum degeneracy and short cycles that exist in the Tanner graph. To overcome this challenge, this paper proposes a reinforcement-learning (RL) approach that learns (offline) how to decode QLDPC codes based on sequential decoding trajectories. The decoding is formulated as a Markov decision process with a local, syndrome-driven state representation of the underlying RL agent. To enable fast inference, critical for practical implementation, we incrementally update our RL-based QLDPC decoder using second-order neighborhoods that avoid global rescans. Simulation results on representative QLDPC codes demonstrate the superiority of the proposed RL-based QLDPC decoders in terms of performance and convergence speed when compared to flooding and random sequential schedules, while achieving performance competitive with state-of-the-art BP-based decoders at comparable complexity.

Demonstrating quantum heuristics that outperform strong classical solvers on large-scale optimization remains an open challenge. Here we introduce Regularized Warm-Started QAOA (RWS-QAOA), which initializes qubits by minimizing expected energy with a regularizer that penalizes near-bitstring states, preventing QAOA from stalling. We further propose a protocol that yields fixed, instance-independent parameters, enabling RWS-QAOA to operate as a non-variational algorithm in which the quantum circuit parameters are fixed and only a classical warm starting step is instance-dependent. We evaluate RWS-QAOA on the Max-Cut problem for random regular graphs, where this protocol yields a constant-depth quantum circuit, across three complementary settings. First, on Quantinuum's trapped-ion processor, RWS-QAOA outperforms the classical algorithms with the best provable guarantees for Max-Cut on $3$-regular graphs, namely Goemans-Williamson and Halperin-Livnat-Zwick, on $96$-node instances. Second, tensor-network simulations on graphs with up to $N{=}10{,}000$ nodes show that depth-$6$ RWS-QAOA, achieving an average cut fraction of $0.9167$, surpasses the best classical heuristics under matched restrictions (no local-search post-processing and no iterative refinement). Third, we remove these restrictions and benchmark against the strongest unrestricted classical heuristics, including an optimized parallel Burer-Monteiro solver that improves upon the MQLib implementation. Even against this stronger baseline, we project that surface-code RWS-QAOA reaches a quantum-classical runtime crossover below $0.2$ seconds on $3{,}000$-node graphs with fewer than $1.3$ million physical qubits. Our results show that constant-depth quantum circuits combined with a classical warm start have a credible potential to surpass classical solvers on the Max-Cut problem when executed on future quantum computers.

We establish Hoeffding-type concentration inequalities for the low and high tail bounds of sums of exchangeable random variables. Our results exhibit an anti-symmetry in such tail bounds due to the assumption of exchangeability, a generalization of the i.i.d. setting. In contrast to the existing literature on this problem, our result provides an upper tail bound with respect to the largest mean of a distribution in the support of the de Finetti mixing measure, and not the population mean. Equivalently, we establish a lower tail bound with respect to the smallest mean of a distribution in the support of the de Finetti mixing measure. This bridges the gap between finite sample and population means of exchangeable random variables, and distributional means.

Achieving efficient ultrafast optical control of antiferromagnetic spin dynamics is a central goal for next-generation high-speed THz spintronic and magnonic devices. Resonant optical pumping of crystal-field-split d-d orbital multiplets in magnetic TM ions directly modulates exchange and spin-orbit interactions, inducing large-amplitude coherent spin precession. However, such effects are limited to a handful of systems and there is no general strategy to enhance d-d photomagnetism in antiferromagnets. Here, we demonstrate the engineering of photomagnetism via TM-ion doping in collinear van der Waals antiferromagnets. In Mn$_{1-x}$Ni$_x$PS$_3$, small amounts of Ni$^{2+}$ activate a strong photomagnetic response while largely preserving the Néel ground state. Even 10% Ni boosts the response by more than an order of magnitude compared to pure MnPS$_3$, with resonant pumping of Ni$^{2+}$ d-d transitions driving large-amplitude coherent spin precession and providing helicity-dependent phase control. Tuning the pump energy across the full Mn$_{1-x}$Ni$_x$PS$_3$ composition range shows that Ni excitations remain effective across competing Néel and zig-zag antiferromagnetic states while supporting tunable-frequency coherent spin precession. These results establish TM-ion doping as a versatile strategy to harness orbital multiplet excitations for ultrafast, low-dissipation spin control in van der Waals antiferromagnets.