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The neutron capture rates and temperature dependent stellar beta decay rates of Mo isotopes are investigated within the framework of the statistical code (Talys v1.96) and proton neutron quasi particle random phase approximation (pnQRPA) models. The Maxwellian average cross section (MACS) and neutron capture rates for 95 98Mo(n,{\gamma}) 96 99Mo radiative capture processes are analyzed within the framework of statistical code Talys v1.96 based on the phenomenological nuclear level density (NLD) parameters and gamma strength functions (GSFs). The current model based computed data for MACS provide a good comparison with the existing measured data. The temperature dependent stellar \b{eta} decay rates of 95 98Mo are investigated at different densities within the framework of the pn-QRPA model. For the considered temperature range, the neutron capture rates were found to be higher, by orders of magnitude, than the stellar \b{eta} rates., Comment: 10 pages, 7 figures

While large language models (LLMs) dominate the AI landscape, Small-scale large Language Models (SLMs) are gaining attention due to cost and efficiency demands from consumers. However, there is limited research on the training behavior and computational requirements of SLMs. In this study, we explore the computational bottlenecks of training SLMs (up to 2B parameters) by examining the effects of various hyperparameters and configurations, including GPU type, batch size, model size, communication protocol, attention type, and the number of GPUs. We assess these factors on popular cloud services using metrics such as loss per dollar and tokens per second. Our findings aim to support the broader adoption and optimization of language model training for low-resource AI research institutes., Comment: 8 pages, 4 figures

Nuclear structure properties and weak interaction rates of neutron rich even even iron (Fe) isotopes (A = 50 70) are investigated using the Interacting Boson Model1 (IBM1) and the proton neutron Quasiparticle Random Phase Approximation (pnQRPA) model. The IBM1 is used for the calculation of energy levels and the B(E2) values of neutron rich Fe isotopes. Later their geometry was predicted within the potential energy formalism of the IBM1 model. Weak interaction rates on neutron rich nuclei are needed for the modeling and simulation of presupernova evolution of massive stars. In the current study, we investigate the possible effect of nuclear deformation on stellar rates of even even Fe isotopes. The pnQRPA model is applied to calculate the weak interaction rates of selected Fe isotopes using three different values of deformation parameter. It is noted that, in general, bigger deformation values led to smaller total strength and larger centroid values of the resulting Gamow Teller distributions. This later translated to smaller computed weak interaction rates. The current finding warrants further investigation before it may be generalized.The reported stellar rates are up to 4 orders of magnitude smaller than previous calculations and may bear astrophysical significance., Comment: 27 pages, 6 figures, 7 Tables

Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with these challenges. To address this gap, we propose MissNODAG, a differentiable framework for learning both the underlying cyclic causal graph and the missingness mechanism from partially observed data, including data missing not at random. Our framework integrates an additive noise model with an expectation-maximization procedure, alternating between imputing missing values and optimizing the observed data likelihood, to uncover both the cyclic structures and the missingness mechanism. We demonstrate the effectiveness of MissNODAG through synthetic experiments and an application to real-world gene perturbation data.

The costs and complexity of the American judicial system limit access to legal solutions for many Americans. Large language models (LLMs) hold great potential to improve access to justice. However, a major challenge in applying AI and LLMs in legal contexts, where consistency and reliability are crucial, is the need for System 2 reasoning. In this paper, we explore the integration of LLMs with logic programming to enhance their ability to reason, bringing their strategic capabilities closer to that of a skilled lawyer. Our objective is to translate laws and contracts into logic programs that can be applied to specific legal cases, with a focus on insurance contracts. We demonstrate that while GPT-4o fails to encode a simple health insurance contract into logical code, the recently released OpenAI o1-preview model succeeds, exemplifying how LLMs with advanced System 2 reasoning capabilities can expand access to justice.

The FCC's National Broadband Map aspires to provide an unprecedented view into broadband availability in the US. However, this map, which also determines eligibility for public grant funding, relies on self-reported data from service providers that in turn have incentives to strategically misrepresent their coverage. In this paper, we develop an approach for automatically identifying these low-quality service claims in the National Broadband Map. To do this, we develop a novel dataset of broadband availability consisting of 750k observations from more than 900 US ISPs, derived from a combination of regulatory data and crowdsourced speed tests. Using this dataset, we develop a model to classify the accuracy of service provider regulatory filings and achieve AUCs over 0.98 for unseen examples. Our approach provides an effective technique to enable policymakers, civil society, and the public to identify portions of the National Broadband Map that are likely to have integrity challenges., Comment: 17 pages

Inference with transformer-based language models begins with a prompt processing step. In this step, the model generates the first output token and stores the KV cache needed for future generation steps. This prompt processing step can be computationally expensive, taking 10s of seconds or more for billion-parameter models on edge devices when prompt lengths or batch sizes rise. This degrades user experience by introducing significant latency into the model's outputs. To reduce the time spent producing the first output (known as the ``time to first token'', or TTFT) of a pretrained model, we introduce a novel method called KV Prediction. In our method, a small auxiliary model is used to process the prompt and produce an approximation of the KV cache used by a base model. This approximated KV cache is then used with the base model for autoregressive generation without the need to query the auxiliary model again. We demonstrate that our method produces a pareto-optimal efficiency-accuracy trade-off when compared to baselines. On TriviaQA, we demonstrate relative accuracy improvements in the range of $15\%-50\%$ across a range of TTFT FLOPs budgets. We also demonstrate accuracy improvements of up to $30\%$ on HumanEval python code completion at fixed TTFT FLOPs budgets. Additionally, we benchmark models on an Apple M2 Pro CPU and demonstrate that our improvement in FLOPs translates to a TTFT speedup on hardware. We release our code at https://github.com/apple/corenet/tree/main/projects/kv-prediction .

Modern vision models have achieved remarkable success in benchmarks where local features provide critical information about the target. There is now a growing interest in solving tasks that require more global reasoning, where local features offer no significant information. These tasks are reminiscent of the connectivity tasks discussed by Minsky and Papert in 1969, which exposed the limitations of the perceptron model and contributed to the first AI winter. In this paper, we revisit such tasks by introducing four global visual benchmarks involving path findings and mazes. We show that: (1) although today's large vision models largely surpass the expressivity limitations of the early models, they still struggle with the learning efficiency; we put forward the "globality degree" notion to understand this limitation; (2) we then demonstrate that the picture changes and global reasoning becomes feasible with the introduction of "visual scratchpads"; similarly to the text scratchpads and chain-of-thoughts used in language models, visual scratchpads help break down global tasks into simpler ones; (3) we finally show that some scratchpads are better than others, in particular, "inductive scratchpads" that take steps relying on less information afford better out-of-distribution generalization and succeed for smaller model sizes.

The Gamow-Teller (GT) strength distributions of sd shell N=Z nuclei ($^{24}$Mg, $^{28}$Si, and $^{32}$S) are investigated within the framework of proton-neutron quasi particle random phase approximation (pn QRPA) using a deformed basis. The nuclear properties of these special nuclei were investigated by the Relativistic Mean Field (RMF) model. The RMF framework with density-dependent interactions (DD-PC1 and DD-ME2) was employed to compute ground-state deformation parameters (beta 2) using potential energy curves. The $\beta_2$ values computed from the RMF and the finite range droplet model (FRDM) were employed as a free parameter in the pn QRPA calculation to investigate the resulting GT strength distributions.The present model-based analyses compare well with the observed data., Comment: 14 pages 1 table 4 figures

The neutron capture rates and Maxwellian averaged cross sections (MACS) for 90Zr(n,{\gamma}) 91Zr and 92Zr(n,{\gamma}) 93Zr processes have been computed within the framework of Talys v1.96. The effects of phenomenological nuclear level density (NLD) parameters and the gamma strength functions (GSFs) on Maxwellian averaged cross sections and neutron capture rates are examined both quantitatively and qualitatively. The present model based computed data for MACS and reaction rates gives a good comparison with the existing literature. The fine-tuning of the statistical models nuclear properties (level density and gamma-ray strength) to reproduce experimental data will allow the detailed investigation of the s process network., Comment: 16 pages 2 figures 3 tables

The nuclear ground state properties of 67 80As nuclei have been investigated within the framework of relativistic mean field (RMF) approach. The RMF model with density dependent (DDME2) interaction is utilized for the calculation of potential energy curves and the nuclear ground state deformation parameters $\beta_2$ of selected As isotopes. Later, the $\beta$ decay properties of As isotopes were studied using the proton neutron quasi particle random phase approximation pnQRPA model. These include Gamow Tellar (GT) strength distributions, log ft values, $\beta$ decay half lives, stellar $\beta$ plus minus decays and stellar electron positron capture rates. The $\beta_2$ values computed from RMF model were employed in the on QRPA model as an input parameter for the calculations of $\beta$-decay properties for 67 80As. The calculated log ft values were in decent agreement with the measured data. The predicted $\beta$-decay half lives matched the experimental values within a factor of 10. The stellar rates were compared with the shell model results. Only at high temperature and density values, the sum of $\beta$ plus and electron capture rates had a finite contribution. On the other hand, the sum of $\beta$ negative and positron capture rates were sizeable only at low density and high temperature values. For all such cases, the pn QRPA rates were found to be bigger than the shell model rates up to a factor of 33 or more. The findings reported in the current investigation could prove valuable for simulating the late stage stellar evolution of massive stars., Comment: 16 pages 6 figures 5 tables, Chinese Journal of Physics (2024)

The thermonuclear reaction rates and Maxwellian averaged cross sections (MACS) for the 84Kr(n,{\gamma}) 85Kr and 86Kr(n,{\gamma}) 87Kr processes were examined using the statistical model code Talys v1.96. The effects of nuclear level densities (NLDs) on Maxwellian averaged cross sections and neutron capture rates are examined both quantitatively and qualitatively. The present Talys based MACS and radiative capture rates for 84Kr(n,{\gamma}) 85Kr and 86Kr(n,{\gamma}) 87Kr processes agree well with the earlier reported findings. The statistical models nuclear properties (level density and gamma ray strength) were fine tuned to reproduce the existing experimental nuclear data., Comment: 11 Pages 1 table 2 figures. arXiv admin note: substantial text overlap with arXiv:2410.06603

Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding, and early exit strategies leverage the insight that computational demands can vary significantly based on the complexity and nature of the input. However, identifying optimal routing patterns for dynamic execution remains an open challenge, limiting the full potential of these adaptive methods. To address this need, we study adaptive computation in LLMs more systematically. We propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM. This design enables dynamic routing of tokens based on task complexity: tokens can be processed by either the small or big modules at each layer, or even bypass certain layers entirely. This allows us to introduce a novel notion of a token's difficulty, defined by its potential to benefit from additional computational resources. Importantly, by employing oracles to identify optimal patterns of adaptive computations, we gain valuable insights into the internal workings of LLMs and the routing processes in a simplified heterogeneous MoE setup. We show that trained routers operate differently from oracles and often yield suboptimal solutions. Notably, activating a large module in just one layer outperforms models that use large modules across all layers, underscoring the gap between practical implementations of routing in MoE models and theoretical optima for adaptive computation.

Beta decay is one of the key factors to understand the r process and evolution of massive stars. The Gamow Teller (GT) transitions drive the beta decay process. We employ the proton neutron quasiparticle random phase approximation (on QRPA) model to calculate terrestrial and stellar beta decay rates for 50 top ranked nuclei possessing astrophysical significance according to a recent survey. The model parameters of the on QRPA model affect the predicted results of beta decay. The current study investigates the effect of nucleon nucleon pairing gaps on charge changing transitions and the associated beta decay rates. Three different values of pairing gaps, namely TF, 3TF and 5TF, were used in our investigation. It was concluded that both GT strength distributions and half lives are sensitive to pairing gap values. The 3TF pairing gap scheme, in our chosen nuclear model, resulted in best prediction with around 80 percent of the calculated half-lives within a factor 10 of the measured ones. The 3TF pairing scheme also led to calculation of biggest beta decay rates in stellar matter., Comment: 32 pages 8 Tables 7 figures

The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters can be extremely slow and costly. In contrast, small language models are less expensive to train, but they often cannot achieve the accuracy of large models. In this paper, we explore an intriguing idea to connect these two different regimes: Can we develop a method to initialize large language models using smaller pre-trained models? Will such initialization bring any benefits in terms of training time and final accuracy? In this paper, we introduce HyperCloning, a method that can expand the parameters of a pre-trained language model to those of a larger model with increased hidden dimensions. Our method ensures that the larger model retains the functionality of the smaller model. As a result, the larger model already inherits the predictive power and accuracy of the smaller model before the training starts. We demonstrate that training such an initialized model results in significant savings in terms of GPU hours required for pre-training large language models.

The beta-decay log ft values for 210 215Pb 210 215Bi and 210 215Bi 210 215Po transitions in the north east region of 208Pb nuclei are estimated using the proton neutron quasiparticle random phase approximation model. The pn-QRPA equations were solved using the schematic model approach. The Woods Saxon (WS) potential was inserted as a mean field basis and nuclei were treated as spherical. Allowed Gamow Teller (GT) and first forbidden (FF) transitions were investigated in the particle hole (ph) channel. The calculated log ft values of the allowed GT and FF transitions using the pn-QRPA(WS) were found closer to the experimental values. Later we performed calculation of beta-decay rates in stellar environment. Here we solved the RPA equations in deformed Nilsson basis, both in the particle particle (pp) and particle hole (ph) channels. Allowed beta decay and unique first-forbidden (U1F) rates were calculated in stellar matter. For certain cases, the calculated U1F contribution was much more than the allowed beta decay rates under prevailing stellar conditions in line with previous findings. Increasing temperature of the stellar core affected the allowed GT rates more than the U1F rates., Comment: 26 pages 13 tables and zero figure

It was only until the 20th century when the Chinese language began using punctuation. In fact, many ancient Chinese texts contain thousands of lines with no distinct punctuation marks or delimiters in sight. The lack of punctuation in such texts makes it difficult for humans to identify when there pauses or breaks between particular phrases and understand the semantic meaning of the written text (Mogahed, 2012). As a result, unless one was educated in the ancient time period, many readers of ancient Chinese would have significantly different interpretations of the texts. We propose an approach to predict the location (and type) of punctuation in ancient Chinese texts that extends the work of Oh et al (2017) by leveraging a bidirectional multi-layered LSTM with a multi-head attention mechanism as inspired by Luong et al.'s (2015) discussion of attention-based architectures. We find that the use of multi-layered LSTMs and multi-head attention significantly outperforms RNNs that don't incorporate such components when evaluating ancient Chinese texts.

The identification theory for causal effects in directed acyclic graphs (DAGs) with hidden variables is well-developed, but methods for estimating and inferring functionals beyond the g-formula remain limited. Previous studies have proposed semiparametric estimators for identifiable functionals in a broad class of DAGs with hidden variables. While demonstrating double robustness in some models, existing estimators face challenges, particularly with density estimation and numerical integration for continuous variables, and their estimates may fall outside the parameter space of the target estimand. Their asymptotic properties are also underexplored, especially when using flexible statistical and machine learning models for nuisance estimation. This study addresses these challenges by introducing novel one-step corrected plug-in and targeted minimum loss-based estimators of causal effects for a class of DAGs that extend classical back-door and front-door criteria (known as the treatment primal fixability criterion in prior literature). These estimators leverage machine learning to minimize modeling assumptions while ensuring key statistical properties such as asymptotic linearity, double robustness, efficiency, and staying within the bounds of the target parameter space. We establish conditions for nuisance functional estimates in terms of L2(P)-norms to achieve root-n consistent causal effect estimates. To facilitate practical application, we have developed the flexCausal package in R.

Engineering magnetism in layered magnets could result in novel phenomena related to two-dimensional (2D) magnetism, which can be useful for fundamental research and practical applications. Extensive doping efforts such as substitution and intercalation have been adopted to tune antiferromagnetic (AFM) properties in M2P2X6 compounds. The substitutional doping in this material family has mainly focused on bimetallic substitution. Recently, the metal substitution can also be extended to more than two metal elements, leading to medium and high-entropy alloys (MEAs and HEAs), which are fairly underexplored in layered magnetic systems including M2P2X6. In this work, we explored the magnetic properties of the previously unreported Cu- and Cr-substituted Ni2P2S6 i.e., CuxNi2(1-x)CrxP2S6. Our study reveals a relatively systematic evolution of AFM phases with substitution than that observed in traditional bimetallic substitution in M2P2X6. Furthermore, the Cu and Cr substitutions in Ni2P2S6 are found to enhance the ferromagnetic (FM) correlation, which is also accompanied by a possible weak FM phase at low temperatures for the intermediate compositions from 0.32 to 0.80. Our work provides a strategy to establish ferromagnetism in AFM M2P2X6 that can also be used for property tuning in other layered magnets.

This paper introduces a framework for estimating fair optimal predictions using machine learning where the notion of fairness can be quantified using path-specific causal effects. We use a recently developed approach based on Lagrange multipliers for infinite-dimensional functional estimation to derive closed-form solutions for constrained optimization based on mean squared error and cross-entropy risk criteria. The theoretical forms of the solutions are analyzed in detail and described as nuanced adjustments to the unconstrained minimizer. This analysis highlights important trade-offs between risk minimization and achieving fairnes. The theoretical solutions are also used as the basis for construction of flexible semiparametric estimation strategies for these nuisance components. We describe the robustness properties of our estimators in terms of achieving the optimal constrained risk, as well as in terms of controlling the value of the constraint. We study via simulation the impact of using robust estimators of pathway-specific effects to validate our theory. This work advances the discourse on algorithmic fairness by integrating complex causal considerations into model training, thus providing strategies for implementing fair models in real-world applications.

We examine the effect of nuclear deformation on the calculated $\beta$-decay half-lives of 55 neutron-rich nuclei. The deformation values were computed using DD-PC1 and DD-ME2 interactions in the Relativistic Hartree-Bogoliubov model. Yet another set of deformation was adopted from the Finite Range Droplet Model (FRDM). The model-dependent deformation values were used as a free parameter in the proton-neutron quasiparticle random phase approximation model to calculate the $\beta$-decay properties under terrestrial and stellar conditions. The Gamow-Teller strength distributions, branching ratios, half-lives and stellar weak rates of selected nuclei were later investigated. It was concluded that the $\beta$-decay properties of neutron-rich nuclei changed with nuclear deformation. The FRDM computed deformation values provided the best predictions for calculated half-lives followed by the DD-ME2 functional. The terrestrial $\beta$-decay half-lives changed up to 3 orders of magnitude and the stellar $\beta$-rates within a factor of 2 as the neutron-rich nuclei switched their geometrical configurations. For magic number nucleus $^{138}$Sn, the stellar rates changed substantially by more than 1 order of magnitude as the nucleus transitioned away from the spherical shape. Our investigation might prove useful for a realistic modeling of nucleosynthesis calculation., Comment: Total Pages = 37, Tables 3, Figures 17

Recently, layered materials transition metal thiophosphate MPX3 (M = transition metals, X = S or Se) have gained significant attention because of their rich magnetic, optical, and electronic properties. Specifically, the diverse magnetic structures and the robustness of magnetism in the two-dimensional limit have made them prominent candidates to study two-dimensional magnetism. Numerous efforts such as substitutions and interlayer intercalations have been made to tune the properties of these materials, which has greatly deepened the understanding of the underlying mechanisms that govern the properties. In this work, we focus on modifying the magnetism of Ising-type antiferromagnet FePS3 using electrochemical lithium intercalation. Our work unveils the effectiveness of electrochemical intercalation as a controllable tool to modulating magnetism, including tuning magnetic ordering temperature and inducing low temperature spin-glass state, offering an approach for implementing this material into applications.

Magnetotransport, the response of electrical conduction to external magnetic field, acts as an important tool to reveal fundamental concepts behind exotic phenomena and plays a key role in enabling spintronic applications. Magnetotransport is generally sensitive to magnetic field orientations. In contrast, efficient and isotropic modulation of electronic transport, which is useful in technology applications such as omnidirectional sensing, is rarely seen, especially for pristine crystals. Here we propose a strategy to realize extremely strong modulation of electron conduction by magnetic field which is independent of field direction. GdPS, a layered antiferromagnetic semiconductor with resistivity anisotropies, supports a field-driven insulator-to-metal transition with a paradoxically isotropic gigantic negative magnetoresistance insensitive to magnetic field orientations. This isotropic magnetoresistance originates from the combined effects of a near-zero spin-orbit coupling of Gd3+-based half-filling f-electron system and the strong on-site f-d exchange coupling in Gd atoms. Our results not only provide a novel material system with extraordinary magnetotransport that offers a missing block for antiferromagnet-based ultrafast and efficient spintronic devices, but also demonstrate the key ingredients for designing magnetic materials with desired transport properties for advanced functionalities., Comment: 44 pages, 18 figures

Magnetic topological semimetals LnSbTe (Ln = Lanthanide) have attracted intensive attention because of the presence of interplay between magnetism, topological, and electron correlations depending on the choices of magnetic Ln elements. Recently, varying Sb-Te composition has been found to effectively control the electronic and magnetic states in LnSbxTe$_{2-x}$. With this motivation, we report the evolution of magnetic properties with Sb-Te substitution in NdSb$_x$Te$_{2-x+\delta}$. Our work reveals the interesting non-monotonic change in magnetic ordering temperature with varying composition stoichiometry. In addition, reducing the Sb content x drives the reorientation of moments from in-plane (ab-plane) to out-of-plane (c-axis) direction that results in the distinct magnetic structures for two end compounds NdTe$_2$ ($x = 0$) and NdSbTe ($x = 1$). Furthermore, the moment orientation in NdSb$_x$Te$_{2-x+\delta}$ is also found to be strongly tunable upon application of weak magnetic field, leading to rich magnetic phases depending on the composition stoichiometry, temperature, and magnetic field. Such strong tuning of magnetism in this material establishes it as a promising platform for investigating tunable topological states and correlated topological physics., Comment: 35 pages, 5 figures

Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning. In these settings, predictive models are developed specifically to satisfy pre-defined notions of fairness. Here, we study the general problem of constrained statistical machine learning through a statistical functional lens. We consider learning a function-valued parameter of interest under the constraint that one or several pre-specified real-valued functional parameters equal zero or are otherwise bounded. We characterize the constrained functional parameter as the minimizer of a penalized risk criterion using a Lagrange multiplier formulation. We show that closed-form solutions for the optimal constrained parameter are often available, providing insight into mechanisms that drive fairness in predictive models. Our results also suggest natural estimators of the constrained parameter that can be constructed by combining estimates of unconstrained parameters of the data generating distribution. Thus, our estimation procedure for constructing fair machine learning algorithms can be applied in conjunction with any statistical learning approach and off-the-shelf software. We demonstrate the generality of our method by explicitly considering a number of examples of statistical fairness constraints and implementing the approach using several popular learning approaches.

Understanding the origin of hot carrier relaxation in nanowires (NWs) with one-dimensional (1D) geometry is significant for designing efficient hot carrier solar cells with such nanostructures. Here, we study the influence of Auger heating and Shockley-Read-Hall recombination on hot carrier dynamics of catalyst-free InGaAs-InAlAs core-shell NWs. Using steady-state and time-resolved photoluminescence (PL) spectroscopy the dependencies of hot carrier effects on the degree of confinement of photo-generated carriers induced by the nanowire diameter are determined at different lattice temperatures. Analysis of excitation-power dependent data and temperature-dependent PL linewidth broadening reveal that at low temperatures, strong Auger recombination and phonon-bottleneck are responsible for hot carrier effects. Our analysis gives also insights into electron-phonon and ionized impurity scattering, showing opposing trends with NW diameter, and it allows to estimate the Fr\"ohlich coupling constant for the InGaAs NWs. Conversely, with increasing lattice temperature, hot carrier relaxation rates increase due to enhanced Shockley-Read Hall and surface recombination. Time-resolved spectroscopy reveals a fourfold increase in the rate of Shockley-Read-Hall recombination from 6 ns at 10 K to 1.5 ns at 150 K. The findings suggest that minimizing defect densities in the bulk and surfaces of these NWs will be key to enhance hot carrier effects towards higher temperatures., Comment: 15 pages, 5 figures

Tuning magnetic properties in layered magnets is an important route to realize novel phenomenon related to two-dimensional (2D) magnetism. Recently, tuning antiferromagnetic (AFM) properties through substitution and intercalation techniques have been widely studied in MPX3 compounds. Interesting phenomena, such as diverse AFM structures and even the signatures of ferrimagnetism, have been reported. However, long-range ferromagnetic (FM) ordering has remained elusive. In this work, we explored the magnetic properties of the previously unreported Cr-substituted NiPS3. We found that Cr substitution is extremely efficient in controlling spin orientation in NiPS3. Our study reveals a field-induced spin polarization in lightly (9%) Cr-substituted NiPS3, which is likely attributed to the attenuation of AFM interactions and magnetic anisotropy due to Cr doping. Our work provides a possible strategy to achieve FM phase in AFM MPX3, which could be useful for investigating 2D magnetism as well as potential device applications.