arXiv subjects

Ho-Kin Tang

Publications and source records attributed to Ho-Kin Tang.

At least 19 recordsLinked to original sources

arXiv2026

Orbital-Selective Mott and Antiferromagnetic Phases in Diagonally Compressed Kagome Lattice

We perform determinant quantum Monte Carlo simulations of the half-filled Hubbard model on a diagonally compressed kagome lattice, introducing exponential decay long-range hopping $t(r) = t_0 \exp\bigl(-r / r_0\bigr)$ to account for the evolving bond length. By varying the lattice angle $\theta$ and the on-site interaction $U$, double occupancy, charge compressibility, and spin-spin correlation functions of the whole system and each sub-lattice are measured. We find that geometric compression induces a clear sublattice differentiation: for $\theta\gtrsim52^\circ$, the A sublattice establishes long-range hoppings, which in turn suppresses the metallic behavior of the $B/C$ sublattice and drives a selective Mott transition; for $\theta\lesssim52^\circ$, the $B$-$C$ chains develop long-range antiferromagnetic correlations within the finite-size simulations, which in turn suppresses the metallic behavior of the $A$ sublattice and drives a selective Mott transition. The critical interaction $U^c_A$ for the $A$ sites decreases sharply near the onset of $B$-$C$ antiferromagnetic correlations, while $U^c_{B/C}$ increases. These competing orders give rise to an orbital-selective Mott phase and a rich $U$-$\theta$ phase diagram featuring paramagnetic-metal, paramagnetic-Mott, antiferromagnetic-metal, and antiferromagnetic-Mott states. Our results highlight the complex interplay between lattice geometry, magnetic frustration, and strong correlations in frustrated two-dimensional systems.

cond-mat.str-el↗

arXiv2026

Altermagnetic-doping interplay as a route to enhanced d-wave pairing in the Hubbard model

Altermagnets - collinear, zero-net-moment magnets with momentum-odd spin splitting protected by crystalline symmetries - offer a tunable route to suppress long-range antiferromagnetism while preserving strong short-range spin fluctuations. We show that this environment robustly stabilizes unconventional superconductivity and naturally produces mixed-symmetry pairing. Through a strong-coupling analysis of a spin-anisotropic Hubbard model, we derive an anisotropic t-J model where exchange interactions cooperatively enhance singlet d-wave pairing and promote triplet p-wave pairing. Our mean-field analysis reveals a pairing evolution driven by altermagnetic anisotropy: for small spin anisotropy, the d-wave channel is enhanced, closely resembling the dominant pairing symmetry in cuprate superconductors, which suggests that weak spin anisotropy may be an essential ingredient in realistic models of these materials. Constrained-path quantum Monte Carlo simulations confirm this picture, showing a regime where dominant d-wave correlations coexist with an emergent p-wave component near optimal doping. As spin anisotropy increases, strong C2 anisotropy and spin splitting activate the triplet channel, leading to a stable d+p mixed-pairing state. This synergistic state exhibits significantly enhanced overall pairing strength, suggesting the possibility of a higher superconducting transition temperature.

cond-mat.supr-con↗

arXiv2026

Evolution of magnetic correlation in doped Hubbard model with altermagnetic spin splitting

The evolution of magnetic correlation in strongly correlated electron systems with altermagentic spin splitting remains largely unexplored. Here we investigate how spin splitting generated by spin-dependent next-nearest-neighbor hopping t' reshapes the Fermi surface nesting and van Hove singularities in the two-dimensional square-lattice Hubbard model, leading evolution of magnetic instabilities. Using the constrained-path quantum Monte Carlo method, we find the dominant magnetic correlation as functions of the filling and t'/t by computing the momentum-resolved spin structure factor. The analysis reveals a transition from antiferromagnetic ({\pi},{\pi}) order in the isotropic, half-filled system to non-collinear spiral ({\pi},q) order upon increasing the spin-dependent anisotropy or doping away from half-filling, ultimately entering a short-range correlation regime where stripe and spiral correlation coexist. These findings highlight a possible route to realizing spiral correlation in altermagnetic systems, potentially providing a platform for spintronic devices that exploit non-collinear spin textures.

cond-mat.str-el↗

arXiv2026

Oriented Triplet $p$-Wave Pairing from Fermi surface Anisotropy and Nonlocal Attraction

Using constrained-path quantum Monte Carlo, we map the ground-state phase diagram versus the nearest-neighbor (NN) attraction $V$ and spin-dependent hopping anisotropy $\alpha$ for the two-dimensional attractive $t$--$U$--$V$ Hubbard model at filling $n\simeq0.85$. We identify an onsite $s$-wave superfluid, a Cooper pair Bose metal with an uncondensed Bose surface, and an oriented equal-spin triplet $p$-wave pairing phase. The NN attraction activates the odd-parity channel, while hopping anisotropy suppresses the competing $s$-wave coherence and selects a $p_x/p_y$ polar axis, and thus lowers the critical $|V_c|$ for the onset of triplet-dominant $p$-wave pairing. A channel-resolved Landau analysis provides a criterion for the Landau $p$-wave scale $V_c^{\mathrm L}(\alpha)$, consistent with the observed anisotropy dependence of $|V_c|$. Our results establish how NN interaction and Fermi surface anisotropy cooperate to generate the oriented triplet $p$-wave pairing, and suggest that cold-atom and altermagnetic platforms could potentially realize this mechanism.

cond-mat.supr-con↗

arXiv2025

Control of localized states of itinerant electrons and their magnetic interactions

Controlling the magnetic properties of nanosystems by an electric field offers a number of advantages for spintronics applications. Using the noncollinear Alexander-Anderson model, we have shown that the interaction of localized magnetic moments formed by itinerant electrons strongly depends on the position of the d-level relative to the Fermi level, which determines the number of localized electrons. Depending on this parameter, the ground state of the magnetic dimer can be ferromagnetic, antiferromagnetic, or noncollinear without the effects of spin-orbit interaction. The magnetic state can be controlled by shifting the d-level with an electric field, even without current flow. For a sufficiently large value of the hopping parameter between localized states there can be several self-consistent solutions with different values of magnetic moments. This opens new possibilities for manipulation of the magnetic structure of nanosystems. The results obtained lead to a new interpretation of the mechanisms of magnetization reversal, recording, and deleting of magnetic structures in tunneling spectroscopy experiments.

physics.comp-ph↗

arXiv2025

Quantum Filtering and Stabilization of Dissipative Quantum Systems via Augmented Neural Ordinary Differential Equations

Modeling open quantum dynamics without full knowledge of the system Hamiltonian or noise model is a key challenge in quantum control and quantum state estimation. We introduce an Augmented Quantum Neural Ordinary Differential Equation (AQNODE) framework that learns quantum trajectories and dissipation parameters directly from partial continuous measurement data. By embedding the system into a latent space evolved via neural ODEs, AQNODE captures both observable and hidden non-Markovian dynamics with temporal smoothness and physical consistency. Our approach integrates weak measurement data to reconstruct qubit states and time-dependent decoherence rates, enabling accurate state prediction and parameter inference without explicit physical equations. Furthermore, we incorporate AQNODE-based feedback control techniques, including proportional-derivative and time-varying linear-quadratic regulator (LQR) strategies, to steer the quantum system toward target states in real time. Extensive numerical simulations demonstrate AQNODE's ability to generalize across system configurations, achieve low prediction errors, and perform robust quantum filtering and control. These results establish AQNODE as a scalable, differentiable, and experimentally compatible framework for real-time modeling and control of dissipative quantum systems.

quant-ph↗

arXiv2025

Crossover in the Ordered Phase in the Non-Mermin-Wagner-Hohenberg Regime of Spin Models with Long-Range Coupling

Continuous spin models with long-range interactions of the form $r^{-\sigma}$, where $r$ is the distance between two spins and $\sigma$ controls the decay of the interaction, exhibit enhanced order that competes with thermal disturbances, leading to a richer variety of phases and types of phase transitions. In-depth research in this area not only aids in comprehending the complex behaviors in theoretical models but also provides valuable insights into the diverse phase transitions observed in real materials. Here, we identify that the true long-range ordered phase encompasses distinct scaling regimes, which we term Enhance Long-Range Ordered (EnLRO) and Reduce Long-Range Ordered (ReLRO) regimes. In the one-dimensional XY model, the crossover from EnLRO to ReLRO regimes occurs around $\sigma \approx 1.575$, while in two dimensions, the crossover happens near $\sigma \approx 3.2$. Applying finite-size scaling analysis, we extract the critical exponents that characterize the order-to-disorder phase transitions in the EnLRO and ReLRO regimes, constructing comprehensive phase diagrams. The analysis is further extended to the 1D and 2D long-range Heisenberg models, where we find the EnLRO-ReLRO crossover at $\sigma \approx 1.575$ and $\sigma \approx 3.22$, respectively. The similar crossover points suggest that the distinction between EnLRO and ReLRO regimes is a generic feature in continuous spin models with long-range interactions.

cond-mat.stat-mech↗

arXiv2025

Variation of Bose surface by Filling in Cooper pair Bose metal

The Cooper pair Bose metal (CPBM) is a non-superfluid quantum phase in which uncondensed fermion pairs form a "Bose surface" in momentum space. We investigate the CPBM in the two-dimensional spin-anisotropic attractive Hubbard model by tuning the next-nearest-neighbor (NNN) hopping t', carrier filling n, and spin anisotropy alpha, using large-scale constrained-path quantum Monte Carlo simulations. A moderate NNN hopping (t'/t = 0.2) substantially enlarges the CPBM region: the phase extends into weaker anisotropy regimes and coexists with a commensurate charge-density wave (CDW) near half-filling (n > 0.95), where CDW order would otherwise dominate at t' = 0. Interestingly, t' suppresses the overall CDW peak amplitude and introduces a geometric correlation between the orientations of the Fermi and Bose surfaces: for weak Fermi-surface rotations, the Bose surface remains aligned with the lattice axes, while larger distortions drive both surfaces to rotate in tandem. Momentum-resolved pairing distributions reveal that the bosonic pairing channels are jointly controlled by t' and carrier filling n. For small t', d_xy-wave correlations dominate across the entire filling range. In contrast, for larger t', the dominant pairing symmetry varies with n, reflecting a nontrivial interplay between frustration and density. These findings establish carrier filling and NNN hopping as complementary levers for manipulating CPBM stability and provide concrete criteria for identifying non-superfluid bosonic matter in cold-atom and correlated-electron systems.

physics.comp-ph↗

arXiv2025

Knowledge Fusion of Large Language Models Via Modular SkillPacks

Cross-capability transfer is a key challenge in large language model (LLM) research, with applications in multi-task integration, model compression, and continual learning. Recent works like FuseLLM and FuseChat have demonstrated the potential of transferring multiple model capabilities to lightweight models, enhancing adaptability and efficiency, which motivates our investigation into more efficient cross-capability transfer methods. However, existing approaches primarily focus on small, homogeneous models, limiting their applicability. For large, heterogeneous models, knowledge distillation with full-parameter fine-tuning often overlooks the student model's intrinsic capacity and risks catastrophic forgetting, while PEFT methods struggle to effectively absorb knowledge from source LLMs. To address these issues, we introduce GraftLLM, a novel method that stores source model capabilities in a target model with SkillPack format. This approach preserves general capabilities, reduces parameter conflicts, and supports forget-free continual learning and model fusion. We employ a module-aware adaptive compression strategy to compress parameter updates, ensuring efficient storage while maintaining task-specific knowledge. The resulting SkillPack serves as a compact and transferable knowledge carrier, ideal for heterogeneous model fusion and continual learning. Experiments across various scenarios demonstrate that GraftLLM outperforms existing techniques in knowledge transfer, knowledge fusion, and forget-free learning, providing a scalable and efficient solution for cross-capability transfer. The code is publicly available at: https://github.com/duguodong7/GraftLLM.

cs.AI↗

arXiv2025

Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer

Foundation models and their checkpoints have significantly advanced deep learning, boosting performance across various applications. However, fine-tuned models often struggle outside their specific domains and exhibit considerable redundancy. Recent studies suggest that combining a pruned fine-tuned model with the original pre-trained model can mitigate forgetting, reduce interference when merging model parameters across tasks, and improve compression efficiency. In this context, developing an effective pruning strategy for fine-tuned models is crucial. Leveraging the advantages of the task vector mechanism, we preprocess fine-tuned models by calculating the differences between them and the original model. Recognizing that different task vector subspaces contribute variably to model performance, we introduce a novel method called Neural Parameter Search (NPS-Pruning) for slimming down fine-tuned models. This method enhances pruning efficiency by searching through neural parameters of task vectors within low-rank subspaces. Our method has three key applications: enhancing knowledge transfer through pairwise model interpolation, facilitating effective knowledge fusion via model merging, and enabling the deployment of compressed models that retain near-original performance while significantly reducing storage costs. Extensive experiments across vision, NLP, and multi-modal benchmarks demonstrate the effectiveness and robustness of our approach, resulting in substantial performance gains. The code is publicly available at: https://github.com/duguodong7/NPS-Pruning.

cs.LG↗

arXiv2025

Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling

Fine-tuning Large Language Models (LLMs) with multimodal encoders on modality-specific data expands the modalities that LLMs can handle, leading to the formation of Multimodal LLMs (MLLMs). However, this paradigm heavily relies on resource-intensive and inflexible fine-tuning from scratch with new multimodal data. In this paper, we propose MMER (Multi-modality Expansion and Retention), a training-free approach that integrates existing MLLMs for effective multimodal expansion while retaining their original performance. Specifically, MMER reuses MLLMs' multimodal encoders while merging their LLM parameters. By comparing original and merged LLM parameters, MMER generates binary masks to approximately separate LLM parameters for each modality. These decoupled parameters can independently process modality-specific inputs, reducing parameter conflicts and preserving original MLLMs' fidelity. MMER can also mitigate catastrophic forgetting by applying a similar process to MLLMs fine-tuned on new tasks. Extensive experiments show significant improvements over baselines, proving that MMER effectively expands LLMs' multimodal capabilities while retaining 99% of the original performance, and also markedly mitigates catastrophic forgetting.

cs.CL↗

arXiv2025

Enhancement of d-wave Pairing in Strongly Correlated Altermagnet

Altermagnetism, featuring momentum-dependent spin splitting without net magnetization, has attracted a growing interest for spintronics. We study a Fermi Hubbard model with altermagnetic order arising from the spin-anisotropic hopping near half-filling using constrained-path quantum Monte Carlo. Spin-dependent hopping breaks SU(2) symmetry and disrupts Fermi surface nesting, giving rise to an altermagnetic state with momentum-space spin splitting but no net magnetization. We find that increasing anisotropy suppresses long-range antiferromagnetic order and significantly enhances effective $d$-wave pairing correlations. Our results demonstrate a doping-free route to unconventional superconductivity mediated by short-range spin fluctuations in an altermagnetic background.

cond-mat.str-el↗

arXiv2025

To See a World in a Spark of Neuron: Disentangling Multi-task Interference for Training-free Model Merging

Fine-tuning pre-trained models on targeted datasets enhances task-specific performance but often comes at the expense of generalization. Model merging techniques, which integrate multiple fine-tuned models into a single multi-task model through task arithmetic, offer a promising solution. However, task interference remains a fundamental challenge, leading to performance degradation and suboptimal merged models. Existing approaches largely overlooked the fundamental roles of neurons, their connectivity, and activation, resulting in a merging process and a merged model that does not consider how neurons relay and process information. In this work, we present the first study that relies on neuronal mechanisms for model merging. Specifically, we decomposed task-specific representations into two complementary neuronal subspaces that regulate input sensitivity and task adaptability. Leveraging this decomposition, we introduced NeuroMerging, a novel merging framework developed to mitigate task interference within neuronal subspaces, enabling training-free model fusion across diverse tasks. Through extensive experiments, we demonstrated that NeuroMerging achieved superior performance compared to existing methods on multi-task benchmarks across both natural language and vision domains. Our findings highlighted the importance of aligning neuronal mechanisms in model merging, offering new insights into mitigating task interference and improving knowledge fusion. Our project is available at https://ZzzitaoFang.github.io/projects/NeuroMerging/.

cs.LG↗

arXiv2025

Pairing phase diagram for electron-doped cuprates in the square-lattice $t-U-V$ Hubbard model

Motivated by significant discrepancies between experimental observations of electron-doped cuprates and numerical results of the Hubbard model, we investigate the role of nearest-neighbor (NN) electron interactions $V$ by studying the $t-U-V$ model on square lattices. Upon doping $\delta$= 0.153, by using constrained path quantum Monte Carlo (CPQMC) method, we find that NN electron attraction $V$ can notably drive an exotic $p$-wave spin-triplet pairing, while the NN electron repulsion $V$ will suppress the $d_{x^2-y^2}$-wave ($d$-wave) pairing and triggers the $d_{xy}$-wave pairing. Especially in the intermediate coupling regime, as NN repulsion increases, the intensity of $d_{xy}$-wave pairing also increases, further suppressing the presence of $d$-wave pairing, which may help explain the notable suppression of $d$-wave pairing in electron-doped cuprate superconductors. Besides the pairing phase, we also find that the NN electron attraction $V$ has no significant effect on spin density wave (SDW) and charge density wave (CDW), but repulsion $V$ significantly enhanced CDW and suppressed SDW. Our study suggests the $t-U-V$ Hubbard model can serve as the minimal model to capture the essential physics of the electron-doped cuprates.

cond-mat.supr-con↗

arXiv2024

Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning

The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using the K-nearest neighbors algorithm. Two training strategies are explored: one based on experimental data, and the other based on the offset between molecular dynamics (MD) simulations and experimental measurements. The latter approach yields a substantial improvement in predictive accuracy, achieving a mean unsigned error (MUE) of 0.64 kcal/mol. Feature analysis identifies molecular geometry and topology as the most critical factors in predicting alchemical free energy, supporting the established theory that surface tension is a key determinant. Furthermore, the feature analysis of offset results highlights the relevance of charge distribution within the system, which correlates with the inaccuracies in force fields employed in MD simulations and may provide guidance for improving force field designs. These results suggest that machine learning approaches can effectively capture the complex features governing solvation free energy, offering novel pathways for enhancing predictive accuracy.

physics.chem-ph↗

arXiv2024

Efficient sampling using Macrocanonical Monte Carlo and density of states mapping

In the context of Monte Carlo sampling for lattice models, the complexity of the energy landscape often leads to Markov chains being trapped in local optima, thereby increasing the correlation between samples and reducing sampling efficiency. This study proposes a Monte Carlo algorithm that effectively addresses the irregularities of the energy landscape through the introduction of the estimated density of states. This algorithm enhances the accuracy in the study of phase transitions and is not model-specific. Although our algorithm is primarily demonstrated on the two-dimensional square lattice model, the method is also applicable to a broader range of lattice and higher-dimensional models. Furthermore, the study develops a method for estimating the density of states of large systems based on that of smaller systems, enabling high-precision density of states estimation within specific energy intervals in large systems without sampling. For regions of lower precision, a re-weighting strategy is employed to adjust the density of states to enhance the precision further. This algorithm is not only significant within the field of lattice model sampling but may also inspire applications of the Monte Carlo method in other domains.

cond-mat.stat-mech↗

arXiv2024

Parameter Competition Balancing for Model Merging

While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model. This strategy promotes multitasking capabilities without requiring retraining on the original datasets. However, existing methods fall short in addressing potential conflicts and complex correlations between tasks, especially in parameter-level adjustments, posing a challenge in effectively balancing parameter competition across various tasks. This paper introduces an innovative technique named PCB-Merging (Parameter Competition Balancing), a lightweight and training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging employs intra-balancing to gauge parameter significance within individual tasks and inter-balancing to assess parameter similarities across different tasks. Parameters with low importance scores are dropped, and the remaining ones are rescaled to form the final merged model. We assessed our approach in diverse merging scenarios, including cross-task, cross-domain, and cross-training configurations, as well as out-of-domain generalization. The experimental results reveal that our approach achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models, outperforming existing model merging methods. The code is publicly available at: \url{https://github.com/duguodong7/pcb-merging}.

cs.CV↗

arXiv2024

Variation of Electron-electron interaction in pyrochlore structures

We conduct a comprehensive \textit{ab initio} investigation of electron-electron interactions within the pyrochlore structures of R$_2$Ru$_2$O$_7$, R$_2$Ir$_2$O$_7$, Ca$_2$Ru$_2$O$_7$, and Cd$_2$Ru$_2$O$_7$, where R denotes a rare-earth element. Utilizing a multiorbital Hubbard model, we systematically explore the effects of various rare-earth elements and applied high pressure on the correlation strength in these compounds. Our calculations on the Coulomb interaction parameter $U$ and the bandwidth $W$ reveal that the chemical pressure for R$_2$Ru$_2$O$_7$ and R$_2$Ir$_2$O$_7$ leads to an unusual increase in $U/W$ ratio, hence, increase in correlation strength. Contrary to conventional understanding of bandwidth control, our study identifies that the Hubbard $U$ is more influential than the bandwidth $W$ behind the metal-insulator landscape of R$_2$Ru$_2$O$_7$ and R$_2$Ir$_2$O$_7$, leading to an interaction-controlled metal-insulator transition. We also find unexpected behavior in physical pressure. Whereas physical pressure leads to a decrease in the correlation strength $U/W$ as usual in R$_2$Ru$_2$O$_7$, the effect is notably small in Ca$_2$Ru$_2$O$_7$ and Cd$_2$Ru$_2$O$_7$, which provides an important clue to understanding unusual pressure-induced metal-insulator transition observed experimentally.

cond-mat.str-el↗