arXiv subjects

Sai Li

Publications and source records attributed to Sai Li.

At least 19 recordsLinked to original sources

arXiv2026

Low Mach number limit for a compressible two-fluid model with algebraic closure and ill-prepared initial data in critical Besov spaces

In this paper, we study the low Mach number limit for a compressible two-fluid model with algebraic closure in the $d$-dimensional torus with $d \geq 2$. For large and ill-prepared initial data in critical Besov spaces, we prove that, provided that the Mach number is sufficiently small, the rescaled compressible two-fluid flow exists in critical Besov spaces for any finite time not exceeding the lifespan of the incompressible flow. Moreover, the rescaled compressible two-fluid flow converges to the incompressible Navier--Stokes flow as the Mach number tends to zero. The proof is based on a high-middle-low frequency analysis of the densities and velocity field, combined with a filtering technique involving wave operators. The main novelty is the derivation of new a priori estimates for the high-middle frequency part of the solution to the compressible two-fluid model, depending explicitly on time, the frequency parameter and the Mach number. To the best of our knowledge, this is the first work that proves (almost) global convergence for large and ill-prepared initial data in the low Mach number limit for compressible two-fluid model in critical framework.

math.AP↗

arXiv2026

BASC : Behavior-Aligned Quantization and Pruning for Low-Bit Spiking Neural Networks

Spiking Neural Networks (SNNs) encode information through binary spikes and compute in an event-driven manner, offering an energy-efficient paradigm for machine intelligence. However, high-performance SNNs incur substantial memory and timestep-wise computation costs that hinder deployment on resource-constrained devices. Quantization and pruning provide complementary routes to reducing these costs, yet both make their decisions with local criteria that overlook temporal task feedback in quantization and inter-channel dependencies in pruning. Consequently, optimizing either criterion can still yield suboptimal compression performance. We refer to this discrepancy as criterion-behavior mismatch and propose Behavior-Aligned SNN Compression (BASC), a unified framework with two lightweight modules. For quantization, the scale is applied to synaptic current at every timestep and therefore shifts spike timing. Temporal-Behavior Scale Correction (TSC) makes the scale learnable under a temporal loss, allowing firing behavior to inform scale optimization. For pruning, channel importance depends on how channels jointly drive the membrane potential across the firing threshold. Boundary-Level Inter-Channel Correction (BIC) uses channelwise importance scores for initial selection and inter-channel information to re-evaluate only channels near the pruning threshold. Extensive experiments on static and neuromorphic benchmarks show that lower-bit BASC models match or outperform higher-bit baselines and retain this accuracy advantage after structured pruning, while further reducing model storage and synaptic operations.

cs.NE↗

arXiv2026

Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation

Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, their use in transfer learning is limited by two obstacles: strict context-size constraints and sensitivity to distribution shifts between source and target tasks. Directly pooling heterogeneous source data can therefore lead to negative transfer. To address these challenges, we propose Context-Constrained Transfer Learning via ANchoring and DIstillation (TL-ANDI), a posterior-aware distillation framework for TFMs. TL-ANDI constructs a compact source context by solving a budget-constrained optimal transport problem whose cost jointly measures target covariate coverage and posterior compatibility. The selected anchor samples are then equipped with locally distilled labels and combined with a residual calibration step using target data.

stat.ML↗

arXiv2026

Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models

Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored. Unlike dense models, MoE architectures employ a router at each layer to assign each token to a sparse subset of experts. In this work, we observe that forget data often activates a small subset of experts disproportionately, while these experts may receive much weaker activation from retain data. This forget--retain routing mismatch can leave forget-critical experts under-regularized during unlearning. To address this, we propose \textbf{TRACE}, Targeted Routing-Aware Calibration of Experts, for MoE unlearning. TRACE first detects forget-critical experts from offline activation statistics, and then calibrates retain regularization by reweighting token-level retain losses so that each selected expert's retain-side activation frequency better matches its forget-side counterpart. Experiments on WMDP and MUSE-BOOKS across multiple MoE LLMs show that TRACE consistently improves the forget-utility trade-off, yielding a 9\% relative utility improvement over the strongest baseline under comparable forgetting quality and the best performance on three out of four MUSE-BOOKS metrics.

cs.CL↗

arXiv2026

Multi-flux Aharonov-Bohm caging with tunable couplings

Aharonov-Bohm (AB) caging is the complete wavefunction localization effect in translational-invariant lattices induced by destructive phase interference. These phases originate from the gauge fields such as the penetrated magnetic fields, which are directly related to several novel topological quantum states of matter. Recently, this effect has demonstrated significant potential for applications in quantum simulation and topological quantum computation. Here, we propose a scalable protocol to derive universal conditions for AB caging with multi-flux. The numerical simulations validate the theoretical predictions by directly observing AB caging phenomena. We also investigate the breakage of the caging effect with onsite detuning. Our protocol can be directly tested in several quantum many-body platforms and provides an alternative approach for advancing quantum simulation of exotic state matter.

quant-ph↗

arXiv2026

Uncertainty-Calibrated Recommendations for Low-Active Users

A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The key to this balance lies in quantifying model uncertainty, which approximates the risk of prediction errors and reveals the limits of the model's current knowledge. On large-scale short-video and livestream platforms, model uncertainty can warn of low-quality recommendations that may lead to disengagement of LAUs and at the same time identify opportunities to diversify content recommendation for HAUs. To leverage this dichotomy, we introduce a unified, production-ready framework that calibrates uncertainty to drive differentiated strategies. Specifically, we implement a model-uncertainty-based risk-averse deboosting policy for LAUs to suppress unreliable recommendations, while employing a risk-seeking Upper Confidence Bound (UCB) strategy for HAUs to encourage exploration. Validated on a major livestream platform, our framework demonstrates significant improvements in retention (active hours) and satisfaction (quality watch time ratio) for LAUs as well as remarkable increases in interest diversity and category coverage for HAUs, proving the value of uncertainty-aware recommendation in industrial settings.

cs.IR↗

arXiv2026

Efficient machine unlearning with minimax optimality

There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the field of machine unlearning, which aims to eliminate the influence of specific data subsets without the cost of full retraining. In this work, we propose a statistical framework for machine unlearning with generic loss functions and establish theoretical guarantees. For squared loss, especially, we develop Unlearning Least Squares (ULS) and establish its minimax optimality for estimating the model parameter of remaining data when only the pre-trained estimator, forget samples, and a small subsample of the remaining data are available. Our results reveal that the estimation error decomposes into an oracle term and an unlearning cost determined by the forget proportion and the forget model bias. We further establish asymptotically valid inference procedures without requiring full retraining. Numerical experiments and real-data applications demonstrate that the proposed method achieves performance close to retraining while requiring substantially less data access.

stat.ML↗

arXiv2026

Newly discovered Luminous blue variable candidates in M31 & M33

This study presents an investigation of nearly two dozen candidate Luminous Blue Variables (cLBVs) in the galaxies M31 and M33. Eight stars have been studied in detail, while an additional sixteen objects are briefly mentioned. Multi-epoch spectra of confirmed cLBVs from LAMOST and previous literature show broad hydrogen, He I lines, abundant Fe II and [Fe II] emission lines, and discernible spectral variability, consistent with the characteristics of known LBVs. Low outflow velocities inferred from P Cygni profiles are also incorporated into the classification criteria. Moreover, key stellar properties, including temperature and luminosity, are determined using the Spectral Energy Distribution (SED) fitting and spectral modeling. By comparison with stellar evolutionary tracks on the temperature luminosity diagram, the initial masses are estimated to be in the range of approximately 32 to 60 $M_{\odot}$. Except for J013401 and J013411, other stars locate within the typical LBV region between the S Doradus instability strip and their outburst phase. More importantly, our sample, except for the binary system, are all positioned in the LBVs region rather than that of B[e]SGs in the near-infrared color-color diagram. Based on all available information, one of the eight sources is confirmed as an LBV, four stars are designated as high-probability cLBVs, and the remaining three stars await further photometric observations to secure their classification. Given the current scarcity of known cLBVs, our study has the potential to make a significant increase in the number of LBVs in M31 and M33.

astro-ph.SR↗

arXiv2026

Low Mach number limit of the compressible Navier-Stokes system for large initial date with critical regularity on the torus

We study the low Mach number limit of the compressible Navier-Stokes equations on the torus. For large initial data with critical regularity, we prove that solutions to the compressible Navier-Stokes system exist as long as the corresponding solutions to the incompressible Navier-Stokes system exist, provided that the Mach number is sufficiently small. Furthermore, we establish the convergence of solutions of the compressible system to those of the incompressible system as the Mach number tends to zero. Our approach combines high-medium-low frequency analysis of density and velocity with the solution filtering technique via acoustic wave groups. This work provides an affirmative answer to the problem posed by Danchin [Amer.J.Math.,124(2002),1153-1219]:"Does convergence hold for large data with critical regularity?"

math.AP↗

arXiv2026

The low mach number limit of global solutions to the full compressible Navier-Stokes system in critical Besov spaces with large initial data

We are concerned with global existence of regular solutions to full compressible Navier-Stokes equations and their asymptotic behavior when the Mach number is sufficiently small. We establish global existence in critical Besov spaces for arbitrary large initial date provided that the divergence-free component of initial velocity and the difference between initial temperature and density generate a global regular solution to incompressible Boussinesq systems. Moreover, we rigorously justify the convergence to the incompressible model as the Mach number tends to zero. The proof relies on a fine-grained analysis of the high-middle-low frequencies of density, velocity and temperature. Our result can be seen as an improvement on Danchin and He [Math. Ann., 366 (2016), no. 3-4, pp. 1365-1402], including the extension from small initial data to large initial data and new convergence results which hold at the level of critical regularity.

math.AP↗

arXiv2026

Non-Abelian Aharonov-Bohm Caging in Synthetic Dimensions with a Trapped Ion

Aharonov-Bohm (AB) caging is a complete localization phenomenon in two-dimensional lattices due to destructive interference induced by the background gauge fields. However, current investigations of AB caging are mostly restricted to the Abelian gauge field case, and the observation of AB caging under non-Abelian gauge fields in a quantum system still remains elusive. Here, we report experimental realization of tunable synthetic non-Abelian SU(2) gauge fields in a rhombic lattice, engineered within the synthetic dimensions of a vibrating trapped ion with multiple levels. We realize AB caging under both Abelian and non-Abelian gauge fields and systematically investigate the distinctive transport properties of the non-Abelian case. In particular, we observe typical emergent quantum dynamics unique to non-Abelian AB caging, including initial-state-dependent dynamics, second-order effects, and asymmetric caging behavior. These observations demonstrate the trapped ion system as a powerful platform for simulating emergent phenomena in high-dimensional quantum systems with exotic synthetic gauge fields.

quant-ph↗

arXiv2026

Personalizing black-box models for nonparametric regression with minimax optimality

Recent advances in large-scale models, including deep neural networks and large language models, have substantially improved performance across a wide range of learning tasks. The widespread availability of such pre-trained models creates new opportunities for data-efficient statistical learning, provided they can be effectively integrated into downstream tasks. Motivated by this setting, we study few-shot personalization, where a pre-trained black-box model is adapted to a target domain using a limited number of samples. We develop a theoretical framework for few-shot personalization in nonparametric regression and propose algorithms that can incorporate a black-box pre-trained model into the regression procedure. We establish the minimax optimal rate for the personalization problem and show that the proposed method attains this rate. Our results clarify the statistical benefits of leveraging pre-trained models under sample scarcity and provide robustness guarantees when the pre-trained model is not informative. We illustrate the finite-sample performance of the methods through simulations and an application to the California housing dataset with several pre-trained models.

stat.ME↗

arXiv2025

An Ultra-Low Power and Fast Ising Machine using Voltage-Controlled Magnetoresistive Random Access Memory

Physics-inspired computing paradigms, such as Ising machines, are emerging as promising hardware alternatives to traditional von Neumann architectures for tackling computationally intensive combinatorial optimization problems (COPs). While quantum, optical, and electronic devices have garnered significant attention for their potential in realizing Ising machines, their translation into practical systems for industry-relevant applications remains challenging, with each approach facing specific limitations in power consumption and speed. To address this challenge, we report the first chip-level spintronic Ising machine using voltage-controlled magnetoresistive random access memory. The core of our design leverages magnetic tunnel junctions (MTJs) driven by the voltage-controlled magnetic anisotropy effect to realize the probabilistic update of Ising spins through a new mechanism. It enables a latency below 1 ns and an energy consumption under 40 fJ per spin update, achieving a 1000-times improvement over previous current-driven MTJ-based implementations. We map two real-world COPs in electronic design automation-global routing and layer assignment-onto the Ising model and demonstrate high-quality results with an energy efficiency of 25000 solutions per second per watt. This outperforms state-of-the-art quantum and graphics processing units by six and seven orders of magnitude, respectively. These results establish voltage-controlled spintronics as a compelling route towards next-generation physics-inspired machine intelligence, offering a paradigm for ultra-low-power, high-speed, and scalable computation.

physics.app-ph↗

arXiv2025

Experimental Proposal on Non-Abelian Aharonov-Bohm Caging Effect with a Single Trapped Ion

In the lattice system, when the synthetic flux reaches a $\pi$ phase along a closed loop under the synthetic gauge field, destructive interference occurs and gives rise to the localization phenomenon. This is known as the Aharonov-Bohm (AB) caging effect. It provides a powerful tool for the study of quantum transportation and dynamical effects. In the system where lattice sites possess internal structure and the underlying gauge field is non-Abelian, localization can also occur, forming the non-Abelian AB caging. Here, we propose an experimental scheme to synthesize non-Abelian gauge fields with a single trapped ion by coupling multiple internal levels and Fock states in its motion via laser fields. In contrast to the Abelian AB caging, we numerically observe that the non-Abelian AB caging occurs either when the interference matrix is nilpotent, or when the initial state is specifically set. Our experimental scheme broadens the study of localization phenomena and provides a novel tool for the study of non-Abelian physics.

quant-ph↗

arXiv2025

QUEST: A Quantized Energy-Aware SNN Training Framework for Multi-State Neuromorphic Devices

Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems.

physics.app-ph↗

arXiv2024

MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models

The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language interfaces offers a unified approach for expressing the capability differences of heterogeneous robots, facilitating communication between them, and enabling seamless task allocation and collaboration. Currently, the utilization of LLMs to achieve decentralized multi-heterogeneous robot collaborative tasks remains an under-explored area of research. In this paper, we introduce a novel framework that utilizes LLMs to achieve decentralized collaboration among multiple heterogeneous robots. Our framework supports three robot categories, mobile robots, manipulation robots, and mobile manipulation robots, working together to complete tasks such as exploration, transportation, and organization. We developed a rich set of textual feedback mechanisms and chain-of-thought (CoT) prompts to enhance task planning efficiency and overall system performance. The mobile manipulation robot can adjust its base position flexibly, ensuring optimal conditions for grasping tasks. The manipulation robot can comprehend task requirements, seek assistance when necessary, and handle objects appropriately. Meanwhile, the mobile robot can explore the environment extensively, map object locations, and communicate this information to the mobile manipulation robot, thus improving task execution efficiency. We evaluated the framework using PyBullet, creating scenarios with three different room layouts and three distinct operational tasks. We tested various LLM models and conducted ablation studies to assess the contributions of different modules. The experimental results confirm the effectiveness and necessity of our proposed framework.

cs.RO↗

arXiv2024

Discovery and inference of possibly bi-directional causal relationships with invalid instrumental variables

Learning causal relationships between pairs of complex traits from observational studies is of great interest across various scientific domains. However, most existing methods assume the absence of unmeasured confounding and restrict causal relationships between two traits to be uni-directional, which may be violated in real-world systems. In this paper, we address the challenge of causal discovery and effect inference for two traits while accounting for unmeasured confounding and potential feedback loops. By leveraging possibly invalid instrumental variables, we provide identification conditions for causal parameters in a model that allows for bi-directional relationships, and we also establish identifiability of the causal direction under the introduced conditions. Then we propose a data-driven procedure to detect the causal direction and provide inference results about causal effects along the identified direction. We show that our method consistently recovers the true direction and produces valid confidence intervals for the causal effect. We conduct extensive simulation studies to show that our proposal outperforms existing methods. We finally apply our method to analyze real data sets from UK Biobank.

stat.ME↗

arXiv2024

FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality

Machine learning methods often assume that the test data have the same distribution as the training data. However, this assumption may not hold due to multiple levels of heterogeneity in applications, raising issues in algorithmic fairness and domain generalization. In this work, we address the problem of fair and generalizable machine learning by invariant principles. We propose a training environment-based oracle, FAIRM, which has desirable fairness and domain generalization properties under a diversity-type condition. We then provide an empirical FAIRM with finite-sample theoretical guarantees under weak distributional assumptions. We then develop efficient algorithms to realize FAIRM in linear models and demonstrate the nonasymptotic performance with minimax optimality. We evaluate our method in numerical experiments with synthetic data and MNIST data and show that it outperforms its counterparts.

stat.ML↗