arXiv subjects

Xiaoguang Wang

Publications and source records attributed to Xiaoguang Wang.

At least 19 recordsLinked to original sources

arXiv2026

SoK: From Finding to Deployment: Systematizing the OS Kernel Bug Lifecycle

Automated kernel bug discovery has advanced rapidly. Continuous fuzzing and static analysis systems, such as syzbot, now expose Linux kernel bugs at a scale that downstream processes struggle to absorb. Yet a crash report is only the beginning. Before a bug is eliminated, it must be triaged, understood, patched, validated, reviewed, integrated, and often backported. These later stages remain far less automated, creating a persistent gap between bug discovery and patch deployment. This SoK systematizes the Linux kernel bug lifecycle from discovery to deployment. We organize prior work and production systems into five stages: discovery, triage, patch generation, patch validation, and integration. We explain the resulting automation gradient through kernel-specific challenges such as concurrency, implicit invariants, cross-syscall state, hardware dependence, lack of fault isolation, and architecture/configuration multiplicity. We further ground the analysis in a measurement of real syzbot-fixed bugs. The data shows that the crash-to-patch gap is not merely a backlog of unfixed reports but a structural failure mode of the repair pipeline: even after being fixed, bugs often remain open for weeks, require review-driven patch revisions, or lack reproducers that current repair and validation systems assume. This exposes a mismatch between where kernel-security automation is mature and where bug closure actually breaks down. These findings expose a deeper mismatch: today's repair and validation techniques often assume reliable reproducers, localized root causes, and checkable correctness oracles, yet these are precisely the artifacts missing from many real kernel bug reports. Closing the crash-to-patch gap, therefore, requires treating such artifacts as outputs to be produced, not prerequisites to be assumed.

cs.CR↗

arXiv2026

Boundary Time Crystals Beyond Mean-Field Theory: A Stroboscopic Rotating-Wave Approximation

Boundary time crystals are a class of exotic dissipative quantum phases that spontaneously break continuous time-translation symmetry in the thermodynamic limit of open quantum systems. In finite-size systems, the long-time evolution of boundary time crystals exhibits decaying oscillations that cannot be captured by widely used mean-field theory. To address this issue, we develop an effective approach called the stroboscopic rotating wave approximation, which provides a well-approximated state for the long-time evolution of boundary time crystals in the strongly driven regime. In this approach, the order parameter exhibits both a long-time decaying envelope governed by an effective Lindblad superoperator and short-time oscillations dominated by a reduced quantum dynamical semigroup. Our results reveal that the competition among dephasing processes along three directions induces persistent oscillations, marking the emergence of the boundary-time-crystal phase. We obtain the analytical expressions for the steady-state density operator, the oscillation period, and the decay rate of the order parameter when the coherent energy splitting exceeds the dissipation rate. Our work provides a beyond-mean-field tool for studying the dynamics of periodically driven open quantum systems and understanding the formation of time crystals.

quant-ph↗

arXiv2026

Capture components of cubic Siegel polynomials

Let $θ$ be an irrational number of bounded type. We prove that all capture components in the parameter space of cubic polynomials $f_a(z)=e^{2πiθ}z+a z^2+z^3$, where $a\in\mathbb{C}$, are Jordan domains.

math.CV↗

arXiv2026

Universal Scaling of the Minimum Error Probability in Qualification of Quantum States

Qualification of quantum states judges which of two sets of quantum states an unknown state lies in, where the two sets are labeled by two distinct parameter regions. We formulate this problem as a composite quantum hypothesis test and uncover universal scaling laws for the minimum error probability for $N$ copies. Taking polarization-direction qualification and purity qualification as examples, we show that the $N$-copy permutation symmetry and the geometric symmetries of the parameter regions identify the optimal measurements and the "worst pairwise states". The minimum error probability scales as $N^{-3/2}\exp(-Nξ)$ for disjoint regions and as $(NF)^{-1/2}$ for adjacent regions, where $ξ$ and $F$ are the quantum Chernoff divergence and quantum Fisher information associated with the "worst pairwise states", respectively. With the minimum error probability serving as an order parameter, the transition between the scaling behaviors becomes a second-order phase transition as $N\to\infty$. Our approach determines whether a quantum state belongs to a given set without full state tomography, thereby enabling qualification of large ensembles using finite samples.

quant-ph↗

arXiv2026

Quantum sensing of aging transitions

The aging transition is a critical phenomenon in which collective dynamics deteriorate as the fraction of inactive quantum nodes exceeds a threshold, referred to as the aging transition point. Such transitions are relevant to a broad range of biological and physiological systems, and may play an important role in quantum information processing, particularly in the stability assessment and robustness control of quantum networks. Detecting the aging transition point is therefore crucial for predicting network breakdown, since it marks the critical threshold at which a quantum network abruptly loses its stable active state and enters a degraded inactive phase. Here we propose a quantum sensing strategy to locate this transition point using a single qubit probe coherently coupled to a small subset of oscillator nodes. As the inactive fraction p approaches the aging transition point, the excited-state population of the probe becomes highly sensitive to variations in p, leading to a pronounced enhancement of the Fisher information. This critical enhancement enables high-precision estimation of the transition point. Remarkably, this enhancement survives even in the classical regime for the oscillators, where the Fisher information increases dramatically as p approaches the transition region. Our results establish a feasible route to sensing aging transitions in oscillator networks and provide a metrological perspective on critical phenomena in quantum many-body systems.

quant-ph↗

arXiv2026

MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing

The emerging collaborative information-based knowledge tracing (KT) has been a promising way to enhance modeling of learners' knowledge states. The core idea is to extract the collaborative information from interaction sequences of other learners to assist the prediction on the target one. Despite effectiveness, existing methods are built on the raw interaction sequences with tailored modules, which inevitably limits their capacity in deeply capturing learning behavioral patterns and generalization. To this end, we propose a general meta-behavioral pattern-aware framework (MBP-KT) for KT. Specifically, MBP-KT introduces a novel meta-behavioral sequence construction to transform the raw interaction sequences into the combinations of different meta-behavioral patterns. In this way, the learning behavioral patterns of learners can be effectively preserved. Then, MBP-KT develops a parameter-free module to extract the global collaborative representations from the constructed meta-behavioral sequences. Moreover, MBP-KT provides general injection strategies to introduce the extracted global collaborative information into various downstream KT models, ensuring the universality of the collaborative information. Extensive results on real-world datasets demonstrate that MBP-KT can consistently boosts the performance of a wide range of KT models.

cs.AI↗

arXiv2026

Beyond Crash-to-Patch: Patch Evolution for Linux Kernel Repair

Linux kernel bug repair is typically approached as a direct mapping from crash reports to code patches. In practice, however, kernel fixes undergo iterative revision on mailing lists before acceptance, with reviewer feedback shaping correctness, concurrency handling, and API compliance. This iterative refinement process encodes valuable repair knowledge that existing automated approaches overlook. We present a large-scale study of kernel patch evolution, reconstructing 6946 syzbot-linked bug-fix lifecycles that connect crash reports, reproducers, mailing-list discussions, revision histories, and merged fixes. Our analysis confirms that accepted repairs are frequently non-local and governed by reviewer-enforced constraints not present in bug reports. Building on these insights, we develop PatchAdvisor, a repair framework that integrates retrieval-based memory with a fine-tuned diagnostic advisor to guide a coding agent toward reviewer-aligned patches. Evaluation on temporally held-out syzbot cases demonstrates that leveraging patch-evolution history yields measurable gains in both reviewer-aligned refinement signals and end-to-end repair quality compared to unguided and retrieval-only baselines.

cs.SE↗

arXiv2026

Optimal Estimation of Temperature in Finite-sized System

Temperature of a finite-sized system fluctuates due to the thermal fluctuations. However, a systematic mathematical framework for measuring or estimating the temperature is still underdeveloped. Here, we incorporate the estimation theory in statistical inference to estimate the temperature of a finite-sized system and propose optimal estimation based on the uniform minimum variance unbiased estimation. Treating the finite-sized system as a thermometer measuring the temperature of a heat reservoir, we demonstrate that different optimal estimation of parameters yield different formulas of entropy, e.g., optimal estimation of inverse temperature (or temperature) aligns with the Boltzmann entropy (or Gibbs entropy). The optimal estimation leads to a achievable energy-temperature uncertainty relation and exhibits sample-size dependence, coinciding with their counterparts in nanothermodynamics. The achievable bound and the non-Gaussian distribution of temperature enable experimental testing in finite-sized systems.

cond-mat.stat-mech↗

arXiv2026

Continuity of Julia sets and invariant rays

For certain typical perturbations $(f_n)_n$ of a rational map $f$ with parabolic cycles, we investigate the relations between the Hausdorff convergence of Julia sets and invariant rays, and the horocyclic convergence of multipliers of periodic points. We establish several equivalent characterizations by means of parabolic implosion theory. This builds upon an analysis of the edge dynamics on the tree for the gate structure induced by the perturbation. The edge dynamics which are driven by Oudkerk's algorithm, are used to trace the orbits for the near parabolic perturbations.

math.DS↗

arXiv2026

Quantum circuit complexity and unsupervised machine learning of topological order

Inspired by the close relationship between Kolmogorov complexity and unsupervised machine learning, we explore quantum circuit complexity, an important concept in quantum computation and quantum information science, as a pivot to understand and to build interpretable and efficient unsupervised machine learning for topological order in quantum many-body systems. We argue that Nielsen's quantum circuit complexity represents an intrinsic topological distance between topological quantum many-body phases of matter, and as such plays a central role in interpretable manifold learning of topological order. To span a bridge from conceptual power to practical applicability, we present two theorems that connect Nielsen's quantum circuit complexity for the quantum path planning between two arbitrary quantum many-body states with quantum Fisher complexity (Bures distance) and entanglement generation, respectively. Leveraging these connections, fidelity-based and entanglement-based similarity measures or kernels, which are more practical for implementation, are formulated. Using the two proposed distance measures, unsupervised manifold learning of quantum phases of the bond-alternating XXZ spin chain, the ground state of Kitaev's toric code and random product states, is conducted, demonstrating their superior performance. Moreover, we find that the entanglement-based approach, which captures the long-range structure of quantum entanglement of topological orders, is more robust to local Haar random noises. Relations with classical shadow tomography and shadow kernel learning are also discussed, where the latter can be naturally understood from our approach. Our results establish connections between key concepts and tools of quantum circuit computation, quantum complexity, quantum metrology, and machine learning of topological quantum order.

quant-ph↗

arXiv2025

Universal Sensitivity Bound for Thermal Quantum Dynamic Sensing

This work unifies the equilibrium and non-equilibrium frameworks of quantum metrology within the context of many-body systems. We investigate dynamic sensing schemes to derive an upper bound on the quantum Fisher information for probe states in thermal equilibrium with their environment. We establish that the dynamic quantum Fisher information for a thermal probe state is upper bounded by the degree of non-commutation between the transformed local generator and the Hamiltonian for the thermal state. Furthermore, we show that this upper bound scales as the square of the product of the inverse temperature and the evolution time. In the low-temperature limit, we establish an additional upper bound expressed as the seminorm of the commutator divided by the energy gap. We apply this thermal dynamic sensing scheme to various models, demonstrating that the dynamic quantum Fisher information satisfies the established upper bounds.

quant-ph↗

arXiv2025

Mixed-State Berry Curvature in quantum multiparameter estimations

For pure states, the quantum Berry curvature was well studied. However, the quantum curvature for mixed states has received less attention. From the concept of symmetric logarithmic derivative, we introduce a mixed-state quantum curvature and find that it plays a key role in the field of multi-parameter precision estimations. Through spectral decomposition, we derive the mixed-state Berry curvature for both the full-rank and non-full-rank density matrices. As an example, we obtain the exact expression of the Berry curvature for an arbitrary qubit state.

quant-ph↗

arXiv2025

Boundary of the central hyperbolic component II: boundary extension theorem

In this paper, we study the boundary behavior of Milnor's parameterization $Φ: \mathcal B_d\rightarrow \mathcal H_d$ of the central hyperbolic component $\mathcal H_d$ via Blaschke products. We establish a boundary extension theorem by giving a necessary and sufficient condition for $D\in \partial \mathcal B_d$ which allows $Φ$-extension. Further we show that cusps are dense in a full Hausdorff dimensional subset of $\partial \mathcal H_d$, partially confirming a conjecture of McMullen.

math.DS↗

arXiv2025

Strongly tilted field induced fractional quantized-drift in non-interacting system

Fractional quantized response appears to be a distinctive characteristic in interacting topological systems. Here, we discover a novel phenomenon of tilt-induced fractional quantize drift in non-interacting system constructed by a time-modulated superlattice subjected to a external time-independent gradient potential. Depending on the tilt strength, Rabi oscillations between adjacent lowest enegy bands caused by Landau-Zener tunneling, can induce that the one-cycle-averaged drift displacement is fraction, which is relate to the ratio of the sum of Chern numbers of multiple bands to the number of energy bands involved in Landau Zener tunneling. As representative examples, we construct fractional (1/3, 1/2) quantize drift only via adjusting period of lattice. The numerical simulations allow us to consider a realistic setup amenable of an experimental realization. Our findings will expand the research implications of both fractional quantize response and topological materials.

cond-mat.quant-gas↗

arXiv2025

Multistable polar textures in geometrically frustrated nematic liquid crystals

The ability to manipulate polar entities with multiple external fields opens exciting possibilities for emerging functionalities and novel applications in spin systems, photonics, metamaterials, and soft matter. Liquid crystals (LCs), exhibiting both a crystalline structure and liquid fluidity, represent a promising platform for manipulating phases with polar molecular order, notably ferroelectric ones. However, achieving a polar symmetry is challenging with rod-shaped LC molecules, which form predominantly apolar nematic phases. We report an approach in which a geometric lattice confinement of nematic LCs is used to induce planar polar order on the scale of a mesoscopic metamaterial. We confine the nematic LC in a micropillar array, forming topological defect-pillar pairs of elastic dipoles with a free top interface in contact with an immiscible fluid. The resulting dipole lattice configurations can be programmed rheologically by flowing the top fluid and maintained even after flow cessation, a phenomenon attributed to orientational multistability of the dipoles. This multi-memory effect enables the encoding and reconfiguration of directional information. Overall, this research establishes a foundational understanding of topological dipoles under confinement and shear flow, enabling the detection, tracking, and recording of flow profiles and paving the way for future advances in soft matter physics and stimuli-responsive materials.

cond-mat.soft↗

arXiv2025

Rejoining fragmented ancient bamboo slips with physics-driven deep learning

Bamboo slips are a crucial medium for recording ancient civilizations in East Asia, and offers invaluable archaeological insights for reconstructing the Silk Road, studying material culture exchanges, and global history. However, many excavated bamboo slips have been fragmented into thousands of irregular pieces, making their rejoining a vital yet challenging step for understanding their content. Here we introduce WisePanda, a physics-driven deep learning framework designed to rejoin fragmented bamboo slips. Based on the physics of fracture and material deterioration, WisePanda automatically generates synthetic training data that captures the physical properties of bamboo fragmentations. This approach enables the training of a matching network without requiring manually paired samples, providing ranked suggestions to facilitate the rejoining process. Compared to the leading curve matching method, WisePanda increases Top-50 matching accuracy from 36% to 52% among more than one thousand candidate fragments. Archaeologists using WisePanda have experienced substantial efficiency improvements (approximately 20 times faster) when rejoining fragmented bamboo slips. This research demonstrates that incorporating physical principles into deep learning models can significantly enhance their performance, transforming how archaeologists restore and study fragmented artifacts. WisePanda provides a new paradigm for addressing data scarcity in ancient artifact restoration through physics-driven machine learning.

cs.CV↗

arXiv2025

Boundary of the central hyperbolic component I: dynamical properties

We study the dynamics of polynomial maps on the boundary of the central hyperbolic component $\mathcal H_d$. We prove the local connectivity of Julia sets and a rigidity theorem for maps on the regular part of $\partial\mathcal H_d$. Our proof is based on the construction of Fatou trees and employs the puzzle technique as a key methodological framework. These results are applicable to a larger class of maps for which the maximal Fatou trees equal the filled Julia sets.

math.DS↗

arXiv2025

Boundaries of the bounded hyperbolic components of polynomials

In this paper, we study the local connectivity and Hausdorff dimension for the boundaries of the bounded hyperbolic components in the space $\mathcal P_d$ of polynomials of degree $d\geq 3$. It is shown that for any non disjoint-type bounded hyperbolic component $\mathcal H\subset \mathcal P_d$, the locally connected part of $\partial\mathcal H$, along each regular boundary strata, has full Hausdorff dimension $2d-2$. An essential innovation in our argument involves analyzing how the canonical parameterization of the hyperbolic component--realized via Blaschke products over a mapping scheme--extends to the boundary. This framework allows us to study three key aspects of $\partial \mathcal H$: the local connectivity structure, the perturbation behavior, and the local Hausdorff dimensions.

math.DS↗