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Fei Yang

Publications and source records attributed to Fei Yang.

At least 19 recordsLinked to original sources
arXiv2026

Polar nanoregions and reentrant-like ferroelectric behavior in SrTiO$_3$

Recent real-space imaging in quantum paraelectric SrTiO$_3$ [Nature 656, 54 (2026)] reveals that local polar textures do not continuously grow upon cooling, but reach a maximum intensity at intermediate temperatures around 60-65~K and weaken again toward the quantum paraelectric ground state. Such a reentrant-like weakening of local polar textures challenges the conventional paradigm in which ordering tendencies generally strengthen as thermal fluctuations are suppressed. Here we employ a self-consistent phase-field theory showing that this anomalous behavior naturally arises from the interplay between intrinsic polar and antiferrodistortiv (AFD) fluctuations. We demonstrate that flexoelectric-like coupling strongly hybridizes the polar and AFD modes. As the uncoupled polar and AFD modes cross near 52~K, their hybridization is maximized, driving the lower hybridized branch to develop a minimum on a finite-wave-vector shell. This finite-$q$ softening triggers a Brazovskii-type instability, strongly enhancing polarization correlations and producing nanoscale polar textures. Away from the crossing temperature, the two modes become increasingly detuned, weakening their hybridization and the associated finite-$q$ softening. These results reaveal the origin of the formation of polar nanoregions in SrTiO$_3$, naturally explaining the unexpected confinement to an intermediate-temperature window and providing a mechanism beyond the quantum-fluctuation-based interpretation suggested by experiment. Furthermore, we predict an unconventional reentrant-like sequence in weakly strained SrTiO$_3$, evolving from ferroelectric to paraelectric, polar-nanoregion, and eventually paraelectric regimes upon heating from zero temperature.

cond-mat.mtrl-sci↗
arXiv2026

Soft-Phonon-Driven Effective Inversion-Symmetry Crossover in Quantum Paraelectrics

Symmetry lays the foundation of condensed matter physics and its experimental manifestation provides fundamental insight into the collective behaviors of quantum materials. Optical second-harmonic generation (SHG) is widely regarded as a fingerprint of inversion-symmetry breaking, yet whether and how collective lattice dynamics govern the nonlinear optical manifestation of local inversion-symmetry breaking remains unknown. Here, combining optical SHG, diffuse X-ray scattering, and microscopic theory, we reveal a phonon-regulated mechanism governing the temperature-dependent manifestation of local inversion-symmetry breaking in quantum paraelectric material KTaO3. We demonstrate that an oxygen-defect-mediated nonlinear optical channel is strongly coupled to the host soft polar mode, whose thermal fluctuations scramble the associated electronic phase coherence and thereby suppress the nonlinear manifestation of local inversion-symmetry breaking at elevated temperatures. Consequently, the nonlinear response exhibits a temperature-driven crossover from a regime in which local inversion-symmetry breaking is optically manifest to one that appears effectively centrosymmetric, without any accompanying structural change. Our findings revise the conventional picture of the temperature-dependent manifestation of inversion-symmetry breaking in quantum paraelectrics and establish a framework for understanding and engineering defect-mediated nonlinear optical responses in materials hosting low-energy polar excitations.

cond-mat.mtrl-sci↗
arXiv2026

Quench of chiral superconductivity by quantum phase fluctuations in twisted cuprate bilayers

Following theoretical proposals of chiral $d+id'$ superconductivity in twisted cuprate bilayers, experimental signatures of time-reversal symmetry breaking (TRSB) remain highly controversial. Here we demonstrate that quantum phase fluctuations fundamentally reshape the phase diagram of this proposed chiral state. Unlike regular superconducting orders, the chiral $d+id'$ state requires long-range coherence of an interlayer phase degree of freedom and is therefore intrinsically vulnerable to phase fluctuations. Incorporating these fluctuations nearly eliminates the chiral phase over most parts of the phase diagram, restricting it to a narrow twist-angle window and ultra-low temperatures. Phase fluctuations also strongly weaken Josephson phase locking near a twist angle of $45^\circ$. More broadly, our work establishes quantum phase fluctuations as a fundamental constraint on the emergence of TRSB phases in low-dimensional layered quantum materials.

cond-mat.supr-con↗
arXiv2026

Collective phase modes in twisted $d$-wave superconducting bilayers

Twisted cuprate bilayers have been predicted to host high-temperature chiral $d+id'$ superconductivity, originating from higher-order Josephson coupling processes. In such two-dimensional superconducting systems, long-wavelength fluctuations in the phase of the superconducting order parameter constitute gapless collective modes and therefore remain significant even at zero temperature. Here, we perform a theoretical analysis of the low-energy phase fluctuations in twisted $d$-wave superconducting bilayers within a self-consistent harmonic approximation, systematically retaining Josephson coupling to all orders. We demonstrate that higher-order Josephson coupling processes lead to nontrivial modifications of the phase dynamics. The momentum-resolved summand of the relative-phase stiffness is nonzero even in the normal state because interlayer tunneling explicitly breaks the intralayer U(1) symmetry, but its momentum integral vanishes for the continuum dispersion. The relative-phase stiffness is smaller in the $d+id'$ phase than in the $d$-wave phase, while the overall-phase stiffness has the opposite behavior. Furthermore, phase fluctuations strongly soften the Josephson plasma frequency near a twist angle of $45^\circ$ and also substantially reduce the Josephson critical current.

cond-mat.supr-con↗
arXiv2026

Bayesian Simultaneous Credible Bands for Polynomial Regression

Quantifying efficacy uncertainty across the entire dose range is crucial in dose-response studies. Although the frequentist simultaneous confidence band (FSCB) is widely used for this purpose, it does not readily incorporate prior knowledge. The Bayesian simultaneous credible band (BSCB) offers a natural alternative, yet practical methods for constructing BSCBs remain scarce in the literature. In this paper, we propose a unified framework for constructing a BSCB for the regression curve in a univariate polynomial model over a finite covariate interval. An efficient simulation-based procedure is developed to determine the critical constant of a BSCB. The framework accommodates inference under different levels of prior information and can be implemented either analytically or via posterior sampling methods. Notably, we prove that under mild regularity conditions, the BSCB is asymptotically equivalent to the FSCB, thereby attaining the nominal frequentist coverage for a broad class of priors. Simulation studies confirm that the BSCB attains the exact posterior simultaneous coverage probability across various scenarios. An application to a dose-response study illustrates its importance in identifying the minimum effective dose in Phase II clinical trials. Software implementation of the proposed methods is available in an accompanying R package.

stat.ME↗
arXiv2026

BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference

Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Transformer blocks, but existing methods (i) provide limited control under user-specific compute budgets and (ii) typically fix the routing path, failing to adapt as the context grows during decoding. We propose Buddy, a budget-driven dynamic depth routing framework. Buddy uses a lightweight Decision Module to score intermediate layers conditioned on the input and deterministically executes the top-k layers to satisfy a given budget. To support decode-time adaptation, Buddy reuses the first-layer KV cache as a low-overhead global context source and pools it together with the newest token representation before each routing decision. When no explicit budget is provided, an optional Budget Predictor estimates an input-dependent compute level to balance quality and efficiency. Experiments on Llama-family and Qwen models show that Buddy is competitive with strong static pruning baselines and often improves the accuracy-compute trade-off, while uniquely supporting strict budget control, decode-time rerouting, and multiple budgets within a single trained model.

cs.LG↗
arXiv2026

Excitonic-Superconducting Coexistence and Emergent Nematic Superconductivity Driven by Spontaneous Symmetry Breaking

Excitonic insulating (EI) and superconducting (SC) orders are generally regarded as mutually exclusive electronic instabilities. Within a self-consistent microscopic theory, we study electronic systems hosting an EI phase in the presence of SC pairing and show that an intrinsic mismatch between electron and hole Fermi surfaces fundamentally reshapes this competition. This mismatch stabilizes FFLO-like electron-hole pairing and drives spontaneous symmetry breaking of the EI state. The resulting symmetry breaking reconstructs the pairing phase space for SC and EI state, such that different regions of the Fermi surface complementarily support either EI or SC correlations, leading to a natural coexistence of the two orders. Notably, the emergent SC state consequently breaks rotational symmetry and develops intrinsic nematic superconductivity, even in the absence of explicit symmetry-breaking fields (such as magnetic fields, spin-orbit coupling, or bare band-structure anisotropy). Our results suggest that candidate materials such as monolayer 1T$'$-MoTe$_2$ and the square-net semimetal NaAlSi may provide promising platforms for observing this phenomenon. More broadly, these findings reveal a unique mechanism by which competing many-body orders generate electronic nematicity, suggesting a broader route toward spontaneous anisotropic electronic states in correlated quantum materials.

cond-mat.supr-con↗
arXiv2026

Hidden Crossover and Relaxor-Like Response from Emerging Polar Skyrmion Correlations in Ferroelectric Superlattices

Polar skyrmions in ferroelectric superlattices are nanoscale topological polarization textures typically regarded as weakly coupled objects confined to individual layers, with a role secondary to that of the underlying symmetry-breaking order parameter. Here using large-scale phase-field simulations of ferroelectric superlattices, we uncover a hidden thermal crossover deep inside the ferroelectric phase, where polar skyrmions evolve from an uncorrelated, layer-resolved state into an interlayer-correlated ensemble. This crossover occurs without additional symmetry breaking or a new order parameter, but produces a pronounced broad peak in the dielectric susceptibility. The anomaly originates from the competition between correlation-enhanced response, associated with the growth of interlayer skyrmion correlations, and polarization-induced stiffness, which suppresses dielectric fluctuations at low temperature. Under AC driving, the peak shifts with frequency, resembling relaxor ferroelectrics despite the absence of quenched disorder or polar nanoregions. Our results establish a disorder-free route to relaxor-like dielectric response and identify topological defect correlations as an organizing principle for thermodynamic anomalies, providing a mechanism distinct from conventional critical behavior associated with symmetry breaking and divergent order-parameter fluctuations.

cond-mat.mtrl-sci↗
arXiv2026

Rational maps with Cantor bubble Julia sets

It has been shown that Cantor bubble Julia sets can appear in the dynamics of polynomials and their singular perturbations. In this paper, we present a criterion that guarantees the existence of Cantor bubble Julia sets for certain rational maps with attracting or parabolic fixed points. Moreover, we construct other Cantor bubble Julia sets, including those with high-periodic attracting cycles and those with Hausdorff dimension two. Finally, we give a sufficient condition for Cantor bubble Julia sets to be quasisymmetrically equivalent to Cantor round bubbles.

math.DS↗
arXiv2026

Tracking unconventional superconductivity in the presence of strongly correlated Fermi arcs

One of the primary reasons that superconductivity in underdoped cuprates is enigmatic is that it emerges from an incoherent Fermi-arc state, so the applicability of the Bardeen-Cooper-Schrieffer (BCS) theory is questionable. Here we approach this problem by investigating unconventional $d$-wave superconductivity in a recently proposed solvable model for strongly correlated Fermi arcs. We show analytically that the exact incorporation of Fermi arcs fundamentally modifies the BCS equations, which enables us to isolate a many-body effect that suppresses the superconducting transition temperature $T_c$ beyond the simple reduction expected from a shrinking Fermi surface. The theory unambiguously produces: (i) a $T_c$ tracing out a dome as a function of hole doping, (ii) a new low-energy mode upon entering superconductivity, (iii) a suppressed superfluid stiffness in the underdoped regime, and (iv) a gap-to-$T_c$ ratio far exceeding the BCS limit, all consistent with experimental observations in cuprate superconductors. These findings provide an analytic benchmark for understanding how superconductivity emerges from a correlated Fermi-arc state in high-$T_c$ superconductors.

cond-mat.supr-con↗
arXiv2026

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Improving GPU kernel efficiency is crucial for advancing AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. However, existing LLM-based kernel optimization pipelines typically rely on opaque, implicitly learned heuristics within the LLMs to determine optimization strategies. This leads to inefficient trial-and-error and weakly interpretable optimizations. Our key insight is to replace implicit heuristics with expert optimization skills that are knowledge-driven and aware of task trajectories. Specifically, we present KernelSkill, a multi-agent framework with a dual-level memory architecture. KernelSkill operates by coordinating agents with long-term memory of reusable expert skills and short-term memory to prevent repetitive backtracking. On KernelBench Levels 1-3, KernelSkill achieves a 100% success rate and average speedups of 5.44x, 2.82x, and 1.92x over Torch Eager on Levels 1, 2, and 3, respectively, outperforming prior baselines. Code is available at https://github.com/0satan0/KernelMem/.

cs.LG↗
arXiv2026

A composite electron-lattice order: electronic nematicity of 2DEG and polarization density waves at a near-ferroelectric interface

We consider a two-dimensional electron gas (2DEG) formed at a near-ferroelectric interface and strongly coupled to polar phonons. Through a self-consistent microscopic many-body calculation, we show that the coupled system stabilizes a composite electron-lattice ordered state in which the lattice polarization spontaneously forms a polarization density wave (PDW), accompanied by an electronic stripe order in the 2DEG. This intertwined order partially reconstructs the electronic spectrum and generates a twofold quasiparticle anisotropy, giving rise to electronic nematicity at the single-particle level. However, under strong external electric fields, the nematic response becomes dominated by the collective sliding dynamics of the composite order: the sliding motion overwhelms the quasiparticle anisotropy and produces a strongly enhanced nematic signal with higher-order angular harmonics. The theory offers a natural explanation for several anomalous transport and anisotropic responses recently observed at the KTaO$_3$ (111) interface. We also estimate the mean-field transition temperature of this emergent ordered state, obtaining good agreement with experiments, and analyze its evolution with several tuning parameters. The proposed composite order, along with the field-induced crossover from quasiparticle-driven to sliding-dominated nematicity, provides a distinct mechanism of nematicity arising from many-body effects and collective dynamics in critical electron-boson systems, with applicability beyond ferroelectric platforms.

cond-mat.str-el↗
arXiv2026

Robust optimal reconciliation for hierarchical time series forecasting with M-estimation

Aggregation constraints, arising from geographical or sectoral division, frequently emerge in a large set of time series. Coherent forecasts of these constrained series are anticipated to conform to their hierarchical structure organized by the aggregation rules. To enhance its resilience against potential irregular series, we explore the robust reconciliation process for hierarchical time series (HTS) forecasting. We incorporate M-estimation to obtain the reconciled forecasts by minimizing a robust loss function of transforming a group of base forecasts subject to the aggregation constraints. The related minimization procedure is developed and implemented through a modified Newton-Raphson algorithm via local quadratic approximation. Extensive numerical experiments are carried out to evaluate the performance of the proposed method, and the results suggest its feasibility in handling numerous abnormal cases (for instance, series with non-normal errors). The proposed robust reconciliation also demonstrates excellent efficiency when no outliers exist in HTS. Finally, we showcase the practical application of the proposed method in a real-data study on Australian domestic tourism.

stat.AP↗
arXiv2026

Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search

As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC-BOS, for the automatic generation of classical Chinese adversarial prompts based on multi-dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black-box settings. Prompts are encoded into eight policy dimensions-covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC-BOS, consistently outperforming state-of-the-art jailbreak attack methods.

cs.AI↗
arXiv2026

Emergent spin-resolved electronic density waves from strong $d$-wave altermagnetism and pseudogap phenomena from phason fluctuations

Metallic $d$-wave altermagnets provide a unique setting in which strong spin-momentum locking can qualitatively reshape electronic instabilities. Here we develop a self-consistent microscopic theory beyond the mean-field approximation for density-wave order in $d$-wave altermagnetic metals. The altermagnetic band structure with strong spin-momentum locking reconstructs the Fermi surface into mutually orthogonal spin-selective quasi-1D sectors, whose strong nesting drives density-wave instabilities in the respective spin sectors. The resulting spin-resolved density-wave orders nevertheless share a common ordering wave vector and give rise to a distinct class of stripe states, accompanied by pronounced gap openings on the nested Fermi sheets. The relative phase between the two spin sectors controls the character of the collective order, allowing charge density wave, spin density wave, and mixed density-wave orders to emerge within the same ordered manifold. As temperature increases from zero, thermal excitation of the emergent phason mode induces strong phase fluctuations that destroy long-range density-wave order at $T_c$, while the single-particle gap remains finite, giving rise to a robust pseudogap regime that persists up to a higher temperature $T_g$. The theory reveals a density-wave mechanism unique to metallic $d$-wave altermagnets, rooted in strong spin-selective Fermi-surface reconstruction, and establishes a fluctuation-driven route to pseudogap phenomena governed by phason dynamics. Applied to the metallic $d$-wave altermagnet KV$_2$Se$_2$O, our simulations quantitatively reproduce the key spectroscopic features reported in recent experiments, providing a unified microscopic understanding of the underlying phenomena.

cond-mat.str-el↗
arXiv2026

LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech

Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription, spoken document understanding, and conversational analysis require robust models capable of processing and reasoning over long-form audio. In this work, we present LongSpeech, a large-scale and scalable benchmark specifically designed to evaluate and advance the capabilities of speech models on long-duration audio. LongSpeech comprises over 100,000 speech segments, each approximately 10 minutes long, with rich annotations for ASR, speech translation, summarization, language detection, speaker counting, content separation, and question answering. We introduce a reproducible pipeline for constructing long-form speech benchmarks from diverse sources, enabling future extensions. Our initial experiments with state-of-the-art models reveal significant performance gaps, with models often specializing in one task at the expense of others and struggling with higher-level reasoning. These findings underscore the challenging nature of our benchmark. Our benchmark will be made publicly available to the research community.

cs.SD↗
arXiv2025

Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation

Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mismatch and linguistic variability. This challenge is amplified for modern Large Language Model (LLM)-based ASR systems, whose massive scale and complex training dynamics make effective fine-tuning non-trivial. To address this gap, this paper proposes a principled and metric-driven fine-tuning framework for adapting both traditional and LLM-based ASR models to specialized domains. The framework emphasizes learning rate optimization based on performance metrics, combined with domain-specific data transformation and augmentation. We empirically evaluate our framework on state-of-the-art models, including Whisper, Whisper-Turbo, and Qwen2-Audio, across multi-domain, multilingual, and multi-length datasets. Our results not only validate the proposed framework but also establish practical protocols for improving domain-specific ASR performance while preventing overfitting.

cs.SD↗
arXiv2025

Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery

Generalized category discovery (GCD) is an important and challenging task in open-world learning. Specifically, given some labeled data of known classes, GCD aims to cluster unlabeled data that contain both known and unknown classes. Current GCD methods based on parametric classification adopt the DINO-like pseudo-labeling strategy, where the sharpened probability output of one view is used as supervision information for the other view. However, large pre-trained models have a preference for some specific visual patterns, resulting in encoding spurious correlation for unlabeled data and generating noisy pseudo-labels. To address this issue, we propose a novel method, which contains two modules: Loss Sharpness Penalty (LSP) and Dynamic Anchor Selection (DAS). LSP enhances the robustness of model parameters to small perturbations by minimizing the worst-case loss sharpness of the model, which suppressing the encoding of trivial features, thereby reducing overfitting of noise samples and improving the quality of pseudo-labels. Meanwhile, DAS selects representative samples for the unknown classes based on KNN density and class probability during the model training and assigns hard pseudo-labels to them, which not only alleviates the confidence difference between known and unknown classes but also enables the model to quickly learn more accurate feature distribution for the unknown classes, thus further improving the clustering accuracy. Extensive experiments demonstrate that the proposed method can effectively mitigate the noise of pseudo-labels, and achieve state-of-the-art results on multiple GCD benchmarks.

cs.CV↗