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Yi Xia

Publications and source records attributed to Yi Xia.

At least 19 recordsLinked to original sources
arXiv2026

Dynamical Phonon Anharmonicity Drives Charge-Density-Wave Softening and Electronic Quasiparticle Decoherence in Monolayer 1H-NbSe2

Charge-density-wave (CDW) instabilities in two-dimensional materials are governed by strongly anharmonic soft phonons, whose dynamical behavior remains poorly understood beyond the statically renormalized vibrations. Here, using monolayer 1H-NbSe2, we investigate how frequency-dependent three- and four-phonon interactions reshape phonon spectral functions and influence electronic excitations. We find that three-phonon interactions induce substantial dynamical softening of CDW modes, increasing the estimated transition temperature relative to static self-consistent phonon theory. Four-phonon interactions further promote such dynamical softening and enhance spectral broadening, producing non-Lorentzian lineshapes that challenge the quasiparticle picture. Incorporating dynamically renormalized phonon frequencies and electron-phonon coupling into Fan-Migdal electron self-energy calculations reveals enhanced electronic kinks, satellite features, and momentum-dependent quasiparticle decoherence, driven by the strong momentum dependence of the electron-phonon coupling matrix elements. These findings establish dynamical anharmonicity as a critical link between soft-mode lattice fluctuations and electronic spectral signatures, providing insights into coupled lattice-electron dynamics in two-dimensional quantum materials.

cond-mat.mtrl-sci↗
arXiv2026

A photonic-integrated optomechanical mid-infrared detector

Tracking the resonance frequency of a mechanical oscillator is the basis of atomic force microscopy, mass sensing and thermometry. Nanomechanical thermal detectors exploit this principle to sense absorbed radiation, most effectively with tensile-stressed resonators, whose tension converts thermal expansion into a large frequency shift and dilutes dissipation. Approaching their fundamental sensitivity limit, however, has required free-space or fibre-coupled interferometers, hindering compact deployment and scaling to arrays. Here we remove this bottleneck by monolithically integrating a high-Q nanomechanical resonator with a silicon nitride photonic integrated circuit. A tensile-stressed silicon nitride trampoline with an impedance-matched platinum absorber is dispersively coupled to a photonic integrated microresonator beneath it. At room temperature and with microwatt-level optical power, the detector reaches a noise-equivalent power of 3.4 pW/$\sqrt{\rm Hz}$ for broadband thermal radiation (3-12 $μ$m), referred to incident power. Referred to absorbed power, this is within a factor of 1.5 of its fundamental thermal fluctuation limit. Using our detector in a state-of-the-art commercial Fourier-transform infrared spectrometer, we resolve the vibrational fingerprint of a polymer film across 2-20 $μ$m and reach the precision of the built-in pyroelectric detector with about an order of magnitude lower incident power. This architecture opens a route to multiplexed optomechanical focal-plane arrays.

physics.optics↗
arXiv2026

A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting

This paper proposes Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting framework that systematically integrates domain-specific expert roles to enhance reasoning in large language models (LLMs) without task-specific examples. We assess two versions of our approach. In the first version, static role selection, we evaluate eight predefined expert roles across arithmetic, commonsense, and reading comprehension benchmarks to determine their impact on reasoning performance. In the second version, dynamic role selection, each model autonomously selects the single most suitable role for each benchmark, enabling adaptive task-role alignment without manual specification. Across 20 LLMs and seven benchmarks, RE-ZS-CoT frequently surpasses zero-shot and zero-shot-CoT baselines, achieving performance improvements of up to 44 percentage points. The results reveal a strong correspondence between static and dynamic versions; the benchmarks where static roles yielded improvements were almost always those where dynamic roles also outperformed baselines, though static selection achieved peak performance. Ablation studies further show that unconventional roles degrade performance, underscoring the necessity of task-role alignment. The framework's scalability is validated across model sizes (2b-14b parameters), with smaller models like Granite3.3-8b achieving remarkable proficiency in arithmetic reasoning. RE-ZS-CoT advances zero-shot prompting by unifying role specialization with chain-of-thought reasoning, offering a practical, resource-efficient solution for improving LLM capabilities across diverse domains.

cs.CL↗
arXiv2026

Emoception: Selective Affective Layer Fine-Tuning of Video Vision Transformers for Player Arousal Change Recognition From Gameplay Footage

This article proposes Selective Affective Layer Fine-Tuning (SALFT), an efficient adaptation framework for Video Vision Transformers in player arousal recognition from gameplay. To bypass computationally expensive full fine-tuning, SALFT introduces a selection criterion based on the L2-norm change in layer parameters after brief adaptation, directly measuring representational shifts and providing a more stable basis than gradient-based alternatives. Evaluated via five-fold cross-validation on the Arousal Video Game AnnotatIoN dataset, SALFT achieves performance comparable to full fine-tuning across all games without statistically significant degradation ($p>0.05$), while updating only $\approx$8% of parameters (over 92% reduction). Notably, in one game, SALFT consistently outperforms both full fine-tuning and the best baseline across all metrics and folds, reaching the theoretical minimum p-value (p=0.0625, exact two-sided Wilcoxon signed-rank test). In addition, we introduce an interpretability method to trace attention patterns, enhancing model transparency. These results establish SALFT as an effective and efficient approach for affective game computing.

cs.HC↗
arXiv2026

SENSE: State-aware Emotion Navigation Storytelling Engine

This paper presents SENSE, a state-aware framework for generating playable branching visual novels with multi-track emotional navigation. Integrating a state-based narrative architecture called MIND, a structure analyzer, and a path-aware context management module, SENSE produces narratives that are both structurally coherent and emotionally rich. From minimal high-level inputs, it generates multiple intersecting routes while preserving character consistency and narrative causality. Evaluations using LLM judges, affective metrics, and visual assessments indicate SENSE outperforms baselines in narrative diversity and robust asset integration, while preliminary human trials show directional improvements in emotional fidelity alongside comparable enjoyment.

cs.HC↗
arXiv2026

Robust distortion risk metrics with Wasserstein and Unimodal Constraints

We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlying distribution: mean, variance, unimodality, and Wasserstein distance to a reference distribution. We first examine a broad class of distortion functions, assuming only finite variation and imposing neither continuity nor monotonicity. This class includes important examples such as the Gini deviation, the mean-median deviation, and inter-quantile differences. When the uncertainty set is defined by a fixed mean, variance, and Wasserstein distance, we derive the worst- and best-case values of the distortion risk metric and identify the corresponding extremal distributions. We then impose an additional unimodality constraint. In this case, for absolutely continuous distortion functions, we again characterize the worst- and best-case values and explicitly determine the optimal distributions attaining these bounds. Moreover, an algorithm is developed to compute sharp upper bounds and the worst-case quantiles. We apply our results to a robust portfolio optimization problem of interest.

q-fin.RM↗
arXiv2026

Equilibrium bias and convergence in augmented primal--dual dynamics with sampled constraints

This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples. Unbiased constraint observations can produce a biased augmented multiplier signal, shifting the equilibria of the mean dynamics. For componentwise inequalities, we give a necessary and sufficient condition for preserving the Karush-Kuhn-Tucker (KKT) equilibria and construct a convex example with a locally exponentially stable equilibrium that violates complementarity. To address this bias, constraint values are estimated recursively before forming the augmented multiplier signal. For smooth convex conic problems, a joint energy analysis establishes boundedness of the primal, dual, and estimation states, vanishing estimation error, and almost sure convergence of the primal-dual iterates to a single KKT point under global regularity and bounded conditional second moments. The result allows nonunique solutions and multipliers while keeping the number of samples per iteration fixed. Numerical studies illustrate the predicted equilibrium bias and examine convergence with nonunique KKT points and nonlinear constraints.

math.OC↗
arXiv2026

Self-Consistent Phonon Spectral Functions and Thermal Transport Beyond the Quasiparticle Approximation

Anharmonic lattice dynamics shapes phonon spectra and thermal transport, yet first-principles linewidth calculations typically assume phonon quasiparticles with sharply defined frequencies. Here, we introduce a self-consistent spectral function framework that determines interacting phonon spectral functions by dressing the internal lines of the three-phonon bubble self-energy with the spectra generated by the same interaction. The method self-consistently determines the real and imaginary parts of the self-energy on equal footing, eliminates the need for externally chosen smearing parameters to enforce energy conservation, and extends lattice dynamics and thermal transport calculations beyond the quasiparticle approximation. Applied to zincblende HgTe, self-consistency substantially broadens acoustic and optical phonon spectra, activates scattering channels inaccessible in the one-shot quasiparticle calculation, and reduces the lattice thermal conductivity by approximately a factor of five to the experimental scale while reproducing its temperature dependence, without explicitly invoking higher-order interactions. Mode-resolved spectral functions are further validated against the power spectra from direct molecular dynamics simulations. These results establish self-consistent spectral functions as a practical bridge between quasiparticle perturbation theory and fully dynamical simulations, and provide a framework readily generalizable to higher-order anharmonic processes.

cond-mat.mtrl-sci↗
arXiv2026

Machine Learning Local Potentials for Accelerated Electron-Phonon Interactions Calculations within the Projector Augmented-Wave Framework

Electron-phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning strategy that learns the self-consistent local potential on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron-phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the established VASP/PHELEL framework. Using targeted grid-point sampling and feature-diversity selection for efficient model training, we validate MLLocP for elemental Cu, polar BAs, and chemically disordered Cu3Au. The learned potentials and their resulting displacement derivatives closely reproduce direct density functional theory calculations, yielding transport coefficients within approximately 3.5% for BAs mobility, 8% for Cu conductivity, and 11% for Cu3Au conductivity. For the 32-atom Cu3Au special quasirandom structure, using 40 sampled configurations reduces the number of self-consistent calculations by about fivefold relative to the 192 displaced structures required directly; using 20 or 10 configurations increases the acceleration to approximately 10- and 20-fold, with conductivity errors of 14.3% and 18.6%, respectively. MLLocP thus provides a scalable route to PAW electron-phonon calculations in complex materials while preserving the underlying all-electron formalism.

cond-mat.mtrl-sci↗
arXiv2026

Quintic-Anharmonicity-Assisted Three-Phonon Scattering: A Previously Overlooked Same-Order Channel to Four-Phonon Scattering

Four-phonon scattering is widely regarded as the leading higher-order anharmonic correction to conventional three-phonon description of anharmonic scattering and lattice thermal transport. Here we show that diagrammatic perturbation theory contains another phonon linewidth contribution of the same perturbation order, arising from the coupling between cubic and quintic anharmonic vertices. We derive and implement this contribution from first principles and identify it as a quintic-anharmonicity-assisted three-phonon scattering channel. Although this process shares energy-conservation structure of the conventional three-phonon process, its perturbative order and temperature dependence resemble those of the conventional four-phonon process. For Si, we find that phonon scattering from this additional channel is comparable to four-phonon scattering over a broad frequency and temperature range, leading to a similar accelerated reduction of lattice thermal conductivity at high temperatures. We further show that in strongly anharmonic AgCl, this channel can become comparable to the ordinary three-phonon scattering even at room temperature. These results demonstrate that the commonly observed high-temperature enhanced scattering and suppression of lattice thermal conductivity might not be attributed uniquely to four-phonon process, and establish quintic-anharmonicity-assisted three-phonon scattering as a previously overlooked same-order channel in anharmonic lattice dynamics, which may be leveraged to uncover hidden microscopic thermal transport mechanisms.

cond-mat.mtrl-sci↗
arXiv2026

Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models

Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical. A common intuition, which we call the Attention-Confidence Assumption, holds that reliability follows from "structural" visual perception: tight attention on relevant regions should signal a trustworthy answer, while scattered attention signals confusion. We challenge this through the VLM Reliability Probe (VRP), a systematic cross-family study of reliability signals in contemporary Vision-Language Models (VLMs). We introduce structural-attention metrics, cluster counts (C_k) and spatial entropy (H_s), to quantify the visual encoder's gaze, and track its evolution (Delta H_s) across layers. This reveals a "Symbolic Detachment": models often "Early Lock" visual features only to diffuse attention later, severing early perception from final generation. Contrary to the grounding hypothesis, we find a "Cluster Failure": spatial attention has near-zero correlation (R approx 0.001) with accuracy. Instead, reliability is a phenomenon of generation dynamics and internal-state distributions. Self-Consistency, the agreement rate across sampled reasoning paths, is the dominant predictor of truth (R = 0.429). Scaling causal interventions exposes a sharp architectural divergence: LLaVA locks its prediction in a fragile late-stage bottleneck, whereas PaliGemma and Qwen2-VL distribute reliability globally, staying resilient even when ~50% or more of their most predictive layer is destroyed. For current VLMs, reliability signals are detached from visual grounding maps and are best inferred from generation-time dynamics and hidden-state probes.

cs.CV↗
arXiv2026

Where Reliability Lives in Vision-Language Models: A Mechanistic Study of Attention, Hidden States, and Causal Circuits

A pervasive intuition holds that vision-language models (VLMs) are most trustworthy when their attention maps look sharp: concentrated attention on the queried region should imply a confident, calibrated answer. We test this Attention-Confidence Assumption directly. We instrument three open-weight VLM families (LLaVA-1.5, PaliGemma, Qwen2-VL; 3-7B parameters) with a unified mechanistic pipeline -- the VLM Reliability Probe (VRP) -- that compares attention structure, generation dynamics, and hidden-state geometry against a single correctness label. Three results emerge. (i) Attention structure is a near-zero predictor of correctness (R_pb(C_k,y)=0.001, 95% CI [-0.034,0.036]; R_pb(H_s,y)=-0.012, [-0.047,0.024] on a pooled n=3,090 split), even though attention remains causally necessary for feature extraction (top-30% patch masking drops accuracy by 8.2-11.3 pp, p<0.001). (ii) Reliability becomes legible later in the computation: a single hidden-state linear probe reaches AUROC>0.95 on POPE for two of three families, and self-consistency at K=10 is the strongest behavioral predictor we measure at 10x inference cost (R_pb=0.43). (iii) Causal neuron-level ablations expose a sharp architectural split with direct monitor-design implications: late-fusion LLaVA concentrates reliability in a fragile late bottleneck (-8.3 pp object-identification accuracy after top-5 probe-neuron ablation), whereas early-fusion PaliGemma and Qwen2-VL distribute it widely and absorb destruction of ~50% of their peak-layer hidden dimension with <=1 pp degradation. The takeaway is narrow but consequential: in 3-7B VLMs, reliability is read more reliably off hidden-state geometry, layer-wise margin formation, and sparse late-layer circuits than off attention-map sharpness.

cs.AI↗
arXiv2026

Coupled Ferroelectricity and Phonon Chirality

The ability to control chirality and chiral phonons offers a route to manipulate the direction of spin and angular-momentum transport. In materials with rigid structural chirality, such as quartz, phonon chirality is fixed by the handedness and cannot be switched. By contrast, ferroelectric materials host a spontaneous polarization that can be reversibly switched by an external electric field. When chirality is coupled to this ferroelectric polarization, it enables electrical switching of crystal chirality and the associated phonon angular momentum, which is compatible with solid-state spintronic architectures, enabling control over chirality-dependent quantum states.1 Here, we report the experimental demonstration of the coupling between ferroelectricity and phonon chirality in the molecular ferroelectric triglycine sulfate. By electrically switching the crystal chirality, we achieve reversible and device-compatible control of phonon chirality, as revealed by in situ time-resolved magneto-optical Kerr effect measurements. The Kerr rotation reverses with electric-field switching, while phonon chirality vanishes in the paraelectric phase and is tunable in the racemic ferroelectric state. Furthermore, density functional theory calculations and circularly polarized Raman spectroscopy further corroborate the opposite circular phonon motions. These results establish an electrically addressable coupling pathway linking ferroelectricity, structural chirality, chiral phonons, and spin, opening a route toward chiral-phonon-enabled spin and phonon control technologies based on ferroelectric materials.

cond-mat.mtrl-sci↗
arXiv2026

Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials

Phonons, quantized vibrations of the atomic lattice, are fundamental to understanding thermal transport, structural stability, and phase behavior in crystalline solids. Despite advances in computational materials science, most predictions of vibrational properties in large materials databases rely on the harmonic approximation and overlook crucial temperature-dependent anharmonic effects. Here, we present a scalable computational framework that combines machine learning interatomic potentials, anharmonic lattice dynamics, and high-throughput calculations to investigate temperature-dependent phonons across thousands of materials. By fine-tuning the universal M3GNet interatomic potential using high-quality phonon data, we improve phonon prediction accuracy by a factor of four while preserving computational efficiency. Integrating this refined model into a high-throughput implementation of the stochastic self-consistent harmonic approximation, we compute temperature-dependent phonons for 4,669 inorganic compounds. Our analysis identifies systematic elemental and structural trends governing anharmonic phonon renormalization, with particularly strong manifestations in alkali metals, perovskite-derived frameworks, and related systems. Machine learning models trained on this dataset identify key atomic-scale features driving strong anharmonicity, including weak bonding, large atomic radii, and specific coordination motifs. First-principles validation confirms that anharmonic effects can dramatically alter lattice thermal conductivity by factors of two to four in some materials. This work establishes a robust and efficient data-driven approach for predicting finite-temperature phonon behavior, offering new pathways for the design and discovery of materials with tailored thermal and vibrational properties.

cond-mat.mtrl-sci↗
arXiv2025

Lattice Thermal Transport Beyond the Quasiparticle Approximation: Nontrivial Spectral Competition between Three- and Four-Phonon Interactions

The breakdown of the quasiparticle approximation (QPA) for phonons in strongly anharmonic materials necessitates advanced first-principles frameworks for accurate lattice dynamics and thermal transport predictions. We develop a comprehensive beyond-quasiparticle approximation (BQPA) approach incorporating both three- (3ph) and four-phonon (4ph) interactions and apply it to investigate lattice thermal conductivity ($κ_{\rm L}$) in MgO, PbTe, and AgCl -- materials that span a broad spectrum of anharmonicity, from weak to severe anharmonic regimes with overdamped phonons. We reveal that while BQPA consistently increases $κ_{\rm L}$ relative to QPA due to phonon softening when considering only 3ph interactions, the inclusion of additional 4ph interactions hardens the phonon spectrum and suppresses this enhancement, bringing BQPA and QPA predictions into close agreement via subtle spectral competition effects across all three compounds. These findings highlight that accurate modeling of $κ_{\rm L}$ in strongly anharmonic materials requires treating both full phonon spectral function and higher-order anharmonicity on equal footing. Our work establishes a systematic framework for modeling thermal transport in systems with overdamped phonons and provides critical insights for materials design beyond the limits of conventional phonon transport theory.

cond-mat.mtrl-sci↗
arXiv2025

First-Principles Theory of Five- and Six-Phonon Scatterings

Higher-order phonon scatterings beyond fourth order remain largely unexplored despite their potential importance in strongly anharmonic materials at elevated temperatures. We develop a theoretical formalism for first-principles calculation of five- and six-phonon scatterings using Green's function techniques based on a diagrammatic formalism, and systematically investigate multi-phonon interactions in Si, MgO, and BaO from room temperature to near melting points. Our calculations reveal dramatically different material-dependent behaviors: while five- and six-phonon processes remain negligible in Si even at high temperatures, they become increasingly important in MgO near its melting point (3100~K) and in BaO at intermediate temperatures (1200~K). Most remarkably, five- and six-phonon scatterings surpass three- and four-phonon scattering intensity in BaO near its melting point (2100~K), reducing lattice thermal conductivity by over 50\%. We demonstrate that the strength of higher-order interactions is primarily governed by interatomic force constants, with BaO exhibiting five- and six-phonon scattering rates over one order of magnitude stronger than MgO despite identical crystal structures, due to large scattering phase space arising from softened harmonic interactions. Our work provides theoretical insights into the lattice dynamics and thermal transport in strongly anharmonic materials and at elevated temperatures.

cond-mat.mtrl-sci↗
arXiv2025

High-throughput computational framework for lattice dynamics and thermal transport including high-order anharmonicity: an application to cubic and tetragonal inorganic compounds

Accurately predicting lattice thermal conductivity (kL) from first principles remains a challenge in identifying materials with extreme thermal behavior. While modern lattice dynamics methods enable routine predictions of kL within the harmonic approximation and three-phonon scattering framework (HA+3ph), reliable results, especially for low-kL compounds, require higher-order anharmonic effects, including self-consistent phonon renormalization, four-phonon scattering, and off-diagonal heat flux (SCPH+3,4ph+OD). We present a high-throughput workflow integrating these effects into a unified framework. Using this, we compute kL for 773 cubic and tetragonal inorganic compounds across diverse chemistries and structures. From 562 dynamically stable compounds, we assess the hierarchical effects of higher-order anharmonicity. For about 60% of materials, HA+3ph predictions closely match those from SCPH+3,4ph+OD. However, SCPH corrections often increase kL, sometimes by over 8 times, while four-phonon scattering universally reduces it, occasionally to 15% of the HA+3ph value. Off-diagonal contributions are minor in high-kL systems but can be comparable to the diagonal ones in highly anharmonic, low-kL compounds. We highlight four cases-Rb2TlAlH6, Cu3VS4, CuBr, and KTlCl4-exhibiting distinct anharmonic behaviors. This work delivers not only a robust workflow for high-fidelity kL dataset but also a quantitative framework to determine when higher-order effects are essential. The hierarchy of kL results, from the HA+3ph to SCPH+3,4ph+OD level, offers a scalable, interpretable route to discovering next-generation extreme thermal materials.

cond-mat.mtrl-sci↗
arXiv2025

Noncontact friction in ultracoherent nanomechanical resonators near dielectric materials

Micro- and nanomechanical resonators are emerging as promising platforms for quantum technologies, precision sensors and fundamental science experiments. To utilize these devices for force sensing or quantum optomechanics, they must be brought in close proximity with other systems for functionalization or efficient readout. Improved understanding of the loss mechanisms in nanomechanical resonators, specifically the advent of dissipation dilution, has led to the development of resonators with unprecedented coherence properties. The mechanical quality factors of this new class of ultracoherent micro- and nanomechanical oscillators can now exceed 1 billion at room temperature, setting their force sensitivities below 1 $\mathrm{aN}/\sqrt{\mathrm{Hz}}$, surpassing those of the state-of-the-art atomic force microscopes (AFMs). Given this new regime of sensitivity, an intriguing question is whether the proximity of other materials hinders mechanical coherence. Here we show: it does. We report a novel dissipation mechanism that occurs in ultracoherent nanomechanical oscillators caused by the presence of nearby dielectrics. By studying the parameter scaling of the effect, we show that the mechanism is more severe for low-frequency mechanical modes and that it is due to dielectric loss within the materials caused by the motion of a resonator which carries static charges. Our observations are consistent with the noncontact friction (NCF) observed in AFMs. Our findings provide insights into limitations on the integration of ultracoherent nanomechanical resonators and highlight the adverse effects of charged defects in these systems.

cond-mat.mes-hall↗