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Haoyu Hu

Publications and source records attributed to Haoyu Hu.

At least 19 recordsLinked to original sources
arXiv2026

Resonant Far-Infrared Spectroscopy of Flat-Band Fermions in Magic Angle Graphene

Moir\'e engineering in twisted two-dimensional (2D) materials radically alters low-energy bands, interactions and topological quantum states. Despite extensive studies, optical spectroscopy of interacting moir\'e bands in the characteristic far-infrared (FIR) regime has remained largely unexplored due to extreme experimental challenges. Using a newly developed millikelvin FIR platform, we report the observation of the long-sought-after characteristic FIR resonances of flat-band electrons in magic-angle twisted bilayer graphene (MATBG). We observe highly tunable spectroscopic signatures of interacting light and heavy fermions that constitute the flat bands in MATBG. Using the topological heavy-fermion model (THF), we show that itinerant topological electrons act as an "antenna" that couples strongly to the optical field, with resonant frequencies renormalized by the hybridization with localized heavy electrons. We establish optical selection rules of MATBG which uncovers the key symmetry governing light-heavy fermion hybridization. At charge neutrality, we observe pronounced resonances at energies below the on-site Coulomb energy, implying the emergence of new many-body modes. Our experiments and modeling provide a fundamental understanding of light-matter interactions in MATBG and enable resonant optical spectroscopy of moir\'e bands down to millikelvin temperatures.

cond-mat.mes-hall↗
arXiv2026

Thermodynamic phase transition, pairing symmetry and Fermi surface topology in Ruddlesden-Popper nickelate films

Ruddlesden-Popper (RP) nickelates provide an uncharted territory to explore high-transition-temperature (high-$T_C$) superconductivity and superconducting mechanism. Here, we investigate the electronic structure of a new type of high-$T_C$ superconducting RP nickelate heterostructure $\mathrm{La_2PrNi_2O_7/NdAlO_3}$ by angle-resolved photoemission spectroscopy. A superconducting state is observed without a pseudogap state, enabling a direct measurement of the superconducting order parameter and a microscopic extraction of the electronic specific heat. The observed superconducting gap opens at $T_C$ with prominent coherence peaks, illustrating the emergence of nonzero order parameter upon entering the superconducting state. An electronic specific heat jump appears at $T_C$, further demonstrating a thermodynamic phase transition. The magnitude of the superconducting order parameter is quantified by the observed superconducting coherence peaks, and a nodeless behavior is unambiguously established in the absence of pseudogap. The underlying Fermi surface consists of $\alpha$, $\beta$ and $\gamma$ pockets, exhibiting a multi-orbital nature. Strain dependent measurements further reveal the $\gamma$ pocket in all superconducting and non-superconducting films with different epitaxial strain. Our results establish the missing thermodynamic evidence for superconducting phase transition in nickelates. They also provide direct evidence for the symmetry of the superconducting order parameter and illustrate the relationship between Fermi surface topology and the emergence of superconductivity in RP nickelate films.

cond-mat.supr-con↗
arXiv2026

Momentum Structure of Superconductivity and Sublattice Effects from Quasiparticle Interference in CsV$_3$Sb$_5$

Quantum interference encoded in the sublattice texture of kagome Bloch wavefunctions has been widely invoked as a route to correlated states, including chiral charge order, unconventional superconductivity, and their possible intertwining in pair-density-wave (PDW) states. Using sub-Kelvin scanning tunneling microscopy, we conducted spectroscopic mapping of the kagome material CsV$_3$Sb$_5$ with high energy resolution and dense energy sampling through the superconducting gap. Quasiparticle interference (QPI) analysis, aided by ab initio and symmetry calculations, reveals an isotropic superconducting gap on the Fermi surfaces derived from V $M_z$-even ($M_z^+$) $d$ orbitals, thereby constraining possible gap symmetries and limiting any gap anisotropy to the remaining V $M_z$-odd ($M_z^-$) and Sb $p_z$ bands. Meanwhile, the CDW-peak-selected d$I$/d$V$ spectra closely track the spatially averaged density of states and show no distinct enhancement restricted to subgap energies, which do not support an additional PDW modulation within our sensitivity. Finally, the selective absence of specific QPI scattering vectors points to a spectroscopic sensitivity to sublattice character on the Fermi surface. Together, these results provide a clearer experimental picture of the low-energy electronic structure relevant to kagome superconductivity in CsV$_3$Sb$_5$.

cond-mat.supr-con↗
arXiv2026

Extended s-wave superconductivity in M-point twisted bilayer SnSe2

We investigate the emergence of electronic order and unconventional superconductivity in M-valley moir\'e materials. Starting from a first-principles Wannier model of AB-stacked twisted SnSe2, we tackle the (gate-screened) long-ranged Coulomb interaction with functional renormalization group simulations resolving the momentum structure and energy scales of the leading Fermi surface instabilities. Upon doping an antiferromagnetic stripe state at half-filling ($\nu=3$ electrons per moir\'e unit cell) of the moir\'e flat bands, magnetic order gives way to unconventional superconductivity mediated by valley-selective spin fluctuations: Large hole doping ($\nu\approx1$) leads to weak-coupling superconductors with various pairing symmetries, while slight electron- and hole-doping ($\nu\approx2,4$) stabilizes a spin-singlet, extended s-wave state that benefits from scattering between virtual particle and hole states that are detuned from the Fermi level. These findings establish M-point moir\'e materials as a quantum simulation platform with phenomenological parallels to the class of iron pnictide superconductors.

cond-mat.supr-con↗
arXiv2026

Quantum geometry and critical temperature enhancement in MgB$_2$ superconductivity

MgB$_2$, a phonon-mediated superconductor with record-high critical temperature $T_c\simeq 39$ K, is revisited to obtain a comprehensive theory of electrons, phonons, and their coupling with minimal ab initio input. We construct compact analytic models for the electronic structure, phonons, and electron-phonon coupling (EPC) of MgB$_2$. We show that strong in-plane B $sp^2$ bonding realizes an obstructed band structure whose natural description is a bond-centered kagome lattice, yielding small quasi-2D $\sigma$-band Fermi-surface cylinders and pronounced quantum-geometric effects. The phonon spectrum is found to closely track that of a graphene-like boron layer, but the heavy intercalated Mg atoms dominate the three acoustic branches and rigidly lift the boron modes into the optical sector, while the in-plane B-B bond-stretching mode exhibits a pronounced softening along $\Gamma$-A. By symmetry, this $\Gamma$-point bond-stretching mode is the only $\Gamma$ phonon that can couple to the $\sigma$ Fermi surface, explaining its dominant contribution to the EPC. Upon electron doping toward the doubly degenerate band edge of the $\sigma$ sheets, we find that a reduced density of states competes with enhanced EPC matrix elements. At light electron doping, ab initio calculations show that the EPC enhancement dominates, leading to an increase in $T_c$ (within the clean doping limit without disorder effects). Using the Gaussian approximation for the EPC tensor, we further show that this enhancement is overwhelmingly quantum geometric in origin, arising from a geometric EPC contribution of the small $\sigma$ Fermi surface peaked at $\Gamma$. Overall, our results provide a transparent, symmetry-based account of superconductivity in MgB$_2$ and suggest that quantum-geometric effects can be essential for shaping doping trends in phonon-mediated superconductors.

cond-mat.supr-con↗
arXiv2026

Quantum-Geometry-Induced Superconductivity near a Fractional Chern Insulator

Recent moir\'e experiments and numerical studies of interacting Chern bands have revealed fractional Chern insulators, charge-density-wave order, and superconductivity as proximate correlation-driven phases in topological systems. How these phases compete or intertwine, and how quantum geometry shapes their interplay, remain open questions. Here we present an analytic study of competing correlation-driven phases in a partially filled Chern band using a coupled-wire construction and bosonization. The key ingredient is the coexistence of interaction channels that favor, respectively, a fractional Chern insulator (FCI) and a closely related anti-FCI (aFCI) state. The aFCI channel is specific to lattice Chern bands and is enhanced by the quantum geometry of the underlying band structure. We show that when both FCI and aFCI scattering channels are present, their interplay generates an effective coupling that drives a superconducting instability near the FCI phase. The same mechanism can also favor a charge-density-wave phase, depending on microscopic parameters. Using a perturbative renormalization-group analysis, we obtain the phase diagram and identify a superconducting regime adjacent to the FCI phase. We further estimate the superconducting transition temperature and show that it is enhanced by quantum geometry. Our results establish quantum geometry as an organizing principle for the interplay among FCI, aFCI, and superconducting correlations.

cond-mat.supr-con↗
arXiv2026

DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation

Although DINOv3 has demonstrated remarkable semantic discrimination in natural imagery, its direct application to volumetric medical segmentation is hindered by inherent dimension and domain disparities. To resolve these issues, we propose DINO-Med3D, a two-stage progressive framework that repurpose the pre-trained DINOv3 encoder for 3D medical tasks. In the first stage, we mitigate the dimension gap by introducing a multi-slice embedding module that incorporates pseudo-3D context, while simultaneously employing a segmentation proxy task to adapt representations learned from natural scenes to the medical domain. Subsequently, we further enhance volumetric understanding by adding lightweight 3D adapters into the frozen backbone to enforce global inter-slice continuity. Finally, to compensate for the spatial information loss inherent in the embedding process, we design a parallel detail recovery stream to explicitly preserve high-frequency boundary cues. Extensive experiments on five public datasets demonstrate that our approach successfully adapts DINOv3 to the medical domain and significantly outperforms state-of-the-art baselines.

cs.CV↗
arXiv2026

Mixed-dimensional quantum Monte Carlo studies of M-point moir\'e materials

A new moir\'e-material platform has recently been proposed based on twisting two-dimensional triangular-lattice monolayers whose low-energy states lie at the three M points of the Brillouin zone. Continuum models derived from extensive ab initio simulations suggest that electrons in the conduction bands of one such M-point moir\'e material, twisted AA-stacked SnSe$_2$, realize a three-orbital Hubbard model with orbitally-selective, quasi-one-dimensional (quasi-1D) hopping, protected by a projective mirror symmetry. Here, we show that the resulting "mixed-dimensional" limit -- in which the hopping is exactly quasi-1D in each valley, while the valleys are coupled by interactions into a fully two-dimensional network -- can be sampled with Stochastic Series Expansion (SSE) quantum Monte Carlo (QMC) without a sign problem at any filling. We develop an efficient new SSE QMC algorithm that combines custom global updates with parallel tempering to overcome the equilibration challenges posed by the mixed-dimensional setting. We then use this algorithm to explore the phase diagram of M-point twisted AA-stacked SnSe$_2$. Over extended and realistic ranges of twist angles and interaction strengths, we find that at integer fillings the system supports correlated insulators whose nature and strength depend strongly on angle. At certain commensurate fractional fillings, we further find evidence for Wigner-Mott insulators. We analytically account for the main features observed numerically using a strong-coupling description. Finally, we discuss perturbations away from the mixed-dimensional limit and the possibility of applying our method to other realizations of mixed-dimensional Hubbard models.

cond-mat.str-el↗
arXiv2026

Engineering topological flat bands in $\Gamma$-valley moir\'e systems with Ising-type SOC: twisted 1T-ZrS$_2$ and 1T-SnSe$_2$

Twisted moir\'e superlattices hosting topological flat bands provide a platform to explore the interplay between topology and correlations. Here we investigate topological band structures in $\Gamma$-valley moir\'e systems based on 1T-ZrS$_2$ and 1T-SnSe$_2$. Using large-scale ab initio calculations and continuum modelling, we demonstrate that both materials exhibit an approximate spin-$U(1)$ symmetry and host isolated topological moir\'e valence bands, including quantum spin Hall and high spin Chern states. By constructing a hierarchy of $\Gamma$-valley moir\'e continuum models, we show that isolated moir\'e bands carry a trivial $C_3$ symmetry indicator when the low-energy physics is described by a single effective orbital and a single layer-hybridized branch, either bonding or antibonding. Topological bands therefore arise from inter-branch and/or inter-orbital coupling. Moreover, we determine interaction-driven phase diagrams using Hartree--Fock and exact diagonalization, finding various phases tunable by twist angle, interaction strength, and displacement field. We identify specific conditions under which fractional Chern insulators are favored. Together with previous work showing that the moir\'e conduction bands of 1T-ZrS$_2$ and 1T-SnSe$_2$ realize $M$-valley twisting and host quasi-one-dimensional physics, our results establish these systems as ideal platforms for strongly correlated moir\'e physics and provide a systematic framework for understanding topological band structures in $\Gamma$-valley moir\'e materials.

cond-mat.mtrl-sci↗
arXiv2026

DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating the computational cost. The overall token budget can be controlled along two main dimensions: (i) deciding which prompts to allocate rollouts to, and (ii) deciding how long each rollout should be. Prior work has generally controlled only one of these dimensions at a time. We show that jointly tuning both decisions under a shared compute budget improves both reasoning quality and wall-clock training time. We instantiate this view as \textbf{DU}al-controlled tok\textbf{E}n alloca\textbf{T}ion (DUET), a computationally efficient layer over GRPO that uses a lightweight pre-rollout surrogate of prompt informativeness to set how many rollouts each prompt receives, and a marker-gated abort rule with importance reweighting to set when to stop them. On Qwen3-1.7B trained on MATH, DUET outperforms full-budget GRPO and the other three budget-aware baseline methods. DUET's advantage further generalizes to other benchmarks across math and coding, and is on par with the best baseline on the scientific Q\&A domain, while also achieving a $1.62\times$ wall-clock speedup. More notably, using only 50\% of the token budget, DUET still outperforms all baseline methods at their full budget, achieving an even higher $2.51\times$ speedup over full-budget GRPO. We verify the high performance of DUET on other backbone LLMs, including Qwen3-4B and Llama-3.2-3B-Instruct. Notably, the gap between DUET and the strongest baseline \emph{widens} as the budget tightens, contrary to the usual pattern in which efficient methods trade off quality as compute decreases. More broadly, these results suggest that DUET budget-aware control strategies are valuable not only for accelerating training, but also for improving the quality of the learning signal.

cs.LG↗
arXiv2026

Twisted Bilayer Graphene Lifetimes At Integer Fillings: An Analytic Result

Twisted bilayer graphene near integer fillings hosts correlated single-particle excitations whose dispersion and linewidth are increasingly accessible experimentally. We study these excitations using the topological heavy-fermion model, which captures both strong correlations and band topology of twisted bilayer graphene. In the decoupled limit, where both the single-particle fc hybridization and the Hund coupling between f and c electrons are absent, the model admits exact solutions in which free Dirac fermions coexist with interacting f electrons that form zero-width Hubbard bands. By treating the fc hybridization and Hund coupling perturbatively around this solvable limit, we obtain analytical results for the single-particle self-energy. From the resulting self-energy, we derive explicit expressions for both dispersion renormalization and scattering rates of both Hubbard-band excitations and low-energy Dirac modes, thereby establishing an analytical framework for understanding correlated excitations in twisted bilayer graphene. We analyze the scattering of the two kinds, Gamma3 and Gamma1,2, of Dirac electrons and find that they arise from different mechanisms. We also briefly investigate the effect of strain. Finally, we compare these analytical expressions with DMFT results for the same model.

cond-mat.str-el↗
arXiv2026

Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems

Age estimation systems are increasingly deployed as gatekeepers for age-restricted online content, yet their robustness to cosmetic modifications has not been systematically evaluated. We investigate whether simple, household-accessible cosmetic changes, including beards, grey hair, makeup, and simulated wrinkles, can cause AI age estimators to classify minors as adults. To study this threat at scale without ethical concerns, we simulate these physical attacks on 329 facial images of individuals aged 10 to 21 using a VLM image editor (Gemini 2.5 Flash Image). We then evaluate eight models from our prior benchmark: five specialized architectures (MiVOLO, Custom-Best, Herosan, MiViaLab, DEX) and three vision-language models (Gemini 3 Flash, Gemini 2.5 Flash, GPT-5-Nano). We introduce the Attack Conversion Rate (ACR), defined as the fraction of images predicted as minor at baseline that flip to adult after attack, a population-agnostic metric that does not depend on the ratio of minors to adults in the test set. Our results reveal that a synthetic beard alone achieves 28 to 69 percent ACR across all eight models; combining all four attacks shifts predicted age by +7.7 years on average across all 329 subjects and reaches up to 83 percent ACR; and vision-language models exhibit lower ACR (59 to 71 percent) than specialized models (63 to 83 percent) under the full attack, although the ACR ranges overlap and the difference is not statistically tested. These findings highlight a critical vulnerability in deployed age-verification pipelines and call for adversarial robustness evaluation as a mandatory criterion for model selection.

cs.CV↗
arXiv2026

Why Human Guidance Matters in Collaborative Vibe Coding

Writing code has been one of the most transformative ways for human societies to translate abstract ideas into tangible technologies. Modern AI is changing this process by enabling experts and non-experts alike to generate code without actually writing it, instead using natural language instructions or "vibe coding". While increasingly popular, the impact of vibe coding on productivity and collaboration, and the role of humans in this process, remains unclear. Here, we introduce a controlled experimental framework for studying collaborative vibe coding and use it to compare human-led, AI-led, and hybrid groups. Across 20 experiments involving 737 human participants, we show that people provide uniquely effective high-level instructions for vibe coding, whereas AI-provided instructions often result in performance collapse. We further demonstrate that hybrid systems perform best when humans lead by providing instructions while evaluation is delegated to AI. Although AI systems can rapidly optimize performance for specific tasks, our work highlights the importance of human guidance in shaping future hybrid societies.

cs.HC↗
arXiv2026

Human-AI Synergy Supports Collective Creative Search

Generative AI is increasingly transforming creativity into a hybrid human-artificial process, but its impact on the quality and diversity of creative output remains unclear. We study collective creativity using a controlled word-guessing task that balances open-endedness with an objective measure of task performance. Participants attempt to infer a hidden target word, scored based on the semantic similarity of their guesses to the target, while also observing the best guess from previous players. We compare performance and outcome diversity across human-only, AI-only, and hybrid human-AI groups. Hybrid groups achieve the highest performance while preserving high diversity of guesses. Within hybrid groups, both humans and AI agents systematically adjust their strategies relative to single-agent conditions, suggesting higher-order interaction effects, whereby agents adapt to each other's presence. Although some performance benefits can be reproduced through collaboration between heterogeneous AI systems, human-AI collaboration remains superior, underscoring complementary roles in collective creativity.

cs.SI↗
arXiv2025

Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public

Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design.

cs.HC↗
arXiv2025

Interplay between many-body correlations, strain and lattice relaxation in twisted bilayer graphene

In twisted bilayer graphene, a unified understanding of the mechanisms governing temperature-dependent electronic spectra and thermodynamic properties remains controversial despite extensive theoretical efforts. Here, we present a comprehensive theoretical framework that quantitatively accounts for scanning tunneling spectroscopy, quantum twisting microscopy, and thermodynamic properties of magic angle twisted bilayer graphene. We demonstrate that the observed behavior arises from the interplay between electron correlations and external symmetry-breaking induced by strain and lattice relaxation. These effects act cooperatively to shape the emergent electronic behavior, leaving characteristic signatures across spectroscopy, compressibility and entropy.

cond-mat.str-el↗
arXiv2025

Obtaining the Spectral Function of Moir\'e Graphene Heavy-Fermions Using Iterative Perturbation Theory

The spectral functions of twisted bilayer graphene (TBG) in the absence of strain have recently been investigated in both the symmetric and symmetry-broken phases using dynamical mean-field theory (DMFT). The theoretically predicted Mott-Hubbard bands and gapless semimetallic state at half-filling have since been confirmed experimentally. Here, we develop several second-order perturbation theory approaches to the topological heavy-fermion (THF) model of TBG and twisted symmetric trilayer graphene (TSTG). In the symmetric phase, we adapt, implement, and benchmark an iterative perturbation theory (IPT) impurity solver within DMFT, enabling computationally efficient yet accurate spectral function calculations. We present momentum- and energy-resolved spectra over a broad range of temperatures and fillings for both symmetric and symmetry-broken states. In addition, we derive analytic expressions for the spectral function within the ``Hubbard-I'' approximation of the THF model and, as expected, find that while it provides a tractable description of Mott physics, it does not capture the low-energy Kondo peak or the finite lifetime broadening of the bands. Our methodology can be extended to include strain, lattice relaxation, and parameter variations, thereby allowing systematic predictions of TBG and TSTG spectral properties across a wide range of physical regimes. Because our perturbative approaches are far less computationally intensive than DMFT with numerically exact impurity solvers, they can be used to efficiently benchmark and scan extensive phase diagrams of the THF parameters, paving the way for full DMFT analyses of the TBG spectral function in the presence of strain and relaxation.

cond-mat.str-el↗
arXiv2025

Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking

Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. However, exchanging of intermediate activations and model updates introduces significant privacy risks, especially from data reconstruction attacks that recover original inputs from intermediate representations. Existing defenses using noise injection often degrade model performance. To overcome these challenges, we present PM-SFL, a scalable and privacy-preserving SFL framework that incorporates Probabilistic Mask training to add structured randomness without relying on explicit noise. This mitigates data reconstruction risks while maintaining model utility. To address data heterogeneity, PM-SFL employs personalized mask learning that tailors submodel structures to each client's local data. For system heterogeneity, we introduce a layer-wise knowledge compensation mechanism, enabling clients with varying resources to participate effectively under adaptive model splitting. Theoretical analysis confirms its privacy protection, and experiments on image and wireless sensing tasks demonstrate that PM-SFL consistently improves accuracy, communication efficiency, and robustness to privacy attacks, with particularly strong performance under data and system heterogeneity.

cs.LG↗