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Ao Zhang

Publications and source records attributed to Ao Zhang.

At least 19 recordsLinked to original sources
arXiv2026

World-Model-Augmented Visual Locomotion for Humanoids on Foothold-Constrained Terrain

Foothold-constrained terrain limits feasible footholds to sparse, discontinuous, or narrow support regions, as in gaps, stepping stones, stairs, and balance beams. In such settings, a single misplaced step can cause a fall. Reliable traversal therefore requires precise foot placement and anticipation of upcoming terrain. To address this challenge, we present World-Model-Augmented Visual Locomotion (WM-LOCO), which augments a visual locomotion policy with predictive context from a jointly trained world model. Given onboard depth observations and proprioception, a recurrent state-space model (RSSM) learns action-conditioned latent dynamics that predict future proprioceptive states, egocentric observations, and task rewards. The resulting recurrent feature is then fed into the proximal policy optimization (PPO) actor-critic, providing predictive context for anticipatory foot placement on foothold-constrained terrain. WM-LOCO achieves average success rates of 96.0% on gaps, 96.9% on stepping stones, 94.0% on stairs, and 92.1% on balance beams, outperforming the evaluated baselines while improving gait efficiency and smoothness. Furthermore, WM-LOCO achieves zero-shot sim-to-real transfer to a Unitree G1, with 92.5% success across these four terrain classes.

cs.RO↗
arXiv2026

Measuring the gas mass of the protoplanetary disk around HD 179218 with CO isotopologues

Carbon monoxide (CO) isotopologues are widely used to estimate protoplanetary disk gas masses, but their reliability is often compromised by CO freeze-out and subsequent chemical processing. The disk around the luminous Herbig star HD~179218 ($L_* \sim 100\,L_\odot$, $M_* \sim 3.0\,M_\odot$) offers a rare test case of CO-based mass measurements. Its CO snowline lies beyond the outer disk edge, and thus CO remains in the gas phase throughout. We present ALMA and NOEMA observations of six CO isotopologue lines ($^{13}$CO, C$^{18}$O, and C$^{17}$O, $J$=2--1 and 1--0). Fitting the spectral energy distribution yields $M_{dust} \approx 3 \times 10^{-5}\, M_\odot$. Modeling CO isotopologue emission with the thermochemical code DALI yields the gas-disk characteristic radius ($R_{c} \approx 90$~au) and the gas mass ($M_{gas} \approx 8 \times 10^{-3}\, M_\odot$). These results yield a gas-to-dust ratio of $\sim$270 for our fiducial dust opacity ($a_{\rm max} = 1$~mm), above the canonical interstellar value of $\sim$100. The thermal structure of our best-fitting model confirms that CO never freezes out in this disk, so the elevated ratio is not an artifact of CO depletion; together with the compact millimeter dust disk relative to the gas disk, it is consistent with substantial inward radial drift of millimeter dust. In addition, we find a gas cavity inside 50\,au that encloses the compact millimeter dust disk---the inverse of a typical transition disk---which we attribute to photoevaporative clearing of the inner gas after the pebbles drifted inward. HD~179218 thus provides a clean benchmark for studying the gas mass distribution and gas--dust interaction in protoplanetary disks, free from the CO-depletion biases that affect colder disks.

astro-ph.EP↗
arXiv2026

Unified Heterogeneous Feature Interaction via Effective-Rank Scaling for Recommender Systems

Large-scale industrial ranking models must jointly capture heterogeneous feature interactions, long-term user behaviors, and multi-scenario dependencies under stringent online serving constraints. Existing architectures typically process sequential and non-sequential features with separate modules, limiting bidirectional information exchange. Directly applying homogeneous self-attention to all semantic tokens can also be inefficient when sequential and non-sequential signals exhibit different effective-ranks (eRanks). Meanwhile, shared representations can overlook scenario-specific patterns and induce negative transfer between scenarios. In this paper, we propose HiRank, a unified framework for multi-scenario recommendation that jointly models Heterogeneous feature Interactions via effective-Rank scaling. HiRank integrates gated sequential attention with heterogeneous feature interaction within a unified backbone, enabling bidirectional fusion between sequential and non-sequential representations. HiRank further uses scenario-aware aggregation to compress heterogeneous low-order tokens into a compact representation. Distinct learned projections transform the full compact representation into scenario-aware composite tokens with complementary global feature views and more uniform information density, enabling more effective high-order self-attention. Extensive experiments on industrial-scale datasets demonstrate favorable performance scaling with parameter count and consistent improvements over competitive baselines. Online A/B tests further show a 2.27% increase in orders per user.

cs.IR↗
arXiv2026

ScholarStack: Layered Research Asset Orchestration and Cross-Task Reuse for Scientific Agents

Scientific agents support a range of literature-based research tasks, such as retrieval, question answering, evidence-grounded generation, and claim assessment. Most existing systems, however, are organized around individual tasks: the same papers are repeatedly retrieved, segmented, and interpreted, and the understanding built in one task is difficult to reuse in the next. We present ScholarStack, a layered research asset framework that compiles a paper collection into reusable, versioned, and provenance-preserving assets at three complementary levels: source-grounded paper-level statements, domain-level organization, and evidence-grounded cross-paper syntheses. A common access interface returns task-specific views at the evidence granularity each task requires, preserving study conditions, source traceability, and verification status. We instantiate the framework on four task families spanning ten task settings, comparing agents that use the compiled assets with task-specific baselines under matched base models. Quality gains concentrate on tasks that require cross-paper evidence, such as multi-paper question answering and literature review generation, and query-time token cost falls on every task where it is measured, with assets compiled once and reused across tasks. These results suggest that layered research assets can serve as shared infrastructure for scientific agents, shifting literature-based assistance from isolated document processing toward cumulative, evidence-grounded workflows.

cs.AI↗
arXiv2026

Search for Anisotropic Pair Halos Associated with Blazar Jets

The origin of intergalactic magnetic fields (IGMFs) remains one of the key open questions in cosmology. Gamma-ray pair halos produced by electromagnetic cascades from TeV-emitting blazars provide a powerful indirect probe of these fields. In this work, we present a novel search for pair halos that explicitly exploits their expected anisotropic morphology, aligning with the projected orientation of blazar jets on the sky. Using a Monte Carlo framework to model the spatial distribution of cascade emission, we identify an optimal sample of 21 high-synchrotron-peaked BL Lac objects with well-constrained jet position angles from radio interferometry. By rotating and stacking \textit{Fermi}-LAT observations of these sources along their jet directions, we enhance sensitivity to anisotropic extended emission that would be diluted in traditional orientation-agnostic analyses. Applying a likelihood analysis to the combined dataset, we find evidence for a non-zero IGMF, excluding the null hypothesis at $3.8σ$ level and obtaining a best-fit field strength of $B_0 = 2.8 \times 10^{-16}\,\mathrm{G}$, with a $99\%$ confidence interval of $0.9 \times 10^{-16}\,\mathrm{G} < B_0 < 8.9 \times 10^{-16}\,\mathrm{G}$. Our result is consistent with previous constraints from spectral, spatial, and temporal studies, while demonstrating that incorporating anisotropic information provides a significant gain in sensitivity. This approach opens a new avenue for probing intergalactic magnetism and highlights the potential of future high-angular-resolution gamma-ray observations to directly image pair halos and map magnetic fields in cosmic voids.

astro-ph.HE↗
arXiv2026

Population-scale Ancestral Recombination Graphs with tskit 1.0

Ancestral recombination graphs (ARGs) are an increasingly important component of population and statistical genetics. The tskit library has become key infrastructure for the field, providing an expressive and general representation of ARGs together with a suite of efficient fundamental operations. In this note, we announce tskit version 1.0, describe its underlying rationale, and document its stability guarantees. These guarantees provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.

q-bio.PE↗
arXiv2026

FormaTheoria: Constructing Large-Scale Lean Theories from Mathematical Literature $-$ Toward the Formalization of the Classification of Finite Simple Groups

Large-scale formalization of advanced mathematics requires more than translating individual statements: it must reconstruct a coherent theory distributed across heterogeneous sources. This process raises four challenges: discovering implicit dependencies, correcting source defects, preserving semantic fidelity, and reconciling cross-source misalignments. We present FormaTheoria, an end-to-end, AI-assisted workflow that coordinates source acquisition, formalization, proof construction, recursive dependency discovery, independent review, and reconciliation, while preserving provenance and protecting approved declarations. A shared agent framework supports long-horizon execution through tool use, context compaction, review-gated termination, section-level source context, and dependency-aware batch parallelization. Applying FormaTheoria to major components of the Classification of Finite Simple Groups (CFSG), we construct a machine-checked Lean development extending through the Bender--Suzuki theorem and encompassing the Feit--Thompson Odd Order Theorem, Glauberman's $Z^*$ theorem, and the Brauer--Suzuki theorem. This development verifies an extensive body of deeply interdependent finite-group theory while providing a foundation for continuing the CFSG formalization. An empirical analysis of the code and recorded construction process supports the practical relevance of the identified challenges and illustrates the roles of the corresponding workflow components. Together, these results demonstrate how AI-assisted workflows can reconstruct mathematically significant formal theories from distributed literature by combining language-model agents with formal verification, structured review, and explicit dependency management.

cs.LO↗
arXiv2026

Entropy Production and Reversibility Criteria for Stochastic Evolution Equations

This paper develops a path-space theory of entropy production for a class of stochastic evolution equations on infinite-dimensional Hilbert spaces. Since such spaces have no canonical Lebesgue reference measure, the usual finite-dimensional density formulas do not extend directly. We instead work relative to the invariant Gaussian measure of a reversible Ornstein--Uhlenbeck reference process. Combining an infinite-dimensional Girsanov transform, time reversal of the reference process, and the stationary Fokker--Planck equation relative to the Gaussian measure, we derive an explicit entropy-production formula in terms of an irreversibility field. On the natural test class, this field represents the difference between the forward and reversed nonlinear drifts. Under the standing assumptions, vanishing entropy production is equivalent to vanishing stationary probability current, self-adjointness of the generator in the invariant Hilbert space, detailed balance, and invariance of the stationary path law under time reversal. The reversible case is therefore characterized by a Gaussian-reference gradient structure for the nonlinear drift.

math.AP↗
arXiv2026

Physics-Guided Spectral Parametric Reduced-Order Modeling for Transient Prediction of Controlled Dynamical Systems

Efficient parametric transient prediction at unseen parameter values and under new operating conditions remains challenging because repeated high-fidelity simulations are computationally prohibitive. Existing data-driven surrogates and parametric reduced-order models perform well within sampled ranges but often lose reliability beyond them. This study proposes a physics-guided spectral parametric reduced-order modeling framework for controlled dynamical systems. Parameter-dependent reduced spectral operators are identified from transient snapshots using Dynamic Mode Decomposition with control, separating intrinsic dynamics from external control effects. After physics-guided parameter transformation, aligned spectral quantities and reduced operator components are propagated across parameter conditions using Secondary Dynamic Mode Decomposition, with linear and radial basis function regressions as baselines. Baseline regularization and nondimensional time mapping improve robustness. Validation uses a mechanical transmission system, a Helium-Xenon closed Brayton cycle, and a Karman vortex street, covering linear transient, nonlinear transient, and nonlinear periodic dynamics. For the mechanical and Brayton systems, system-level multivariable responses at unseen parameter values and under new operating conditions are predicted with relative norm errors below 1%. For the vortex-street system, nondimensional time mapping preserves dominant vortex-shedding structures across Reynolds numbers. Further analyses compare the framework with an LSTM surrogate and assess extrapolation confidence using an auxiliary error-prediction model. Overall, the framework extends parametric reduced-order modeling from in-domain approximation toward extrapolative transient prediction of controlled dynamical systems under unseen conditions.

math.DS↗
arXiv2026

Heterostructuring as Gateway to Electron Doping of Nickelate Superconductors

Despite enormous expenditures in the research field, the electron-doped side of nickelate superconductors remains uncharted territory. Substituting the trivalent rare-earth cations by a tetravalent one hitherto failed. Here, we demonstrate by first-principles calculations a disorder-free route to electron dope Ruddlesden-Popper nickelates. When intercalating wide-band-gap insulating layers such as La$X$O$_3$ ($X$=Al, Ga, Sc) into La$_2$NiO$_4$, the extra (LaO)$^+$ layers act as electron donors, releasing carriers into the Ni-3$d$ orbitals. This electron doping puts La$_2$NiO$_4$:La$_2$AlO$_4$ naturally in the optimal region for $d_{x^2-y^2}$-wave superconductivity with T$_c$ exceeding 50 K. The same concept also allows us to electron dope La$_3$Ni$_2$O$_7$, the superconductor in the limelight.

cond-mat.supr-con↗
arXiv2026

A linear, decoupled and positivity-preserving time-staggered block-centered finite difference method for the multi-species Keller-Segel chemotaxis system

In this paper, we present a linearly implicit, second-order block-centered finite difference (BCFD) prediction-then-projection scheme for the multi-species Keller-Segel chemotaxis system on non-uniform spatio-temporal grids. The proposed scheme integrates a standard Crank-Nicolson time-marching algorithm with an $L^2$ projection step to enforce positivity and mass conservation. The use of variable time stepsize and time-staggered discretization fully decouples the solutions of the multi-species cell density variables and the chemoattractant concentration variable while facilitating linearization, thereby greatly enhancing computational efficiency. Notably, the variable time-stepping algorithm and non-uniform grid BCFD discretization jointly enable adaptive resolution and local refinement near blow-up, thereby improving efficiency and accuracy without compromising the desired physical property-preserving in the simulation. Furthermore, using the mathematical induction method and the energy analysis approach, the unique solvability of the proposed scheme is rigorously proved, and we show that cell densities achieve second-order convergence in both time and space in the discrete $L^2$ norm, while the chemoattractant concentration achieves second-order convergence in the discrete $H^1$ norm. Representative numerical experiments are presented to validate the theoretical findings and demonstrate the reliability of the proposed scheme in simulating the blow-up phenomenon.

math.NA↗
arXiv2026

A variable time-step, second-order, and MBP-preserving linear stabilized scheme for the time-fractional Allen-Cahn equation

In this paper, we present a second-order linear scheme based on the variable-step Alikhanov formula and central difference discretization for the time-fractional Allen-Cahn equation. The nonlinear potential is treated explicitly via a second-order extrapolation with preprocessing, which enables the discrete maximum-bound principle (MBP) to be preserved through an appropriate stabilization technique. Moreover, by developing a discrete fractional Grönwall inequality together with the uniform boundedness of numerical solutions guaranteed by the MBP, we establish an $α$-robust and optimal second-order maximum-norm error estimate under initial weak singularity assumption. In addition, energy stability is proved in the sense that the discrete original energy is uniformly bounded by the initial energy plus a high-order spatiotemporal correction term. Finally, extensive numerical experiments are presented to demonstrate the effectiveness of the proposed scheme.

math.NA↗
arXiv2026

Proton-electron coupled catalyst for ionomer-free electrochemical energy conversion

Efficient electrochemical energy devices are vital to renewable energy technology, yet coordinating the effective flow of electrons, ions, and chemical species continues to be a major challenge. In conventional proton-exchange membrane fuel cell (PEMFC) catalyst layers, proton and electron transport are supplied separately through percolating carbon networks and ionomer binders, rendering the catalyst largely passive and imposing fundamental trade-offs between reactant accessibility, ionic conductivity, and catalyst activity. Here, we introduce a one-dimensional proton-electron coupled catalyst (PECC) design, a transport-integrated electrocatalyst architecture in which the catalyst itself simultaneously supplies electronic and protonic transport to catalyst active sites. Using this PECC, PEMFCs can have an ionomer-free cathode catalyst layer (CCL), resulting in a dramatic 95% reduction in non-Fickian oxygen transport and boosting power density by 34% and 85% compared to traditional CCLs, with cathode Pt loadings of approximately 0.090 mg/cm^2 and 0.037 mg/cm^2, respectively. Meanwhile, PECC retains 65% of its mass activity and exhibits 32% higher power density than its ionomer-based CCL counterpart after 30k accelerated stressed test. Similar mass transport improvements have been observed in the electrochemical hydrogen pump (EHP) using PECC in the catalyst layers. Molecular dynamics simulations show the PECC's proton conductivity is 249% higher than Nafion. This PECC catalyst structure addresses core transport problems in PEMFCs, leading to almost 20% improvement in fuel efficiency and opens up new possibilities for designing high-performance, cost-effective electrochemical devices.

cond-mat.mtrl-sci↗
arXiv2026

Order Flow Exclusivity and Value Extraction Mechanisms: An Analysis of Ethereum Builder Centralization

This study investigates the rapid centralization of the Ethereum builder market under the Proposer-Builder Separation (PBS) architecture. We argue that existing research, by focusing predominantly on influential order flows, lacks a comprehensive evaluation of order flow behavioral patterns and economic purposes. To address this gap, we analyze Ethereum transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV) -- the missing components corresponding to these behavioral and economic dimensions, respectively. We introduce a novel exclusivity metric based on Kullback-Leibler divergence and employ supervised learning to identify 75 EOFs and 322 non-atomic MEV flows, which account for 71\% and 23\% of trading-related builder revenue. A longitudinal analysis of builder strategies across these dimensions delineates the market's evolution into four distinct eras, revealing that while EOFs were instrumental in establishing early dominance, incumbents have since decoupled market share from immediate EOF dependency by leveraging entrenched network effects. Ultimately, we conclude that builder centralization is an emergent property of the PBS framework itself, as the architecture systematically violates the fundamental prerequisites of a competitive market.

cs.CR↗
arXiv2026

Rippled graphene pores as fluidic memristive devices with synaptic and neuromorphic functionalities

Nanofluidic memristive devices work with nanoscale pores and ions dissolved in water, which harness the ionic memory effect aiming to store and process information. These devices share the same charge carriers as biological systems and bring hope for better emulating the neural functions and developing ionic circuits for neuromorphic applications. Specially, theory and experiments suggest that nanoconfinement is essential for inducing a memory effect, which places limit on the pore size to nm-scale or smaller. Such devices are difficult to scale up with precision and operate with long-term stability. Here, we show that a micrometer size pore, generally expected to exhibit a linear ion transport, can display a pronounced memory effect, if its rim is wrapped by strongly curved and tightly stacked graphene. We attribute the observation to slow ion dynamics confined in the rippled graphene edges. The devices are easy to scale up and integrate into fluidic circuits. The memory effect is ion-selective and exhibits long endurance comparable to the lifetime of synaptic proteins, which enables reversible modification of the conductance states using programmable voltage spikes and various electrolytes over a long time, akin to biological synaptic plasticity. Thanks to this plasticity, our devices and their integrated circuits enable storing, transmitting and processing information with high reliability, fidelity and accuracy, as evidenced in the identification of both greyscale and color images, and in the real-time analysis of emulated neural signals. Our results highlight nanoscale morphology of the pore wall as an important parameter regulating ion transport and indicate that the stringent nanoconfinement for ionic memory can be lifted from restricting the pore size to designing its rim structure. The devices and their integrated circuits may find use in ionic neuromorphic applications.

cond-mat.mtrl-sci↗
arXiv2026

PegasusFlow: Parallel Rolling-Denoising Score Sampling for Robot Diffusion Planner Flow Matching

Diffusion models offer powerful generative capabilities for robot trajectory planning, yet their practical deployment on robots is hindered by a critical bottleneck: a reliance on imitation learning from expert demonstrations. This paradigm is often impractical for specialized robots where data is scarce and creates an inefficient, theoretically suboptimal training pipeline. To overcome this, we introduce PegasusFlow, a hierarchical rolling-denoising framework that enables direct and parallel sampling of trajectory score gradients from environmental interaction, completely bypassing the need for expert data. Our core innovation is a novel sampling algorithm, Weighted Basis Function Optimization (WBFO), which leverages spline basis representations to achieve superior sample efficiency and faster convergence compared to traditional methods like MPPI. The framework is embedded within a scalable, asynchronous parallel simulation architecture that supports massively parallel rollouts for efficient data collection. Extensive experiments on trajectory optimization and robotic navigation tasks demonstrate that our approach, particularly Action-Value WBFO (AVWBFO) combined with a reinforcement learning warm-start, significantly outperforms baselines. In a challenging barrier-crossing task, our method achieved a 100% success rate and was 18% faster than the next-best method, validating its effectiveness for complex terrain locomotion planning. https://masteryip.github.io/pegasusflow.github.io/

cs.RO↗
arXiv2026

CSAI: Conditional Self-Attention Imputation for Healthcare Time-series

We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address the challenges of complex missing data patterns in multivariate time series derived from hospital electronic health records (EHRs). CSAI extends state-of-the-art neural network-based imputation by introducing key modifications specific to EHR data: a) attention-based hidden state initialisation to capture both long- and short-range temporal dependencies prevalent in EHRs, b) domain-informed temporal decay to mimic clinical data recording patterns, and c) a non-uniform masking strategy that models non-random missingness by calibrating weights according to both temporal and cross-sectional data characteristics. Comprehensive evaluation across four EHR benchmark datasets demonstrates CSAI's effectiveness compared to state-of-the-art architectures in data restoration and downstream tasks. CSAI is integrated into PyPOTS, an open-source Python toolbox designed for machine learning tasks on partially observed time series. This work significantly advances the state of neural network imputation applied to EHRs by more closely aligning algorithmic imputation with clinical realities.

cs.LG↗
arXiv2025

Singular Perturbations of Nonlocal HJB Equations in Multiscale Stochastic Control

This paper investigates a class of multiscale stochastic control problems driven by $α$-stable Lévy noises, where the controlled dynamics evolve across separate slow and fast time scales. The associated value functions are governed by a family of nonlocal Hamilton-Jacobi-Bellman (HJB) equations subject to singular perturbations. By employing the perturbed test function method, we carefully analyze this singular perturbation problem and derive a limiting effective equation as the time-scale separation parameter $\varepsilon$ approaches zero. This limiting equation characterizes the value function of the averaged control problem, thereby establishing a rigorous averaging principle for the original multiscale system. The effective Hamiltonian-along with the corresponding averaged control problem is obtained by averaging with respect to the invariant measure of the fast process. Moreover, we provide a probabilistic proof of convergence and establish an explicit convergence rate for the value functions.

math.OC↗