arXiv subjects

Qi Feng

Publications and source records attributed to Qi Feng.

At least 19 recordsLinked to original sources

arXiv2026

Mode Coverage in Normalizing Flow Boltzmann Generators via Log-Ratio Variation

Normalizing flow Boltzmann generators retain a tractable pushforward density, but training with forward KL depends on target samples that may be biased or omit modes. As a result, a flow can miss target mass while its observed importance weights give a high effective sample size. We introduce the log-ratio variation $\X_\omega$, the mean absolute pairwise difference of the target-to-pushforward log-density ratio under a weighting measure $\omega$, and use it to define KLXX, a new loss function. Two log-ratio variations are added to the forward KL (denoted by the two X's): one weighted by the target to improve accuracy, the other by a mixture of quench and temper samples with pushforward samples to search candidate modes. We derive the Fisher--Rao gradient flow of KLXX, where both variations contribute nonpositive dissipation, and a fixed-surrogate error bound for KLXX. We use KLXX in an adaptive-staging Boltzmann generator, with importance reweighting at every stage. We bound the sampling error of its inference scheme when the stage weights are essentially bounded, and prove it asymptotically unbiased in the sample size. In the numerical tests, KLXX improves mode coverage over forward KL. It also improves the generator's per-stage diagnostics against the loss that built the schedule. The observables the generator recovers are close to independent references. The log-ratio variations thus supply information that the forward KL loss usually omits.

stat.ML↗

arXiv2026

The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory

Long-term language-model agents accumulate memories across interactions, but their retrievers typically do not accumulate retrieval experience. Semantic retrieval is efficient, but embedding similarity does not always reflect whether a memory contains evidence relevant to the current query. Large language model (LLM) rerankers provide stronger query-conditioned relevance scores, yet stateless reranking repeatedly scores a large candidate pool and discards these scores after each query. We introduce EARM, an experience-amortized reranking framework that treats previously acquired LLM relevance scores as reusable retrieval experience. EARM stores sparse query--memory relevance scores in an online matrix, learns their shared structure through causal matrix completion, and combines a small set of newly observed scores with estimated scores to rerank the remaining candidates. The scoring budget decreases as experience accumulates, changing LLM reranking from a repeated per-query expense into a retrieval capability learned over an agent's lifetime. Experiments on long-term conversational memory show that mixed observed-and-estimated reranking improves answer accuracy over semantic retrieval by up to 6.62% and remains effective when only 17.5% of candidates receive direct LLM relevance scores, thereby substantially reducing the inference overhead of LLM reranking. These results motivate a broader view of agent memory: a long-lived agent should remember not only past content, but also how that content has proved useful for retrieval.

cs.AI↗

arXiv2026

N\'eel-order-dependent transverse transport in noncoplanar antiferromagnet $\text{MnTe}_{2}$

Antiferromagnets hold appealing potential in next-generation spintronic devices with higher frequency and scalability, thanks to their alternating spin orientations that cancel out net magnetization. However, the lack of a nonzero magnetization makes the detection of the magnetic configuration of antiferromagnet difficult, hampering the applications of antiferromagnets. Here, we report a new transverse transport effect in noncoplanar antiferromagnet $\text{MnTe}_{2}$. This effect is antisymmetric in both magnetic field and N\'eel order, but symmetric in its two indices. It can be understood in terms of the contribution induced by both magnetic field and geometric quantities, as confirmed by our theoretical calculations. Our discovery of a new N\'eel-order-dependent transverse transport effect provides opportunities to the advancing antiferromagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Sharp hypocoercive convergence estimates for underdamped Langevin dynamics with specular reflection

We study the underdamped (kinetic) Langevin dynamics confined to a bounded convex domain $\Omega\subset\mathbb{R}^d$ by specular reflection of the velocity at the boundary. This process is the natural momentum-based analogue of the normally reflected overdamped Langevin diffusion, and it is used in practice for constrained sampling; however, no explicit quantitative convergence rate is available in the literature. We provide the first such rate. Assuming only that the position marginal $\mu_x\propto e^{-U}$ satisfies a Poincar\'e inequality on $\Omega$ with constant $m>0$ and that $\nabla^2U\succeq-K\,\mathrm{Id}$, we prove that the law converges to the Gibbs measure exponentially fast in $L^2$, with an explicit rate that scales like $\sqrt m$, which is optimal when $U$ is convex. Since the normally reflected overdamped dynamics converges exactly at rate $m$, this establishes a square-root acceleration for constrained sampling in the small-gap regime when $m$ is small, matching the acceleration known in the unconstrained case. The proof adapts the modified $L^2$ hypocoercivity method of Dolbeault--Mouhot--Schmeiser with the gap-shifted corrector of Fan--Li--Lu. The specular symmetry makes the transport operator antisymmetric, and that the corrector automatically selects the Neumann realization of the overdamped generator, which is precisely the boundary condition that keeps every auxiliary function inside the specular class. The Bochner identity used in the whole-space argument is replaced by a weighted Reilly formula, whose boundary contribution involves the second fundamental form of $\partial\Omega$ and is nonnegative for convex $\Omega$.

math.PR↗

arXiv2026

Symplectic dynamics via a tube construction

This is the first in a series of papers studying symplectic dynamics on Liouville domains $(W, \lambda)$ from a geometric approach. We employ Usher's tube construction to build a higher-dimensional Liouville domain from $(W,\lambda; H)$, where $H$ is a Hamiltonian function on $(W, \lambda)$, possibly non-autonomous. We establish dynamical stabilities with respect to a new Thompson-type pseudo-metric on Hamiltonians, bounded below by Banach--Mazur type distances. We also explore relations between the topological entropies of the Hamiltonian dynamics and its induced Reeb dynamics, examine symplectic capacities, especially the Gromov width, under the tube construction, and give a categorification of the Thompson-type metric via a generalized tube construction (called the tunnel construction). Finally, we investigate the relation between the filtered symplectic homology of the resulting tube and the Hamiltonian Floer homologies of the input Hamiltonians, as well as their iterates, on the given base $(W, \lambda)$.

math.SG↗

arXiv2026

A Multiwavelength Study of a Long-Duration VHE Flare from BL Lacertae with VERITAS

We report the first observations of a long-duration very-high-energy (VHE; $E > 100$ GeV) flare from BL Lacertae (VER J2202+422), taken with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). On October 15, 2022, the Fermi-Large Area Telescope (LAT) detected elevated GeV activity originating from this blazar. This triggered a multiwavelength campaign, which includes observations from VERITAS, Swift, NuSTAR, and select optical and radio observatories. VERITAS observed the source for a total of $\sim 9.8$ hours between September 1, 2022 and December 1, 2022. An analysis of these data yields a $\sim 28 \sigma$ detection of the source. While previously observed VHE flares from BL Lacertae have lasted on time-scales of minutes to days, VERITAS continued to detect flaring activity from the source for over a month ($\sim 40$ days) after the original flaring activity was detected with Fermi-LAT. Broadband spectral modeling shows that a synchrotron self-Compton (SSC) model with an external inverse-Compton (EC) component is preferred over a one-zone SSC model.

astro-ph.HE↗

arXiv2026

Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals

We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a \emph{single observed trajectory} of a possibly rough forcing signal--a setting common within earthquake engineering, finance, biology, and structural health monitoring, where only one forcing realization is available, and the solver must respect the underlying physical law without an ensemble of realizations. We first introduce a count-sampling construction method to turn the single observation into a hierarchical family of $N+1$ nested training paths on which the branched signature kernel can be evaluated; this allows the signature kernel machinery, originally designed for multi-realization regression problems, to operate on a single-trajectory observation. Then we build a kernel-collocation framework, which places the ansatz either on the highest-order derivative of the solution or on the solution itself. We prove a universal approximation theorem for the branched signature kernel, leveraging the Hairer--Kelly morphism to express branched signature evaluations through geometric signatures of time-extended paths. The offline solver is extended to a streaming Test/Train/Retrain protocol with optional closed-form online updates in both linear and nonlinear cases. Numerical experiments on six benchmarks show accurate, stable predictions across all regimes.

math.NA↗

arXiv2026

Nonlinear Hall quantum oscillations to probe topological Brown-Zak fermions in graphene moir\'e systems

Due to the deep connection with the quantum geometry of electronic Bloch wavefunctions, the second-order nonlinear Hall effect (NLHE) has been an attractive topic since its proposal. However, studies on NLHE under a magnetic field have been lacking. Given that quantum oscillations in the linear response regime have been proven to be useful tools in investigating electronic systems, searching for quantum oscillations in NLHE is of great interest and is expected to provide new avenues to unveil rich quantum geometric properties of novel quasiparticles. Here, we propose a new type of NLHE quantum oscillations and experimentally probe it in graphene moir\'e systems. It stems from the alternation of the dominant NLHE mechanisms with recurring Bloch states under magnetic field, which enables sensitive detection of Brown-Zak fermions, giving an onset field as low as 0.5 T. Most importantly, when the commensurability condition is satisfied, the nonlinear transport of Brown-Zak fermions is mainly governed by quantum geometric contributions. Our findings not only establish a new type of quantum oscillations, but also demonstrate the first experimental detection of the topological nature of Brown-Zak fermions, shedding light on the exploration of novel topological quasiparticles.

cond-mat.mes-hall↗

arXiv2026

Efficient Generation and Quality Screening of Visible Windows for Regional SAR Reconnaissance

Regional synthetic aperture radar reconnaissance requires observation windows that satisfy geometric feasibility under side-looking constraints and deliver interpretable image quality. This paper develops an efficient framework for visible window generation and per-window signal-level quality assessment. Window construction proceeds through three stages: coarse angular bandpass screening eliminates orbit arcs without potential target intersection, a planar characteristic curve containment test on the sensor calculation plane determines the precise geometry feasible intervals, and one-dimensional boundary bisection resolves each entry and exit epoch to subsecond precision. Each geometry feasible window then undergoes a companion point target stripmap simulation that measures range and azimuth impulse response width, peak sidelobe ratio, and integrated sidelobe ratio against mission-dependent acceptance thresholds. Numerical experiments validate the three-stage generation pipeline against an independent STK reference and demonstrate that the quality screening procedure differentiates imaging performance across observation windows with measurably different geometry. The framework provides an auditable preprocessing stage that converts continuous time regional visibility into quality-qualified observation windows suitable for subsequent mission planning.

physics.geo-ph↗

arXiv2026

Can Heterogeneous Language Models Be Fused?

Model merging aims to integrate multiple expert models into a single model that inherits their complementary strengths without incurring the inference-time cost of ensembling. Recent progress has shown that merging can be highly effective when all source models are \emph{homogeneous}, i.e., derived from the same pretrained backbone and therefore share aligned parameter coordinates or compatible task vectors. Yet this assumption is increasingly unrealistic in open model ecosystems, where useful experts are often built on different families such as Llama, Qwen, and Mistral. In such \emph{heterogeneous} settings, direct weight-space fusion becomes ill-posed due to architectural mismatch, latent basis misalignment, and amplified cross-source conflict. We address this problem with \texttt{HeteroFusion} for heterogeneous language model fusion, which consists of two key components: topology-based alignment that transfers knowledge across heterogeneous backbones by matching functional module structures instead of raw tensor coordinates, and conflict-aware denoising that suppresses incompatible or noisy transfer signals during fusion. We further provide analytical justification showing that preserving the target adapter basis while predicting structured updates leads to a stable and well-conditioned transfer process. Across heterogeneous transfer, multi-source fusion, noisy-source robustness, and cross-family generalization settings, \texttt{HeteroFusion} consistently outperforms strong merging, fusion, and ensemble baselines.

cs.AI↗

arXiv2026

Interpreting Swift and NuSTAR Observations of the Low-Luminosity Active Galactic Nucleus NGC 4278 with Radiatively Inefficient Accretion Flows and Implications for Neutrino Emission

We report the first NuSTAR hard X-ray observations of the low-luminosity active galactic nucleus NGC 4278. The source is clearly detected beyond 10 keV with a hard X-ray spectrum consistent with a power law of photon index between 2.2 and 2.5 without evidence for a high-energy cutoff. The X-ray flux is low compared to the active state in 2021, but exhibits variability by a factor of ~2 on a timescale of a month. We discuss the origin of the hard X-ray emission and explore its connection to gamma rays and high-energy neutrinos. We explain the X-ray data, including both quiescent and active states, using a radiatively inefficient accretion flow (RIAF) model with a variable accretion rate. We also show that TeV gamma rays cannot escape from the RIAF disk, and very high-energy gamma rays observed in LHAASO are likely to originate from outer regions such as jets and winds, which is consistent with our results favoring a magnetically arrested disk. We also discuss hidden neutrino emission from RIAFs together with possible connections to coronae of active galactic nuclei with standard, radiatively efficient disks.

astro-ph.HE↗

arXiv2026

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning

Learning universal graph representations across heterogeneous domains is difficult because graph datasets differ in topology, node-attribute semantics, feature dimensions, and even attribute availability. We propose GraphVec, a language-model-free graph vectorization model that maps diverse graphs into transferable fixed-dimensional embeddings for graph-level tasks. Instead of directly using incomparable raw node attributes, GraphVec constructs multi-scale global graphs over all nodes in each dataset and extracts spectral embeddings to obtain domain-agnostic relational features. To make these spectral features comparable across datasets, we introduce a density-maximization mean alignment algorithm over orthogonal transformations and prove its monotonic convergence. GraphVec further combines a GIN--Graph Transformer backbone with a multi-layer reference distribution module, which preserves node-level distributional information beyond standard pooling. We also provide a generalization error bound for the proposed model. Experiments on 13 datasets with more than 15 comparison methods demonstrate that GraphVec consistently outperforms strong graph pretraining baselines in cross-domain few-shot graph classification and graph clustering. Beyond graph-level tasks, GraphVec also yields strong node-level representations, achieving competitive performance on few-shot node classification against representative graph prompt learning methods.

cs.LG↗

arXiv2026

An Analytic Solution to the Optimal Spherical Dubins Path Problem with Geodesic Curvature Constraints

Computing shortest paths for curvature-constrained Dubins vehicles on the unit sphere is fundamental to many engineering applications, including long-range flight planning, persistent surveillance patterns, and global routing problems where great circles are natural routes. Numerical optimization methods on $\SO(3)$ suffer from sensitivity to initialization, may converge to local minima, and often miss feasible solution branches. This paper proposes a unified analytic computational approach for spherical Dubins CGC and CCC paths that overcomes these limitations. By exploiting the axis-fixing property of rotations and developing a closed-form back-substitution method using geometric projection, the three-dimensional boundary value problem is reduced to solving a quadratic polynomial equation. The proposed analytic solver achieves machine precision accuracy with errors on the order of $10^{-16}$, is approximately $717$ times faster than numerical methods under the same computational environment, and systematically enumerates all feasible solution branches without requiring exhaustive multi-start initialization. The method provides closed-form solutions for optimal path computation in the regime where turning radius $\Rturn \in (0, 1/2]$, corresponding to $U_{\max} \geq \sqrt{3}$.

physics.geo-ph↗

arXiv2025

Design and Performance of the Upgraded Prototype Schwarzschild-Couder Telescope Camera Module

The Cherenkov Telescope Array Observatory (CTAO) is a ground-based observatory that will improve upon the sensitivities of the current generation of very-high-energy gamma-ray instruments. The Schwarzschild-Couder Telescope (SCT) is a dual-mirror candidate design for a CTAO Medium-Sized Telescope (MST). The prototype Schwarzschild-Couder Telescope (pSCT) was inaugurated in 2019 at Fred Lawrence Whipple Observatory (FLWO) in Arizona and observed significant gamma-ray emission from the Crab Nebula with a partially populated camera. The pSCT camera is currently being upgraded to fully instrument the focal plane with 11,328 silicon photomultiplier (SiPM) pixels split between 177 camera modules. Additionally, the modules will feature upgraded electronics designed to reduce electronics crosstalk and noise. A module calibration procedure has been developed using a preproduction test module. Following this calibration procedure, performance testing shows that the upgrade module has low noise, minimal electronics crosstalk, and excellent charge resolution. After calibration and optimization, the 177 production modules will be installed in the pSCT camera for commissioning. This will be followed by observations of known VHE gamma-ray sources for camera performance validation.

astro-ph.IM↗

arXiv2025

Deep Neural Operator Learning for Probabilistic Models

We propose a deep neural-operator framework for a general class of probability models. Under global Lipschitz conditions on the operator over the entire Euclidean space-and for a broad class of probabilistic models-we establish a universal approximation theorem with explicit network-size bounds for the proposed architecture. The underlying stochastic processes are required only to satisfy integrability and general tail-probability conditions. We verify these assumptions for both European and American option-pricing problems within the forward-backward SDE (FBSDE) framework, which in turn covers a broad class of operators arising from parabolic PDEs, with or without free boundaries. Finally, we present a numerical example for a basket of American options, demonstrating that the learned model produces optimal stopping boundaries for new strike prices without retraining.

cs.LG↗

arXiv2025

Data-driven Feynman-Kac Discovery with Applications to Prediction and Data Generation

In this paper, we propose a novel data-driven framework for discovering probabilistic laws underlying the Feynman-Kac formula. Specifically, we introduce the first stochastic SINDy method formulated under the risk-neutral probability measure to recover the backward stochastic differential equation (BSDE) from a single pair of stock and option trajectories. Unlike existing approaches to identifying stochastic differential equations-which typically require ergodicity-our framework leverages the risk-neutral measure, thereby eliminating the ergodicity assumption and enabling BSDE recovery from limited financial time series data. Using this algorithm, we are able not only to make forward-looking predictions but also to generate new synthetic data paths consistent with the underlying probabilistic law.

q-fin.MF↗

arXiv2025

Branched Signature Model

In this paper, we introduce the branched signature model, motivated by the branched rough path framework of [Gubinelli, Journal of Differential Equations, 248(4), 2010], which generalizes the classical geometric rough path. We establish a universal approximation theorem for the branched signature model and demonstrate that iterative compositions of lower-level signature maps can approximate higher-level signatures. Furthermore, building on the existence of the extension map proposed in [Hairer-Kelly. Annales de l'Institue Henri Poincar\'e, Probabilit\'es et Statistiques 51, no. 1 (2015)], we show how to explicitly construct the extension of the original paths into higher-dimensional spaces via a map $\Psi$, so that the branched signature can be realized as the classical geometric signature of the extended path. This framework not only provides an efficient computational scheme for branched signatures but also opens new avenues for data-driven modeling and applications.

math.NA↗

arXiv2025

Noise estimation of SDE from a single data trajectory

In this paper, we propose a data-driven framework for model discovery of stochastic differential equations (SDEs) from a single trajectory, without requiring the ergodicity or stationary assumption on the underlying continuous process. By combining (stochastic) Taylor expansions with Girsanov transformations, and using the drift function's initial value as input, we construct drift estimators while simultaneously recovering the model noise. This allows us to recover the underlying $\mathbb P$ Brownian motion increments. Building on these estimators, we introduce the first stochastic Sparse Identification of Stochastic Differential Equation (SSISDE) algorithm, capable of identifying the governing SDE dynamics from a single observed trajectory without requiring ergodicity or stationarity. To validate the proposed approach, we conduct numerical experiments with both linear and quadratic drift-diffusion functions. Among these, the Black-Scholes SDE is included as a representative case of a system that does not satisfy ergodicity or stationarity.

q-fin.ST↗