arXiv subjects

Xinyu Long

Publications and source records attributed to Xinyu Long.

4 recordsLinked to original sources

arXiv2026

Lattice balls with large additive energy in discrete cubes

For a finite set $A$ in an abelian group, let \[ E(A)=\#\{(a_1,a_2,a_3,a_4)\in A^4:a_1+a_2=a_3+a_4\}. \] We obtain an estimate uniform in $d$ that compares the normalized additive energy of $\mathbb{Z}^d \cap B_d(R)$ with the continuous energy of $B_d(R)$ . If $R_d/\sqrt d\to\infty$, then \[ \lim_{d\to\infty} \left( \frac{E\bigl(\mathbb{Z}^d\cap B_d(R_d)\bigr)} {\lvert \mathbb{Z}^d\cap B_d(R_d)\rvert^3} \right)^{1/d} =\frac{4\sqrt{3}}{9}. \] As an application, consider \[ A_n = R_n\mathbf{1}_d + \bigl(\mathbb{Z}^d\cap B_d(R_n)\bigr), \] where $d=d(n)\to\infty$ satisfy $\log d=o(\log n)$, and $R_n=\lfloor(n-1)/2\rfloor$. Then $A_n\subset\{0,1,\ldots,n-1\}^d$ and \[ \log E(A_n) =3\log|A_n|-d\log\frac{3\sqrt3}{4}+o(d). \] In particular, taking $d=\lfloor(\log n)^{1/2}\rfloor$ gives an explicit construction answering a question of Shao \cite{Shao2026}. We also prove that in Gram-matrix coordinates, the exponential rate of the continuous ball energy is determined by a fixed dimensional determinant maximization whose extremizer is the Gram matrix of a regular tetrahedron.

math.CO↗

arXiv2026

From Micro-Cognition to Self-Construction: A Four-Layer Integrative Review of Psychological Theories in HCI

Human-computer interaction (HCI) is undergoing a paradigm shift from "tool use" toward "partnership" and even "mind symbiosis," with psychology evolving from a supplementary explanatory tool to a core pillar shaping interaction paradigms and long-term relationships. This paper systematically reviews relevant research and proposes a four-layer integrative framework comprising the Micro-cognitive, Meso-affective, Macro-social, and Self-constructive layers. The framework reveals that: the cognitive layer constitutes the foundation of interaction, the affective layer drives relational engagement, the social layer regulates trust and behavior through norms, and the self-constructive layer points to the ultimate goal of human-machine symbiosis. These four layers follow a progressive logic of "foundation--mediation--context--goal." The review further argues that psychology has shifted from "post-hoc evaluation" to a "proactive design paradigm," with direct implications for emerging scenarios such as generative AI and embodied agents. Concurrently, this paper identifies core challenges including the nature of affect, agency erosion, and ethical risks, and proposes future directions such as bidirectional theory of mind and personalized co-evolution. This framework offers a systematic theoretical perspective for understanding complex human-machine relationships.

cs.CY↗

arXiv2026

RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines

Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features. While current quantum hardware is not yet practical for direct large-scale vision-language model (VLM) inference, simulated quantum computation can be used during model construction to generate structured parameters for compact classical AI systems. We build RiverONE, a lightweight vision-language model for quantum calibration plot understanding, using simulated quantum computation. It employs a specialized visual encoder and an InternVL-based language backbone. To compensate for compression-induced information loss, we introduce quantum-generated parameters, which are materialized as classical tensors after training. This allows RiverONE to run entirely on classical GPUs at inference time, with no quantum hardware or runtime quantum simulation. With approximately 1.9 billion parameters, RiverONE achieves at least 95\% of the performance of NVIDIA Ising Calibration 1 on quantum calibration plot understanding tasks while using less than 10\% of its parameter count. These results suggest that simulated quantum computation can serve as a practical construction-stage mechanism for building lightweight, knowledge-intensive scientific VLMs. Our code is available at https://github.com/THeWakeSystems/RiverOne.

quant-ph↗

arXiv2026

Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields

Accurate forecasting of three-dimensional (3D) cloud fields is important for atmospheric analysis and short-range numerical weather prediction, yet it remains challenging because cloud evolution involves cross-layer interactions, nonlocal dependencies, and multiscale spatiotemporal dynamics. Existing spatiotemporal prediction models based on convolutions, recurrence, or attention often rely on locality-biased representations and therefore struggle to preserve fine cloud structures in volumetric forecasting tasks. To address this issue, we propose QENO, a hybrid quantum-inspired spatiotemporal forecasting framework for 3D cloud fields. The proposed architecture consists of four components: a classical spatiotemporal encoder for compact latent representation, a topology-aware quantum enhancement block for modeling nonlocal couplings in latent space, a dynamic fusion temporal unit for integrating measurement-derived quantum features with recurrent memory, and a decoder for reconstructing future cloud volumes. Experiments on CMA-MESO 3D cloud fields show that QENO consistently outperforms representative baselines, including ConvLSTM, PredRNN++, Earthformer, TAU, and SimVP variants, in terms of MSE, MAE, RMSE, SSIM, and threshold-based detection metrics. In particular, QENO achieves an MSE of 0.2038, an RMSE of 0.4514, and an SSIM of 0.6291, while also maintaining a compact parameter budget. These results indicate that topology-aware hybrid quantum-classical feature modeling is a promising direction for 3D cloud structure forecasting and atmospheric Earth observation data analysis.

cs.LG↗