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Researcher profile

Wen Luo

Wen Luo contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
ApplicationsComputation and Languagehep-phmath.QAphysics.opticsphysics.plasm-phphysics.soc-phquant-ph

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Published work

4 published item(s)

preprint2026arXiv

Discovering the Gell-Mann-Okubo Formula with Kolmogorov-Arnold Networks

Uncovering physical laws from experimental data is a fundamental goal of theoretical physics. In this work, we apply the spline-based, interpretable Kolmogorov-Arnold Network (KAN) to explore the algebraic structure underlying the baryon octet and decuplet mass spectra. Within a symbolic regression framework and without imposing theoretical priors, KAN autonomously recovers the classical Gell-Mann-Okubo mass relations and accurately extracts the associated SU(3) symmetry-breaking parameters. Compared to conventional fitting approaches, this method achieves comparable predictive accuracy while offering substantially improved interpretability and analytic transparency. Our results demonstrate the potential of KAN as a powerful tool for symbolic discovery in hadron physics and for bridging data-driven modeling with fundamental physical laws.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality

Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning steps. Existing approaches to improving factuality, including abstention and factuality-driven optimization, follow a \emph{coupled exploration-commitment} paradigm, in which intermediate reasoning is unconditionally propagated to the final output, limiting fine-grained control over information selection and integration. In this paper, we propose an \textbf{Exploration-Commitment Decoupling} paradigm that disentangles knowledge exploration from final commitment, enabling models to explore with awareness while answering cautiously. We instantiate the paradigm with \textbf{Calibration-Aware Generation (CAG)}, a framework that equips models with end-to-end, calibration-aware generation capabilities, by augmenting intermediate reasoning with calibrated reliability estimates and prioritizing reliable content in final outputs. Across five long-form factuality benchmarks and multiple model families, CAG improves factuality by up to 13%, while reducing decoding time by up to 37%. Overall, our work highlights decoupling as a principled approach for more reliable long-form generation, offering directions for trustworthy and self-aware generative systems.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

High-efficiency water-window x-ray generation from nanowire array targets irradiated with femtosecond laser pulses

We demonstrate the high-efficiency generation of water-window soft x-ray emissions from polyethylene nanowire array targets irradiated by femtosecond laser pulses at the intensity of 4*10^19 W/cm^2. The experimental results indicate more than one order of magnitude enhancement of the water-window x-ray emissions from the nanowire array targets compared to the planar targets. The highest energy conversion efficiency from laser to water-window x-rays is measured as 0.5%/sr, which comes from the targets with the longest nanowires. Supported by particle-in-cell simulations and atomic kinetic codes, the physics that leads to the high conversion efficiency is discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Modeling Spontaneous Exit Choices in Intercity Expressway Traffic with Quantum Walk

In intercity expressway traffic, a driver frequently makes decisions to adjust driving behavior according to time, location and traffic conditions, which further affects when and where the driver will leave away from the expressway traffic. Spontaneous exit choices by drivers are hard to observe and thus it is a challenge to model intercity expressway traffic sufficiently. In this paper, we developed a Spontaneous Quantum Traffic Model (SQTM), which models the stochastic traffic fluctuation caused by spontaneous exit choices and the residual regularity fluctuation with Quantum Walk and Autoregressive Moving Average model (ARMA), respectively. SQTM considers the spontaneous exit choice of a driver as a quantum stochastic process with a dynamical probability function varies according to time, location and traffic conditions. A quantum walk is applied to update the probability function, which simulates when and where a driver will leave the traffic affected by spontaneous exit choices. We validate our model with hourly traffic data from 7 exits from the Nanjing-Changzhou expressway in Eastern China. For the 7 exits, the coefficients of determination of SQTM ranged from 0.5 to 0.85. Compared with classical random walk and ARMA model, the coefficients of determination were increased by 21.28% to 104.98%, and relative mean square error decreased by 11.61% to 32.92%. We conclude that SQTM provides new potential for modeling traffic dynamics with consideration of unobservable spontaneous driver's decision-making.

0 citations0 reviewsNo code linkedOpen access