arXiv subjects

Jiawei Dong

Publications and source records attributed to Jiawei Dong.

7 recordsLinked to original sources

arXiv2026

SOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection

Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, but suffer from hallucination issues where they generate unfaithful or inconsistent content. While Reinforcement Learning with Verifiable Rewards (RLVR) has shown promising applications in reasoning tasks, its application to span-level hallucination detection is challenged by localization ambiguity, reward sparsity on hard examples, and uncontrolled length inflation. To address these issues, we introduce Socratic Guided Policy Optimization (SOPO), a multi-turn RL framework for robust hallucination detection. The key innovation of SOPO is a Socratic feedback mechanism, where a teacher model guides the reasoning trajectories during rollout by providing indirect hints inferred from the ground truth. This teacher-guided approach enables the model to generate more effective reasoning trajectories for hard examples, thus alleviating reward sparsity. Furthermore, we incorporate Turn-based Advantage Scaling to penalize verbosity bias, encouraging the model to develop efficient and autonomous reasoning capabilities. Empirical results on RAGTruth demonstrate that SOPO (4B) achieves state-of-the-art performance, surpassing leading proprietary models (e.g., GPT-5) and standard RL baselines, while obtaining higher reported mean scores than GRPO on four out-of-domain benchmarks, with mixed evidence against CAPO. We release our training and evaluation code alongside the 1.7B and 4B SOPO models at https://github.com/patsnap/SOPO.

cs.CL↗

arXiv2026

LDPGraph: Locally Differentially Private Graph Synthesis by Exploiting Neighborhood Structure

The widespread application of graph data inevitably brings significant privacy risks, as its unprotected use can lead to the leakage of sensitive information. These risks are particularly acute in the setting with an untrusted curator, where the data remains decentralized and each user only holds the connections to their neighbors. To mitigate such privacy risks, we adopt local differential privacy (LDP) to collect users' private information and generate a synthetic graph. However, existing methods suffer from either excessive noise injection by perturbing the local adjacency lists or significant structural information loss due to the simplistic graph encoding process. To address these issues, we propose LDPGraph, an effective graph synthesis algorithm that takes one step further by exploiting neighborhood structures under LDP. To obtain neighborhood statistics beyond degrees, LDPGraph aggregates a noisy global view from perturbed adjacency lists and combines it with projected local connections to estimate node-level triangle counts. To correct the structural inconsistency caused by separately perturbing degree and triangle count, LDPGraph jointly refines them into feasible degree-triangle targets. To reconstruct a global graph from these estimated targets, LDPGraph adopts a triangle-first strategy that first preserves local clustering structures and then fulfills remaining degree requirements. Extensive experiments on four real-world datasets and multiple commonly used graph metrics validate the superiority of LDPGraph. Source code is available at https://github.com/ZJU-TrustAID/LDPGraph.

cs.DB↗

arXiv2026

Dzyaloshinskii-Moriya Gradients Unlock Topological Dimensional Reduction in Magnetic Hopfions

Three-dimensional magnetic solitons retain topological protection only while their spin field remains continuous. Here we show that chemical inhomogeneity can break this protection in a controlled way, converting a hopfion-like toroidal texture into an effectively two-dimensional skyrmion string. Tilt-dependent Lorentz transmission electron microscopy, electron energy-loss spectroscopy, and micromagnetic simulations of pristine, uniformly Y-doped, and nonuniformly Y-doped disordered TiO2 nanoparticles embedded in a FeCrNiMn host reveal two regimes. Uniform Y doping enriches Ti3+/oxygen-vacancy localization centers and stabilizes closed rings with Hopf invariant QH approximately 1. Nonuniform Y doping forms a Ti4+-rich, vacancy-depleted boundary that creates a sharp q=D/(2A) gradient and a weak-moment leakage channel. This coupled mismatch torque and continuity leakage split the ring, leaving a skyrmion-string remnant and a field-sensitive helicity texture.

cond-mat.other↗

arXiv2025

MCGA: Mixture of Codebooks Hyperspectral Reconstruction via Grayscale-Aware Attention

Reconstructing hyperspectral images (HSIs) from RGB inputs provides a cost-effective alternative to hyperspectral cameras, but reconstructing high-dimensional spectra from three channels is inherently ill-posed. Existing methods typically directly regress RGB-to-HSI mappings using large attention networks, which are computationally expensive and handle ill-posedness only implicitly. We propose MCGA, a Mixture-of-Codebooks with Grayscale-aware Attention framework that explicitly addresses these challenges using spectral priors and photometric consistency. MCGA first learns transferable spectral priors via a mixture-of-codebooks (MoC) from heterogeneous HSI datasets, then aligns RGB features with these priors through grayscale-aware photometric attention (GANet). Efficiency and robustness are further improved via top-K attention design and test-time adaptation (TTA). Experiments on multiple real-world benchmarks demonstrate the state-of-the-art accuracy, strong cross-dataset generalization, and 4-5x faster inference. Codes will be available once acceptance at https://github.com/Fibonaccirabbit/MCGA.

cs.CV↗

arXiv2025

Direct imaging of quantum interference and Non-Abelian entanglement in Hopfion: an magnetic soliton possess loop-like anyonic properties

This work provides the first experimental elucidation of quantum topological effects in individual hopfions, establishing their potential as building blocks for three-dimensional topological quantum spintronics. The observed Non-Abelian characteristics suggest pathways toward fault-tolerant quantum operations through controlled hopfion braiding in engineered magnetic metamaterials.

cond-mat.mes-hall↗

arXiv2025

Physics-Informed Machine Learning for EDFA: Parameter Identification and Gain Estimation

As the key component that facilitates long-haul transmission in optical fiber communications by increasing capacity and reducing costs, accurate characterization and gain settings of erbium-doped fiber amplifiers (EDFAs) are essential for quality of transmission estimation and system configuration optimization. However, it is difficult to construct accurate and reliable EDFA models due to complex physical mechanisms and dynamic loading conditions. Although some mathematical and data-driven models have been proposed, their practical applications will face limitations of intricate parameter measurements and high data requirements, respectively. To overcome limitations of both methods, a physics-informed machine learning (PIML) method for parameter identification and gain estimation of EDFA is proposed, which greatly reduces the data requirements by embedding physical prior knowledge in the neural network. In this approach, the gain of EDFA can be accurately estimated by a physics-informed neural network (PINN)-based forward model when parameters including absorption, gain, saturation, and background loss are known. For practical scenarios where parameters are unknown, PINN-based inverse models are established first to identify actual values of parameters from only several sets of input-output data pairs, and PINN-based forward models are accordingly established for gain estimation with identified values. Moreover, an experimental system is constructed to verify the feasibility and performance of proposed method in practical scenarios. Results show that PIML-based method can effectively identify physical parameters from measured data, and better gain estimation results are achieved with mean absolute error of 0.127 dB and standard deviation of 0.065 dB using identified values than typical values of parameters.

physics.optics↗

arXiv2021

High-performance green and blue quantum-dot light-emitting diodes with eliminated charge leakage

Quantum-dot light-emitting diodes (QD-LEDs) promise a new generation of efficient, low-cost, large-area, and flexible electroluminescent devices. However, the inferior performance of green and blue QD-LEDs is hindering the commercialization of QD-LEDs in display and solid-state lighting. Here, we demonstrate best-performing green and blue QD-LEDs with ~100% conversion of the injected charge carriers into emissive excitons. Key to this success is eliminating electron leakage at the organic/inorganic interface by using hole-transport polymers with low electron affinity and reduced energetic disorder. Our devices exhibit record-high peak external quantum efficiencies (28.7% for green, 21.9% for blue), exceptionally high efficiencies in wide ranges of luminance, and unprecedented stability (T95 lifetime: 580,000 h for green, 4,400 h for blue). The overall performance surpasses previously reported solution-processed green and blue LEDs.

physics.optics↗