arXiv subjects

Pan Chen

Publications and source records attributed to Pan Chen.

At least 19 recordsLinked to original sources

arXiv2026

Writing and erasing skyrmions by single ultrafast laser pulses in monolayer Janus 2D magnets

Skyrmions in 2D magnets are promising candidates for nonvolatile, low-power, and high-density spintronic memories. However, their experimental realization at the 2D limit remains challenging, owing to the difficulty in engineering the required chiral magnetic interactions. Here, we report the creation and direct imaging of N\'eel-type skyrmions in Janus 2D chromium chalcogenides using synchrotron X-ray photoemission electron microscopy, and scanning nitrogen-vacancy magnetometry, which exhibit field-free stability, nonvolatility, and size tunability. First-principles calculations and micromagnetic simulations reveal that Janus-surface-induced inversion-symmetry breaking enhances the Dzyaloshinskii-Moriya interaction, providing the microscopic mechanism for skyrmion stabilization and tunability. We further achieve reversible skyrmion writing and erasing using a single ultrafast laser pulse in a magnetic field as low as 300 Oe, demonstrating the excellent manipulability of this 2D magnetic system. These results establish Janus engineering as a route to creating and manipulating nonvolatile skyrmions in atomically thin magnets, with implications for skyrmion-based low-power spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Multiplicity and Nonrelativistic limit of Bound States of Nonlinear Dirac Equations on Noncompact Metric Graphs with Localized Nonlinearities

In this paper, we investigate the multiplicity of normalized solutions to a nonlinear Dirac equation with localized nonlinearities on noncompact metric graphs under the $L^2$-constraint, as well as the asymptotic behavior of these solutions in the nonrelativistic limit. First, we establish the existence of multiple normalized bound states. Moreover, we explore the nonrelativistic limit and show that, as the speed of light tends to infinity, the solutions converge to those of a nonlinear Schr\"odinger equation. Our results including the mass-subcritical, mass-critical and, in particular, mass-supercritical regimes.

math.AP↗

arXiv2026

Energy quantization for Dirac systems over non-collapsed degenerating Einstein manifolds

We study energy quantization for a class of Dirac systems on compact spin Einstein manifolds of dimension $n$. For a sequence of solutions to a nonlinear Dirac system with uniformly bounded energy on a fixed spin Riemannian manifold, we first establish an energy identity theorem. We then investigate the more complicated case of underlying domain manifolds being a sequence of non-collapsed degenerating spin Einstein manifolds. At an orbifold singular point, three types of bubble spinors can possibly appear, living respectively on $\mathbb{R}^n$, on a Ricci-flat ALE bubble space, and on the flat cone $\mathbb{R}^n/\Gamma$. By developing asymptotic analysis for solutions over degenerating neck regions, we establish that energy identity holds.

math.AP↗

arXiv2026

Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function

Hyperspectral imaging provides rich spectral information for quantitative remote sensing, yet hyperspectral sensors remain costly and thus unavailable in many UAV deployments. Spectral super-resolution (SSR) seeks to reconstruct hyperspectral images (HSIs) from multispectral images (MSIs). Most existing SSR methods assume a fixed and known spectral response function (SRF) and are therefore limited to single-sensor settings. In practical cross-sensor scenarios, the spectral degradation from HSI to MSI is unknown and varies with sensor characteristics and scene content, which renders HSI reconstruction ill-posed. This paper proposes a physics-guided deep unfolding network, termed PGU-Net, to address blind cross-sensor SSR by jointly estimating the HSI and a learnable spectral transformation function (STF). PGU-Net unrolls an alternating optimization procedure into an end-to-end trainable architecture with stages, where each stage sequentially updates the HSI and the STF. Both modules combine learnable proximal networks with differentiable closed-form solvers, enabling physical interpretability while retaining strong representation capacity. Experiments on benchmark datasets (CAVE and NTIRE 2022) with multiple SRFs demonstrate accurate recovery of the STF (degradation operator) and improved reconstruction performance over state-of-the-art SSR methods. Furthermore, evaluations on a real UAV cross-sensor dataset (Headwall Nano HSI and DJI P4 Multispectral MSI) verify the effectiveness and robustness of PGU-Net under truly blind conditions, and suggest that the estimated STF may exhibit land-cover-related differences.

cs.CV↗

arXiv2026

Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation

Modeling of long history data suffers from long-context window attention dilution, system efficiency and catastrophic forgetting problems, where naive linear scaling approach like LastN would fail. We introduce Memento, a personalized retrieval-augmented framework that treats historical user engagements as a document corpus and ad requests as queries, retrieving relevant interactions via Maximal Marginal Relevance (MMR) to balance similarity with diversity. We identify two complementary applications: Representation Memento, which retrieves historical embeddings for feature augmentation, and Data Memento, which retrieves past training examples for multipass training. Through infrastructure co-design -- temporal chunking, INT8 quantization, and asynchronous serving -- Memento achieves 5-10$\times$ resource efficiency over linear scaling. Memento processes daily requests with sub-10ms latency, yielding 0.25-0.3% Normalized Entropy gain on both click-through and conversion prediction. In production, Memento delivers a 1% CTR lift on Facebook Feed and Reels and a 1.2% CVR lift, scaling personalization to 365+ days of history.

cs.IR↗

arXiv2026

Code as Agent Harness

Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineering. In emerging agentic systems, code is no longer only a target output. It increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification. We frame this shift through the lens of agent harnesses and introduce code as agent harness: a unified view that centers code as the basis for agent infrastructure. To systematically study this perspective, we organize the survey around three connected layers. First, we study the harness interface, where code connects agents to reasoning, action, and environment modeling. Second, we examine harness mechanisms: planning, memory, and tool use for long-horizon execution, together with feedback-driven control and optimization that make harness reliable and adaptive. Third, we discuss scaling the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification. Across these layers, we summarize representative methods and practical applications of code as agent harness, spanning coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. We further outline open challenges for harness engineering, including evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments. By centering code as the harness of agentic AI, this survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.

cs.CL↗

arXiv2026

TreeWriter: AI-Assisted Hierarchical Planning and Writing for Long-Form Documents

Long documents pose many challenges to current intelligent writing systems. These include maintaining consistency across sections, sustaining efficient planning and writing as documents become more complex, and effectively providing and integrating AI assistance to the user. Existing AI co-writing tools offer either inline suggestions or limited structured planning, but rarely support the entire writing process that begins with high-level ideas and ends with polished prose, in which many layers of planning and outlining are needed. Here, we introduce TreeWriter, a hierarchical writing system that represents documents as trees and integrates contextual AI support. TreeWriter allows authors to create, save, and refine document outlines at multiple levels, facilitating drafting, understanding, and iterative editing of long documents. A built-in AI agent can dynamically load relevant content, navigate the document hierarchy, and provide context-aware editing suggestions. A within-subject study (N=12) comparing TreeWriter with Google Docs + Gemini on long-document editing and creative writing tasks shows that TreeWriter improves idea exploration/development, AI helpfulness, and perceived authorial control. A two-month field deployment (N=8) further demonstrated that hierarchical organization supports collaborative writing. Our findings highlight the potential of hierarchical, tree-structured editors with integrated AI support and provide design guidelines for future AI-assisted writing tools that balance automation with user agency.

cs.HC↗

arXiv2026

Contact resistance and interfacial engineering: Advances in high-performance 2D-TMD based devices

The development of advanced electronic devices is contingent upon sustainable material development and pioneering research breakthroughs. Traditional semiconductor-based electronic technology faces constraints in material thickness scaling and energy efficiency. Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as promising candidates for next-generation nanoelectronics and optoelectronic applications, boasting high electron mobility, mechanical strength, and a customizable band gap. Despite these merits, the Fermi level pinning effect introduces uncontrollable Schottky barriers at metal-2D-TMD contacts, challenging prediction through the Schottky-Mott rule. These barriers fundamentally lead to elevated contact resistance and limited current-delivery capability, impeding the enhancement of 2D-TMD transistor and integrated circuit properties. In this review, we succinctly outline the Fermi pinning effect mechanism and peculiar contact resistance behavior at metal/2D-TMD interfaces. Subsequently, highlights on the recent advances in overcoming contact resistance in 2D-TMDs devices, encompassing interface interaction and hybridization, van der Waals (vdW) contacts, prefabricated metal transfer and charge-transfer doping will be addressed. Finally, the discussion extends to challenges and offers insights into future developmental prospects.

cond-mat.mtrl-sci↗

arXiv2025

Interplay of spin-orbit coupling and trigonal crystal field enhances superconductivity in $LaAlO_3/KTaO_3$ (111)

In conventional superconductors, bulk physical properties typically degrade as the film thickness approaches the two-dimensional (2D) limit. Here in the (111) oriented LaAlO3/KTaO3 (LAO/KTO) heterostructure, we demonstrate experimental evidence that reducing the conducting layer thickness at the interface significantly enhances superconducting transition temperature Tc, in direct contrast to conventional wisdom. From the sum frequency generation (SFG) spectroscopy and superconducting upper-critical field measurements, both the trigonal symmetry and spin orbit scattering are enhanced with the increased Tc. We attribute the enhanced superconductivity (SC) to the synergic interplay between spin-orbit coupling (SOC) and trigonal crystal field, resulting in an enhanced electron-phonon coupling. Furthermore, we show the existence of unconventional SC: the approaching linear temperature dependence of normal state resistance with increasing Tc and the existence of a quantum critical point (QCP) near the superconducting phase. Our findings provide important insight into the underlying mechanism of the strong orientation-dependent KTO interface SC.

cond-mat.supr-con↗

arXiv2025

Schema for In-Context Learning

In-Context Learning (ICL) enables transformer-based language models to adapt to new tasks by conditioning on demonstration examples. However, traditional example-driven in-context learning lacks explicit modules for knowledge retrieval and transfer at the abstraction level. Inspired by cognitive science, specifically schema theory, which holds that humans interpret new information by activating pre-existing mental frameworks (schemas) to structure understanding, we introduce Schema-Activated In-Context Learning (SA-ICL). This framework extracts the representation of the building blocks of cognition for the reasoning process instilled from prior examples, creating an abstracted schema, a lightweight, structured template of key inferential steps and their relationships, which is then used to augment a model's reasoning process when presented with a novel question. We demonstrate that a broad range of large language models (LLMs) lack the capacity to form and utilize internal schema-based learning representations implicitly, but instead benefit significantly from explicit schema-based scaffolding. Across chemistry and physics questions from the GPQA dataset, our experiments show that SA-ICL consistently boosts performance, up to 36.19 percent, when the single demonstration example is of high quality, which simultaneously reduces reliance on the number of demonstrations and enhances interpretability. Schema-Activated In-Context Learning not only bridges disparate ICL strategies ranging from pattern priming to Chain-of-Thought prompting, but also paves a new path for enhancing human-like reasoning in LLMs.

cs.CL↗

arXiv2025

MEMS Vapor Cells-based Rydberg-atom Electrometry Toward Miniaturization and High Sensitivity

Rydberg-atom electrometry, as an emerging cutting-edge technology, features high sensitivity, broad bandwidth, calibration-free operation, and beyond. However, until now the key atomic vapor cells used for confining electric field-sensitive Rydberg atoms nearly made with traditional glass-blown techniques, hindering the miniaturization, integration, and batch manufacturing. Here, we present the wafer-level MEMS atomic vapor cells with glass-silicon-glass sandwiched structure that are batch-manufactured for both frequency stability and electric field measurement. We use specially customized ultra-thick silicon wafers with a resistivity exceeding 10,000 cm, three orders of magnitude higher than that of typical silicon, and a thickness of 6 mm, providing a 4-fold improvement in optical interrogation length. With the as-developed MEMS atomic vapor cell, we configured a high-sensitivity Rydberg-atom electrometry with the minimal detectable microwave field to be 2.8 mV/cm. This combination of miniaturization and sensitivity represents a significant advance in the state-of-the-art field of Rydberg-atom electrometry, paving the way for chip-scale Rydberg-atom electrometry and potentially opening up new applications in a wider variety of fields.

physics.atom-ph↗

arXiv2025

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models

Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative study on academic reading practices, we introduce TreeReader, a novel language model-augmented paper reader. TreeReader decomposes papers into an interactive tree structure where each section is initially represented by an LLM-generated concise summary, with underlying details accessible on demand. This design allows users to quickly grasp core ideas, selectively explore sections of interest, and verify summaries against the source text. A user study was conducted to evaluate TreeReader's impact on reading efficiency and comprehension. TreeReader provides a more focused and efficient way to navigate and understand complex academic literature by bridging hierarchical summarization with interactive exploration.

cs.HC↗

arXiv2025

Existence and nonrelativistic limit of ground states to nonlinear Dirac equation

This paper explores the existence and properties of ground states, including both energy and action ground states, for nonlinear Dirac equations with power-type potentials. \begin{equation*} -i c\sum\limits_{k=1}^3\alpha_k\partial_k u +mc^2 \beta {u}- |{u}|^{p-2}{u}=\omega {u}. \end{equation*} We establish the existence of energy ground states and demonstrate that as the speed of light approaches infinity, both energy and action ground states converge to their counterparts in the nonlinear Schr\"odinger equation. Furthermore, we characterize the convergence rate of the ground state energy and investigate the equivalence between action and energy ground states.

math.AP↗

arXiv2025

Ferromagnetic Two-dimensional Electron Gases with Magnetic Doping and Proximity Effects

The advent of magnetic two-dimensional electron gases (2DEGs) at oxide interfaces has provided new opportunities in the field of spintronics. The enhancement of magnetism in 2DEGs at oxide interfaces continues to be a significant challenge, as exemplified by the relatively weak magnetism observed in the classical LaAlO3/SrTiO3 interface. Here, we present ferromagnetic (FM) 2DEGs at the interface fabricated between the FM insulator EuTiO3 (ETO) and the strong spin-orbit coupled (SOC) perovskite insulator KTaO3 (KTO). With the combined effects of magnetic atom doping and magnetic proximity from ETO films, the coercive field of 2DEGs can be significantly enhanced. Magnetoresistance (MR) curve with a high coercive field of 1000 Oe has been observed, in conjunction with a temperature-dependent unambiguous hysteresis loop in the anomalous Hall effect (AHE). Furthermore, within the 2DEGs, we have identified a synergetic interplay between magnetic scattering and the weak antilocalization (WAL) effect on transport. This study provides fresh insights into the formation of FM 2DEGs at ETO/KTO interfaces, and introduce an innovative pathway for creating high-performance magnetic 2DEGs at oxide interfaces.

cond-mat.mtrl-sci↗

arXiv2025

Asymptotic properties of non-relativistic limit for pseudo-relativistic Hartree equations

In this paper, we study the asymptotic behavior of energy and action ground states to the following pseudo-relativistic Hartree equation \[ \left(\sqrt{-c^2\Delta +m^2c^4}-mc^2\right)u + \lambda u = \left(|x|^{-1}*|u|^2\right)u \] as the speed of light $c\to\infty$. We obtain an asymptotic expansion of the ground state as $c \to \infty,$ which is new in the case of the energy ground state and generalizes the results of Choi, Hong, and Seok (2018) for the action ground state.

math.AP↗

arXiv2024

Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the short-term state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods.

cs.LG↗

arXiv2023

P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion

Inferring missing regions from severely occluded point clouds is highly challenging. Especially for 3D shapes with rich geometry and structure details, inherent ambiguities of the unknown parts are existing. Existing approaches either learn a one-to-one mapping in a supervised manner or train a generative model to synthesize the missing points for the completion of 3D point cloud shapes. These methods, however, lack the controllability for the completion process and the results are either deterministic or exhibiting uncontrolled diversity. Inspired by the prompt-driven data generation and editing, we propose a novel prompt-guided point cloud completion framework, coined P2M2-Net, to enable more controllable and more diverse shape completion. Given an input partial point cloud and a text prompt describing the part-aware information such as semantics and structure of the missing region, our Transformer-based completion network can efficiently fuse the multimodal features and generate diverse results following the prompt guidance. We train the P2M2-Net on a new large-scale PartNet-Prompt dataset and conduct extensive experiments on two challenging shape completion benchmarks. Quantitative and qualitative results show the efficacy of incorporating prompts for more controllable part-aware point cloud completion and generation. Code and data are available at https://github.com/JLU-ICL/P2M2-Net.

cs.CV↗

arXiv2023

Opportunities for Adaptive Experiments to Enable Continuous Improvement in Computer Science Education

Randomized A/B comparisons of alternative pedagogical strategies or other course improvements could provide useful empirical evidence for instructor decision-making. However, traditional experiments do not provide a straightforward pathway to rapidly utilize data, increasing the chances that students in an experiment experience the best conditions. Drawing inspiration from the use of machine learning and experimentation in product development at leading technology companies, we explore how adaptive experimentation might aid continuous course improvement. In adaptive experiments, data is analyzed and utilized as different conditions are deployed to students. This can be achieved using machine learning algorithms to identify which actions are more beneficial in improving students' learning experiences and outcomes. These algorithms can then dynamically deploy the most effective conditions in subsequent interactions with students, resulting in better support for students' needs. We illustrate this approach with a case study that provides a side-by-side comparison of traditional and adaptive experiments on adding self-explanation prompts in online homework problems in a CS1 course. This work paves the way for exploring the importance of adaptive experiments in bridging research and practice to achieve continuous improvement in educational settings.

cs.HC↗