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Researcher profile

Pan Chen

Pan Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mtrl-sciMachine LearningArtificial IntelligenceComputation and Languageeess.ASeess.IVNeural and Evolutionary Computingphysics.app-phSound

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Published work

7 published item(s)

preprint2026arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Parameter efficient dendritic-tree neurons outperform perceptrons

Biological neurons are more powerful than artificial perceptrons, in part due to complex dendritic input computations. Inspired to empower the perceptron with biologically inspired features, we explore the effect of adding and tuning input branching factors along with input dropout. This allows for parameter efficient non-linear input architectures to be discovered and benchmarked. Furthermore, we present a PyTorch module to replace multi-layer perceptron layers in existing architectures. Our initial experiments on MNIST classification demonstrate the accuracy and generalization improvement of dendritic neurons compared to existing perceptron architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks

Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones. It suffers from the class imbalance issue and the lacking in the abnormal instances. In addition, collecting all kinds of abnormal or unknown samples for training purpose is impractical and timeconsuming. In this paper, a novel Gaussian Mixture Generative Adversarial Network (GMGAN) is proposed under semi-supervised learning framework, in which the underlying structure of training data is not only captured in spectrogram reconstruction space, but also can be further restricted in the space of latent representation in a discriminant manner. Experiments show that our model has clear superiority over previous methods, and achieves the state-of-the-art results on DCASE dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Anomaly Detection by One Class Latent Regularized Networks

Anomaly detection is a fundamental problem in computer vision area with many real-world applications. Given a wide range of images belonging to the normal class, emerging from some distribution, the objective of this task is to construct the model to detect out-of-distribution images belonging to abnormal instances. Semi-supervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, the training process of GAN is still unstable and challenging. To solve these issues, a novel adversarial dual autoencoder network is proposed, in which the underlying structure of training data is not only captured in latent feature space, but also can be further restricted in the space of latent representation in a discriminant manner, leading to a more accurate detector. In addition, the auxiliary autoencoder regarded as a discriminator could obtain an more stable training process. Experiments show that our model achieves the state-of-the-art results on MNIST and CIFAR10 datasets as well as GTSRB stop signs dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Atomic Imaging of Mechanically Induced Topological Transition of Ferroelectric Vortices

Ferroelectric vortices formed through complex lattice-charge interactions have great potential in applications for future nanoelectronics such as memories. For practical applications, it is crucial to manipulate these topological states under external stimuli. Here, we apply mechanical loads to locally manipulate the vortices in a PbTiO3-SrTiO3 superlattice via atomically resolved in situ scanning transmission electron microscopy. The vortices undergo a transition to the a-domain with in-plane polarization under external compressive stress and spontaneously recover after removal of the stress. We reveal the detailed transition process at the atomic scale and reproduce this numerically using phase-field simulations. These findings provide new pathways to control the exotic topological ferroelectric structures for future nanoelectronics and also valuable insights into understanding of lattice-charge interactions at nanoscale.

0 citations0 reviewsNo code linkedOpen access