MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics

Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-consuming tasks. Although LLMs show promise in code generation and domain-specific question answering, their performance in MD scenarios is limited by scarce domain data, the high deployment cost of state-of-the-art LLMs, and low code executability. Building upon our prior MDAgent, we present MDAgent2, the first end-to-end framework capable of performing both knowledge Q&A and code generation within the MD domain. We construct a domain-specific data-construction pipeline that yields three high-quality datasets spanning MD knowledge, question answering, and code generation. Based on these datasets, we adopt a three stage post-training strategy--continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL)--to train two domain-adapted models, MD-Instruct and MD-Code. Furthermore, we introduce MD-GRPO, a closed-loop RL method that leverages simulation outcomes as reward signals and recycles low-reward trajectories for continual refinement. We further build MDAgent2-RUNTIME, a deployable multi-agent system that integrates code generation, execution, evaluation, and self-correction. Together with MD-EvalBench proposed in this work, the first benchmark for LAMMPS code generation and question answering, our models and system achieve performance surpassing several strong baselines.This work systematically demonstrates the adaptability and generalization capability of large language models in industrial simulation tasks, laying a methodological foundation for automatic code generation in AI for Science and industrial-scale simulations. URL: https://github.com/FredericVAN/PKU_MDAgent2

Generating Novel and Realistic Speakers for Voice Conversion

Voice conversion models modify timbre while preserving paralinguistic features, enabling applications like dubbing and identity protection. However, most VC systems require access to target utterances, limiting their use when target data is unavailable or when users desire conversion to entirely novel, unseen voices. To address this, we introduce a lightweight method SpeakerVAE to generate novel speakers for VC. Our approach uses a deep hierarchical variational autoencoder to model the speaker timbre space. By sampling from the trained model, we generate novel speaker representations for voice synthesis in a VC pipeline. The proposed method is a flexible plug-in module compatible with various VC models, without co-training or fine-tuning of the base VC system. We evaluated our approach with state-of-the-art VC models: FACodec and CosyVoice2. The results demonstrate that our method successfully generates novel, unseen speakers with quality comparable to that of the training speakers.

LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction

Exposure correction is essential for enhancing image quality under challenging lighting conditions. While supervised learning has achieved significant progress in this area, it relies heavily on large-scale labeled datasets, which are difficult to obtain in practical scenarios. To address this limitation, we propose a pseudo label-based unsupervised method called LoopExpose for arbitrary-length exposure correction. A nested loop optimization strategy is proposed to address the exposure correction problem, where the correction model and pseudo-supervised information are jointly optimized in a two-level framework. Specifically, the upper-level trains a correction model using pseudo-labels generated through multi-exposure fusion at the lower level. A feedback mechanism is introduced where corrected images are fed back into the fusion process to refine the pseudo-labels, creating a self-reinforcing learning loop. Considering the dominant role of luminance calibration in exposure correction, a Luminance Ranking Loss is introduced to leverage the relative luminance ordering across the input sequence as a self-supervised constraint. Extensive experiments on different benchmark datasets demonstrate that LoopExpose achieves superior exposure correction and fusion performance, outperforming existing state-of-the-art unsupervised methods. Code is available at https://github.com/FALALAS/LoopExpose.

Bridging BCI and Communications: A MIMO Framework for EEG-to-ECoG Wireless Channel Modeling

As a method to connect human brain and external devices, Brain-computer interfaces (BCIs) are receiving extensive research attention. Recently, the integration of communication theory with BCI has emerged as a popular trend, offering potential to enhance system performance and shape next-generation communications. A key challenge in this field is modeling the brain wireless communication channel between intracranial electrocorticography (ECoG) emitting neurons and extracranial electroencephalography (EEG) receiving electrodes. However, the complex physiology of brain challenges the application of traditional channel modeling methods, leaving relevant research in its infancy. To address this gap, we propose a frequency-division multiple-input multiple-output (MIMO) estimation framework leveraging simultaneous macaque EEG and ECoG recordings, while employing neurophysiology-informed regularization to suppress noise interference. This approach reveals profound similarities between neural signal propagation and multi-antenna communication systems. Experimental results show improved estimation accuracy over conventional methods while highlighting a trade-off between frequency resolution and temporal stability determined by signal duration. This work establish a conceptual bridge between neural interfacing and communication theory, accelerating synergistic developments in both fields.

StablePCA: Learning Shared Representations across Multiple Sources via Minimax Optimization

When synthesizing multisource high-dimensional data, a key objective is to extract low-dimensional feature representations that effectively approximate the original features across different sources. Such general feature extraction facilitates the discovery of transferable knowledge, mitigates systematic biases such as batch effects, and promotes fairness. In this paper, we propose Stable Principal Component Analysis (StablePCA), a novel method for group distributionally robust learning of latent representations from high-dimensional multi-source data. A primary challenge in generalizing PCA to the multi-source regime lies in the nonconvexity of the fixed rank constraint, rendering the minimax optimization nonconvex. To address this challenge, we employ the Fantope relaxation, reformulating the problem as a convex minimax optimization, with the objective defined as the maximum loss across sources. To solve the relaxed formulation, we devise an optimistic-gradient Mirror Prox algorithm with explicit closed-form updates. Theoretically, we establish the global convergence of the Mirror Prox algorithm, with the convergence rate provided from the optimization perspective. Furthermore, we offer practical criteria to assess how closely the solution approximates the original nonconvex formulation. Through extensive numerical experiments, we demonstrate StablePCA's high accuracy and efficiency in extracting robust low-dimensional representations across various finite-sample scenarios.

Large Language Models in Bioinformatics: A Survey

Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine.

A Fusion Model for Artwork Identification Based on Convolutional Neural Networks and Transformers

The identification of artwork is crucial in areas like cultural heritage protection, art market analysis, and historical research. With the advancement of deep learning, Convolutional Neural Networks (CNNs) and Transformer models have become key tools for image classification. While CNNs excel in local feature extraction, they struggle with global context, and Transformers are strong in capturing global dependencies but weak in fine-grained local details. To address these challenges, this paper proposes a fusion model combining CNNs and Transformers for artwork identification. The model first extracts local features using CNNs, then captures global context with a Transformer, followed by a feature fusion mechanism to enhance classification accuracy. Experiments on Chinese and oil painting datasets show the fusion model outperforms individual CNN and Transformer models, improving classification accuracy by 9.7% and 7.1%, respectively, and increasing F1 scores by 0.06 and 0.05. The results demonstrate the model's effectiveness and potential for future improvements, such as multimodal integration and architecture optimization.

A Fusion Model for Art Author Identification Based on Convolutional Neural Networks and Transformers

The identification of art authors is crucial in areas like cultural heritage protection, art market analysis, and historical research. With the advancement of deep learning, Convolutional Neural Networks (CNNs) and Transformer models have become key tools for image classification. While CNNs excel in local feature extraction, they struggle with global context, and Transformers are strong in capturing global dependencies but weak in fine-grained local details. To address these challenges, this paper proposes a fusion model combining CNNs and Transformers for art author identification. The model first extracts local features using CNNs, then captures global context with a Transformer, followed by a feature fusion mechanism to enhance classification accuracy. Experiments on Chinese and oil painting datasets show the fusion model outperforms individual CNN and Transformer models, improving classification accuracy by 9.7\% and 7.1\%, respectively, and increasing F1 scores by 0.06 and 0.05. The results demonstrate the model's effectiveness and potential for future improvements, such as multimodal integration and architecture optimization.

A Collaborative Jade Recognition System for Mobile Devices Based on Lightweight and Large Models

With the widespread adoption and development of mobile devices, vision-based recognition applications have become a hot topic in research. Jade, as an important cultural heritage and artistic item, has significant applications in fields such as jewelry identification and cultural relic preservation. However, existing jade recognition systems still face challenges in mobile implementation, such as limited computing resources, real-time requirements, and accuracy issues. To address these challenges, this paper proposes a jade recognition system based on size model collaboration, aiming to achieve efficient and accurate jade identification using mobile devices such as smartphones.First, we design a size model based on multi-scale image processing, extracting key visual information by analyzing jade's dimensions, shapes, and surface textures. Then, a collaborative multi-model classification framework is built by combining deep learning and traditional computer vision algorithms. This framework can effectively select and adjust models based on different jade characteristics, providing high accuracy results across various environments and devices.Experimental results show that the proposed system can provide high recognition accuracy and fast processing time on mobile devices, while consuming relatively low computational resources. The system not only holds great application potential but also provides new ideas and technical support for the intelligent development of jade identification.

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Developing robust and general-purpose robotic manipulation policies is a key goal in the field of robotics. To achieve effective generalization, it is essential to construct comprehensive datasets that encompass a large number of demonstration trajectories and diverse tasks. Unlike vision or language data that can be collected from the Internet, robotic datasets require detailed observations and manipulation actions, necessitating significant investment in hardware-software infrastructure and human labor. While existing works have focused on assembling various individual robot datasets, there remains a lack of a unified data collection standard and insufficient diversity in tasks, scenarios, and robot types. In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot manipulation), featuring 55k real-world demonstration trajectories across 279 diverse tasks involving 61 different object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related information, including multi-view RGB-D images, proprioceptive robot state information, end effector details, and linguistic task descriptions. To ensure dataset consistency and reliability during policy learning, RoboMIND is built on a unified data collection platform and standardized protocol, covering four distinct robotic embodiments. We provide a thorough quantitative and qualitative analysis of RoboMIND across multiple dimensions, offering detailed insights into the diversity of our datasets. In our experiments, we conduct extensive real-world testing with four state-of-the-art imitation learning methods, demonstrating that training with RoboMIND data results in a high manipulation success rate and strong generalization. Our project is at https://x-humanoid-robomind.github.io/.