Jiacheng Miao

Jiacheng Miao, Xinran Miao, Yixuan Wu et al.

A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be costly, labor-intensive, or invasive to obtain. With the rapid development of machine learning (ML), scientists can now employ ML algorithms to predict gold-standard outcomes with variables that are easier to obtain. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data. Its "assumption-lean" property guarantees reliable statistical inference without assumptions on the ML prediction. Its "data-adaptive" feature guarantees an efficiency gain over existing methods, regardless of the accuracy of ML prediction. We demonstrate the statistical superiority and broad applicability of our method through simulations and real-data applications.

Jiacheng Miao, Chao Liu, Qiliang Wang et al.

Precision reducers are critical components in robotic systems, directly affecting the motion accuracy and dynamic performance of humanoid robots, quadruped robots, collaborative robots, industrial robots, and SCARA robots. This paper presents a dynamic toolkit for analyzing the transmission characteristics of precision reducers with explicit contact geometry. A unified framework is proposed to address the challenges in modeling accurate contact behaviors, evaluating gear stiffness, and predicting system vibrations. By integrating advanced contact theories and numerical solving methods, the proposed toolkit offers higher precision and computational efficiency compared to traditional dynamics software. The toolkit is designed with a modular, scriptable architecture that supports rapid reconfiguration across diverse reducer topologies. Numerical validation against published benchmarks confirms the accuracy of the proposed approach.

Jiacheng Miao, Joe R. Davis, Yaohui Zhang et al.

We introduce Paper2Agent, an automated framework that converts research papers into AI agents. Paper2Agent transforms research output from passive artifacts into active systems that can accelerate downstream use, adoption, and discovery. Conventional research papers require readers to invest substantial effort to understand and adapt a paper's code, data, and methods to their own work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by automatically converting a paper into an AI agent that acts as a knowledgeable research assistant. It systematically analyzes the paper and the associated codebase using multiple agents to construct a Model Context Protocol (MCP) server, then iteratively generates and runs tests to refine and robustify the resulting MCP. These paper MCPs can then be flexibly connected to a chat agent (e.g. Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the original paper. We demonstrate Paper2Agent's effectiveness in creating reliable and capable paper agents through in-depth case studies. Paper2Agent created an agent that leverages AlphaGenome to interpret genomic variants and agents based on ScanPy and TISSUE to carry out single-cell and spatial transcriptomics analyses. We validate that these paper agents can reproduce the original paper's results and can correctly carry out novel user queries. Paper2Agent automatically created AI co-scientist that identified new splicing variant associated with ADHD risk. By turning static papers into dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for knowledge dissemination and a foundation for the collaborative ecosystem of AI co-scientists.

Tingxuan Huang, Jiacheng Miao, Shizhuo Deng et al.

Depth completion is a critical task for handling depth images with missing pixels, which can negatively impact further applications. Recent approaches have utilized Convolutional Neural Networks (CNNs) to reconstruct depth images with the assistance of color images. However, vanilla convolution has non-negligible drawbacks in handling missing pixels. To solve this problem, we propose a new model for depth completion based on an encoder-decoder structure. Our model introduces two key components: the Mask-adaptive Gated Convolution (MagaConv) architecture and the Bi-directional Progressive Fusion (BP-Fusion) module. The MagaConv architecture is designed to acquire precise depth features by modulating convolution operations with iteratively updated masks, while the BP-Fusion module progressively integrates depth and color features, utilizing consecutive bi-directional fusion structures in a global perspective. Extensive experiments on popular benchmarks, including NYU-Depth V2, DIML, and SUN RGB-D, demonstrate the superiority of our model over state-of-the-art methods. We achieved remarkable performance in completing depth maps and outperformed existing approaches in terms of accuracy and reliability.