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Minghao Li

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10 papers

2 author rows

IS Journal 2026 Journal Article

Transforming Future Data Center Operations and Management via Physical AI

Zhiwei Cao
Minghao Li
Feng Ling
Jimin Jia
Yonggang Wen
Jianxiong Yin
Simon See

Data centers (DCs) are critical for artificial intelligence (AI) and the digital economy, with AIDCs introducing new operational challenges. This research proposes a novel Physical AI (PhyAI) framework to advance DC operations and management. The system features three core modules: an industry-grade in-house DC simulation engine for high-fidelity AIDC modeling, an AI engine built on NVIDIA PhysicsNeMo for training and evaluating physics-informed machine learning (PIML) models, and a digital twin platform based on NVIDIA Omniverse. This framework enables the creation of real-time digital twins to digitalize, optimize, and automate future DC operations. A case study demonstrated its effectiveness in predicting thermal and airflow profiles for a large-scale DC in real-time, achieving a median absolute temperature prediction error of 0. 18 °C, outperforming traditional CFD/HT simulations. This emerging approach would open doors to several potential research directions for advancing PhyAI in future DC operations.

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NeurIPS Conference 2025 Conference Paper

PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models

Shi Qiu
Shaoyang Guo
Zhuo-Yang Song
Yunbo Sun
Zeyu Cai
Jiashen Wei
Tianyu Luo
Yixuan Yin

Current benchmarks for evaluating the reasoning capabilities of Large Language Models (LLMs) face significant limitations: task oversimplification, data contamination, and flawed evaluation items. These deficiencies necessitate more rigorous assessment methods. To address these limitations, we introduce PHYBench, a benchmark of 500 original physics problems ranging from high school to Physics Olympiad difficulty. PHYBench addresses data contamination through original content and employs a systematic curation pipeline to eliminate flawed items. Evaluations show that PHYBench activates more tokens and provides stronger differentiation between reasoning models compared to other baselines like AIME 2024, OlympiadBench and GPQA. Even the best-performing model, Gemini 2. 5 Pro, achieves only 36. 9\% accuracy compared to human experts' 61. 9\%. To further enhance evaluation precision, we introduce the Expression Edit Distance (EED) Score for mathematical expression assessment, which improves sample efficiency by 204\% over binary scoring. Moreover, PHYBench effectively elicits multi-step and multi-condition reasoning, providing a platform for examining models' reasoning robustness, preferences, and deficiencies. The benchmark results and dataset are publicly available at https: //www. phybench. cn/.

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IJCAI Conference 2025 Conference Paper

Preventing Latent Diffusion Model-Based Image Mimicry via Angle Shifting and Ensemble Learning

Minghao Li
Rui Wang
Ming Sun
Lihua Jing

The remarkable progress of Latent Diffusion Models (LDMs) in image generation has raised concerns about the potential for unauthorized image mimicry. To address these concerns, studies on adversarial attacks against LDMs have gained increasing attention in recent years. However, existing methods face bottlenecks when attacking the denoising module. In this work, we reveal that the robustness of the denoising module stems from two key factors: the cancellation effect between adversarial perturbations and estimated noise, and unstable gradients caused by randomly sampled timesteps and Gaussian noise. Based on these insights, we introduce a cosine similarity adversarial loss to prevent the generation of perturbations that are easily impaired and develop a more stable optimization strategy by ensembling gradients and fixing the noise in the latent space. Additionally, we propose an alternating iterative framework to reduce memory usage by mathematically dividing the optimization process into two spaces: latent space and pixel space. Compared to previous strategies, our proposed framework reduces video memory demands without sacrificing attack effectiveness. Extensive experiments demonstrate that the alternating iterative framework and the stable optimization strategy on cosine similarity loss are more efficient and more effective. Code is available at https: //github. com/MinghaoLi01/cosattack.

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NeurIPS Conference 2024 Conference Paper

Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity

Dayou Yu
Minghao Li
Weishi Shi
Qi Yu

Multi-label active learning is a crucial yet challenging area in contemporary machine learning, often complicated by a large and sparse label space. This challenge is further exacerbated in active learning scenarios where labeling resources are constrained. Drawing inspiration from existing mixture of Bernoulli models, which efficiently compress the label space into a more manageable weight coefficient space by learning correlated Bernoulli components, we propose a novel model called Evidential Mixture Machines (EMM). Our model leverages mixture components derived from unsupervised learning in the label space and improves prediction accuracy by predicting weight coefficients following the evidential learning paradigm. These coefficients are aggregated as proxy pseudo counts to enhance component offset predictions. The evidential learning approach provides an uncertainty-aware connection between input features and the predicted coefficients and components. Additionally, our method combines evidential uncertainty with predicted label embedding covariances for active sample selection, creating a richer, multi-source uncertainty metric beyond traditional uncertainty scores. Experiments on synthetic datasets show the effectiveness of evidential uncertainty prediction and EMM's capability to capture label correlations through predicted components. Further testing on real-world datasets demonstrates improved performance compared to existing multi-label active learning methods.

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AAAI Conference 2024 Conference Paper

Preference Ranking Optimization for Human Alignment

Feifan Song
Bowen Yu
Minghao Li
Haiyang Yu
Fei Huang
Yongbin Li
Houfeng Wang

Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secure AI systems. Reinforcement learning from human feedback (RLHF) has been employed to achieve this alignment. However, it encompasses two main drawbacks: (1) RLHF exhibits complexity, instability, and sensitivity to hyperparameters in contrast to SFT. (2) Despite massive trial-and-error, multiple sampling is reduced to pair-wise contrast, thus lacking contrasts from a macro perspective. In this paper, we propose Preference Ranking Optimization (PRO) as an efficient SFT algorithm to directly fine-tune LLMs for human alignment. PRO extends the pair-wise contrast to accommodate preference rankings of any length. By iteratively contrasting candidates, PRO instructs the LLM to prioritize the best response while progressively ranking the rest responses. In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses. Experiments have shown that PRO outperforms baseline algorithms, achieving comparable results to ChatGPT and human responses through automatic-based, reward-based, GPT-4, and human evaluations.

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ICLR Conference 2024 Conference Paper

Protein-ligand binding representation learning from fine-grained interactions

Shikun Feng
Minghao Li
Yinjun Jia
Wei-Ying Ma
Yanyan Lan

The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional computationally intensive methods, but resulting in poor generalization due to limited supervised data. In this paper, we propose to learn protein-ligand binding representation in a self-supervised learning manner. Different from existing pre-training approaches which treat proteins and ligands individually, we emphasize to discern the intricate binding patterns from fine-grained interactions. Specifically, this self-supervised learning problem is formulated as a prediction of the conclusive binding complex structure given a pocket and ligand with a Transformer based interaction module, which naturally emulates the binding process. To ensure the representation of rich binding information, we introduce two pre-training tasks, i.e. atomic pairwise distance map prediction and mask ligand reconstruction, which comprehensively model the fine-grained interactions from both structure and feature space. Extensive experiments have demonstrated the superiority of our method across various binding tasks, including protein-ligand affinity prediction, virtual screening and protein-ligand docking.

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ICML Conference 2024 Conference Paper

UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning

Shikun Feng
Yuyan Ni
Minghao Li
Yanwen Huang
Zhiming Ma
Wei-Ying Ma
Yanyan Lan

Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal model capable of effectively applying to various categories of molecular tasks, since existing prevalent pre-training methods exhibit effectiveness for specific types of downstream tasks. Furthermore, the lack of profound understanding of existing pre-training methods, including 2D graph masking, 2D-3D contrastive learning, and 3D denoising, hampers the advancement of molecular foundation models. In this work, we provide a unified comprehension of existing pre-training methods through the lens of contrastive learning. Thus their distinctions lie in clustering different views of molecules, which is shown beneficial to specific downstream tasks. To achieve a complete and general-purpose molecular representation, we propose a novel pre-training framework, named UniCorn, that inherits the merits of the three methods, depicting molecular views in three different levels. SOTA performance across quantum, physicochemical, and biological tasks, along with comprehensive ablation study, validate the universality and effectiveness of UniCorn.

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AAMAS Conference 2023 Conference Paper

CraftEnv: A Flexible Collective Robotic Construction Environment for Multi-Agent Reinforcement Learning

Rui Zhao
Xu Liu
Yizheng Zhang
Minghao Li
Cheng Zhou
Shuai Li
Lei Han

CraftEnv is a flexible Collective Robotic Construction (CRC) environment for Multi-Agent Reinforcement Learning (MARL) research. CraftEnv can be used to study how artificial intelligent agents may learn to cooperate and solve complex real world tasks, such as collective construction and intelligent warehousing. The environment contains a set of collective construction tasks, which require a group of robotic vehicles to cooperate and learn to build different constructions efficiently. There are different elements in the CraftEnv, such as smartcars, blocks, and slopes. The smartcars can use the blocks and slopes to build different structures. The CraftEnv is highly flexible and simple to use, which enables creative and quick task-designs. The environment is written in python and can be rendered using PyBullet. The simulation is built based on real world robotic systems, designed with real-world constraints in mind. The learned policy can be transferred to the real world robotic system. CraftEnv is tailored for effective use by the research community and pushing forward collective intelligence and swarm technology.

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AAAI Conference 2023 Conference Paper

TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models

Minghao Li
Tengchao Lv
Jingye Chen
Lei Cui
Yijuan Lu
Dinei Florencio
Cha Zhang
Zhoujun Li

Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks. The TrOCR models and code are publicly available at https://aka.ms/trocr.

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ICRA Conference 2022 Conference Paper

Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach

Minghao Li
Yingrui Jie
Yang Kong
Hui Cheng

The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications. This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning (RL) approach to develop a decentralized policy, which is shared among multiple robots. Given range sensor measurements and the positions of other robots, the policy aims to generate control commands for navigation and preserve the global connectivity of the robot team. We incorporate connectivity concerns into the RL framework as constraints and introduce behavior cloning to reduce the exploration complexity of policy optimization. The policy is optimized with all transition data collected by multiple robots in random simulated scenarios. We validate the effectiveness of the proposed approach by comparing different combinations of connectivity constraints and behavior cloning. We also show that our policy can generalize to unseen scenarios in both simulation and holonomic robots experiments.

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