Li Mao

Yuanrui Wang, Xingxuan Zhang, Han Yu et al.

Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar tokenization, each feature injects value variation through an essentially one-dimensional channel, and feature IDs/positional signals cannot increase within-feature value degrees of freedom, yielding weak early-layer value sensitivity and redundant hidden states. We present a unified \emph{tokenize-and-route} framework for strong TFMs: \textbf{RaBEL} expands each scalar into compact localized RBF features (optionally exponent-gated) to improve conditioning and shallow-layer effective rank, while a reordered bidirectional block \textbf{S$\rightarrow$N$\rightarrow$F} aligns computation with the readout by aggregating cross-sample context before feature mixing and using attention pooling. Together, these changes yield \textbf{LimiX-2M}, a 2M-parameter model that outperforms larger TabPFN-v2 and TabICL baselines on widely used tabular benchmarks while reducing training and inference costs. These results highlight value-aware tokenization and readout-aligned routing as key levers for improving the accuracy--efficiency trade-off in TFMs. Model checkpoints and inference code are available at https://github.com/limix-ldm-ai/LimiX.

Xingxuan Zhang, Gang Ren, Han Yu et al.

We argue that progress toward general intelligence requires complementary foundation models grounded in language, the physical world, and structured data. This report presents LimiX-16M and LimiX-2M, two instantiations of our large structured-data models (LDMs). Both models treat structured data as a joint distribution over variables and missingness, thus capable of addressing a wide range of tabular tasks through query-based conditional prediction via a single model. They are pretrained using masked joint-distribution modeling with an episodic, context-conditional objective, supporting rapid, training-free adaptation at inference. We evaluate LimiX models across 11 large structured-data benchmarks with broad regimes of sample size, feature dimensionality, class number, categorical-to-numerical feature ratio, missingness, and sample-to-feature ratios. LimiX-16M consistently surpasses strong baselines, as shown in Figure 1 and Figure 2. The superiority holds across a wide range of tasks, such as classification, regression, missing value imputation, and data generation, often by substantial margins, while avoiding task-specific architectures or bespoke training per task. Notably, LimiX-2M delivers strong results under tight compute and memory budgets. We also present the first scaling law study for LDMs, revealing how data and model scaling jointly influence downstream performance and offering quantitative guidance for tabular foundation modeling. All LimiX models are publicly accessible under Apache 2.0.

Li Mao, Wei Du, Shuo Wen et al.

This study uses deep-learning models to predict city partition crime counts on specific days. It helps police enhance surveillance, gather intelligence, and proactively prevent crimes. We formulate crime count prediction as a spatiotemporal sequence challenge, where both input data and prediction targets are spatiotemporal sequences. In order to improve the accuracy of crime forecasting, we introduce a new model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. We conducted a comparative analysis to access the effects of various data sequences, including raw and binned data, on the prediction errors of four deep learning forecasting models. Directly inputting raw crime data into the forecasting model causes high prediction errors, making the model unsuitable for real - world use. The findings indicate that the proposed CNN-LSTM model achieves optimal performance when crime data is categorized into 10 or 5 groups. Data binning can enhance forecasting model performance, but poorly defined intervals may reduce map granularity. Compared to dividing into 5 bins, binning into 10 intervals strikes an optimal balance, preserving data characteristics and surpassing raw data in predictive modelling efficacy.

Yi Guo, Li Mao, Gongsen Zhang et al.

Recent studies have reported an increased risk of developing brain and neck tumors, as well as cataracts, in practitioners in interventional radiology (IR). Occupational radiation protection in IR has been a top concern for regulatory agencies and professional societies. To help minimize occupational radiation exposure in IR, we conceptualized a virtual reality (VR) based radiation safety training system to help operators understand complex radiation fields and to avoid high radiation areas through game-like interactive simulations. The preliminary development of the system has yielded results suggesting that the training system can calculate and report the radiation exposure after each training session based on a database precalculated from computational phantoms and Monte Carlo simulations and the position information provided in real-time by the MS Hololens headset worn by trainee. In addition, real-time dose rate and cumulative dose will be displayed to the trainee by MS Hololens to help them adjust their practice. This paper presents the conceptual design of the overall hardware and software design, as well as preliminary results to combine MS HoloLens headset and complex 3D X-ray field spatial distribution data to create a mixed reality environment for safety training purpose in IR.