January 2026

Credit default risk arises from complex interactions among borrowers, financial institutions, and transaction-level behaviors. While strong tabular models remain highly competitive in credit scoring, they may fail to explicitly capture cross-entity dependencies embedded in multi-table financial histories. In this work, we construct a massive-scale heterogeneous graph containing over 31 million nodes and more than 50 million edges, integrating borrower attributes with granular transaction-level entities such as installment payments, POS cash balances, and credit card histories. We evaluate heterogeneous […]

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A large number of heuristics have been proposed to optimize the reinforcement fine-tuning of LLMs. However, inconsistent claims are made from time to time, making this area elusive. Reflecting on this situation, two fundamental questions still lack a clear understanding: 1) what is the role of each optimizing choice? 2) which ones are the bottlenecks? This paper aims to shed light on them, and it faces the challenge of several entangled confounding factors in the fine-tuning process. To […]

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The requirement for expert annotations limits the effectiveness of deep learning for medical image analysis. Although 3D self-supervised methods like volume contrast learning (VoCo) are powerful and partially address the labeling scarcity issue, their high computational cost and memory consumption are barriers. We propose 2D-VoCo, an efficient adaptation of the VoCo framework for slice-level self-supervised pre-training that learns spatial-semantic features from unlabeled 2D CT slices via contrastive learning. The pre-trained CNN backbone is then integrated into a CNN-LSTM […]

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I have been writing articles on freeCodeCamp for a while (20+ articles, 240K+ views). Recently, I completed my biggest project! I explain the math from an engineering perspective and connect how math solves real life problems and makes billion dollar industries possible. For example, in “Chapter 6: Probability & Statistics – Learning from Uncertainty” I explain how Markov chains allow the application of the Markov decision processes, which is the foundation for all RL and DRL. The chapters: […]

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Electricity use of AI coding agents Previous work estimating the energy and water cost of LLMs has generally focused on the cost per prompt using a consumer-level system such as ChatGPT. Simon P. Couch notes that coding agents such as Claude Code use way more tokens in response to tasks, often burning through many thousands of tokens of many tool calls. As a heavy Claude Code user, Simon estimates his own usage at the equivalent of 4,400 “typical […]

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Partial differential equations (PDEs) are central to scientific modeling. Modern workflows increasingly rely on learning-based components to support model reuse, inference, and integration across large computational processes. Despite the emergence of various physics-aware data-driven approaches, the field still lacks a unified perspective to uncover their relationships, limitations, and appropriate roles in scientific workflows. To this end, we propose a unifying perspective to place two dominant paradigms: Physics-Informed Neural Networks (PINNs) and Neural Operators (NOs), within a shared design […]

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