arXiv:2601.10907v1 Announce Type: new Abstract: Networking is central to the growth and visibility of software engineering research and researchers. However, opportunities and capacities to build such networks are not easily identified and often are unevenly distributed. While networking is often viewed as an individual skill, a researchers workplace, culture and environment significantly influence their motivation and, consequently, the networks they form. This paper explores how factors such as country of residence, immigration status, language, gender, and surrounding context […]

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arXiv:2601.03298v1 Announce Type: new Abstract: This is a brief description of a project that has already autoformalized a large portion of the general topology from the Munkres textbook (which has in total 241 pages in 7 chapters and 39 sections). The project has been running since November 21, 2025 and has as of January 4, 2026, produced 160k lines of formalized topology. Most of it (about 130k lines) have been done in two weeks,from December 22 to January […]

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Cosine similarity is prevalent in contrastive learning, yet it makes an implicit assumption: embedding magnitude is noise. Prior work occasionally found dot product and cosine similarity comparable, but left unanswered WHAT information magnitude carries, WHEN it helps, and HOW to leverage it. We conduct a systematic study through a $2 times 2$ ablation that independently controls input-side and output-side normalization across text and vision models. Our findings reveal three key insights. First, in text retrieval, output (document) magnitude […]

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Offline reinforcement learning (RL) provides a compelling paradigm for training autonomous systems without the risks of online exploration, particularly in safety-critical domains. However, jointly achieving strong safety and performance from fixed datasets remains challenging. Existing safe offline RL methods often rely on soft constraints that allow violations, introduce excessive conservatism, or struggle to balance safety, reward optimization, and adherence to the data distribution. To address this, we propose Epigraph-Guided Flow Matching (EpiFlow), a framework that formulates safe offline […]

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arXiv:2602.00907v1 Announce Type: new Abstract: PyGALAX is a Python package for geospatial analysis that integrates automated machine learning (AutoML) and explainable artificial intelligence (XAI) techniques to analyze spatial heterogeneity in both regression and classification tasks. It automatically selects and optimizes machine learning models for different geographic locations and contexts while maintaining interpretability through SHAP (SHapley Additive exPlanations) analysis. PyGALAX builds upon and improves the GALAX framework (Geospatial Analysis Leveraging AutoML and eXplainable AI), which has proven to outperform […]

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I’ve spent some time recently building an RL agent to play competitive Pokémon (Generation 9 Random Battles on Pokémon Showdown). I wanted to share the architecture, the training pipeline, and some thoughts on the MCTS vs. pure-network approaches in this specific environment. Why Pokémon? From an RL perspective, a Pokémon battle is a great proxy for real-world, messy decision-making. It combines three massive headaches: Simultaneous Action: Both agents lock in actions concurrently. You are trying to approximate Nash […]

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Elastic scaling in cloud native environments is essential for maintaining service quality and resource efficiency. In practice, frequent traffic bursts and shifts in workload distributions make rule-based methods or approaches with a single optimization objective insufficient. They struggle to ensure system stability and decision reliability at the same time. To address this challenge, this study formulates elastic scaling as a risk-constrained reinforcement learning problem from a sequential decision perspective. A unified framework is used to model resource adjustment […]

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Diabetic foot ulcers (DFU) are one of the serious complications of diabetes that can lead to amputations and high healthcare costs. Regular monitoring and early diagnosis are critical for reducing the clinical burden and the risk of amputation. The aim of this study is to investigate the impact of using multimodal images on deep learning models for the classification of DFU stages. To this end, we developed a Raspberry Pi-based portable imaging system capable of simultaneously capturing RGB […]

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