Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning […]

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I was prototyping a 2D Side Scroller game in Unity a week ago and started implementing an Inventory System (Equipments, slots, etc…). Unfortunately, Unity didn’t have a Context Menu out of the box so I searched a bit and found this, which helped me get an idea of how to implement one as well as make it completely mouseless, since my game was mouseless itself.

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arXiv:2601.17110v1 Announce Type: new Abstract: Accurate short-term energy consumption forecasting is essential for efficient power grid management, resource allocation, and market stability. Traditional time-series models often fail to capture the complex, non-linear dependencies and external factors affecting energy demand. In this study, we propose a forecasting approach based on Recurrent Neural Networks (RNNs) and their advanced variant, Long Short-Term Memory (LSTM) networks. Our methodology integrates historical energy consumption data with external variables, including temperature, humidity, and time-based features. […]

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This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fixed plans, construction tasks are defined through targets and obstacles, allowing the system to adapt more flexibly to environmental uncertainty and variations during the building process. A reinforcement learning (RL) policy, trained using deep Q-learning with successor features, serves as the decision-making component. As a proof of concept, we evaluate the approach on a benchmark of […]

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arXiv:2602.18997v2 Announce Type: replace Abstract: We investigate Stochastic Mirror Descent (SMD) with matrix parameters and vector-valued predictions, a framework relevant to multi-class classification and matrix completion problems. Focusing on the overparameterized regime, where the total number of parameters exceeds the number of training samples, we prove that SMD with matrix mirror functions $psi(cdot)$ converges exponentially to a global interpolator. Furthermore, we generalize classical implicit bias results of vector SMD by demonstrating that the matrix SMD algorithm converges to […]

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In our Haskell in Production series, we interview developers and technical leaders from companies that use Haskell for real-world tasks. We cover benefits, downsides, common pitfalls, and tips for building useful Haskell products. This time, we have quite a special guest – Simon Marlow from Meta. He’s one of the co-authors of the Glasgow Haskell Compiler (GHC) and the author of Parallel and Concurrent Programming in Haskell. Currently, he’s working at Meta on Glean, a system for collecting, […]

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arXiv:2602.17711v1 Announce Type: new Abstract: Multi-branch deep neural networks like AASIST3 achieve state-of-the-art comparable performance in audio anti-spoofing, yet their internal decision dynamics remain opaque compared to traditional input-level saliency methods. While existing interpretability efforts largely focus on visualizing input artifacts, the way individual architectural branches cooperate or compete under different spoofing attacks is not well characterized. This paper develops a framework for interpreting AASIST3 at the component level. Intermediate activations from fourteen branches and global attention modules […]

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You’ve spent weeks building a sophisticated LangGraph.js agent. It can research, write, and execute complex tasks. You show it to a user, they type a prompt, and… they wait. And wait. That spinning loader is a silent killer of user engagement, turning your powerful AI into a sluggish, unresponsive tool. This is the latency gap—the frustrating chasm between a user’s action and your agent’s response. But what if the interface didn’t have to wait? What if it assumed success and […]

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arXiv:2602.18461v1 Announce Type: new Abstract: The rapid evolution of Agentic AI and large language models (LLMs) presents transformative opportunities for higher education institutions. This chapter introduces the concept of self-driving universities, a vision in which AI-enabled systems progressively automate administrative, academic, and quality-assurance processes through a staged autonomy model inspired by self-driving systems. We examine the current challenges facing traditional universities, including bureaucratic overload, fragmented information systems, and the disproportionate amount of time faculty spend on clerical tasks, […]

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