arXiv:2601.04264v1 Announce Type: new Abstract: Deep learning models, particularly recurrent neural networks and their variants, such as long short-term memory, have significantly advanced time series data analysis. These models capture complex, sequential patterns in time series, enabling real-time assessments. However, their high computational complexity and large model sizes pose challenges for deployment in resource-constrained environments, such as wearable devices and edge computing platforms. Knowledge Distillation (KD) offers a solution by transferring knowledge from a large, complex model (teacher) […]

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I. The “Complete System” Fallacy 1.1 The Problem The pursuit of ethical Artificial Intelligence (AI) has been defined by a single, implicit ambition: the creation of a “complete” ethical machine. This is the dream of building an autonomous system whose internal logic, training data, and reward functions are comprehensive enough to resolve any moral dilemma it encounters without human intervention. From the rigid rule-sets of symbolic AI to the “aligned” reinforcement learning models of today, the governing philosophy […]

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Hi guys, I’m learning meta RL. I’m trying to try the RL2 algorithm with some Gymnasium environments. However, it seems that there is no implementation of this algorithm in current RL libraries like rllib, stable-baselines3, TorchRL. Do you have any ideas of implementing of this RL algorithm? Which library should I use? submitted by /u/ZitaLovesCats [link] [comments]

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Key Highlights: Spotify isn’t shying away from bringing new AI features to its platform. The music-streaming giant has now announced a new feature that gives users more control over the service’s algorithm. Prompted Playlists is an evolved version of Spotify’s AI Playlists Well… that’s how Spotify is positioning the launch of its new “Prompted Playlists” feature. So, what’s new feature all about? Basically, it allows you to describe what you want to hear in a personalized playlist, which […]

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Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of […]

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arXiv:2601.03268v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has shifted language model evaluation toward reasoning and problem-solving tasks as measures of general intelligence. Small Language Models (SLMs) — defined here as models under 10B parameters — typically score 3-4 times lower than LLMs on these metrics. However, we demonstrate that these evaluations fail to capture SLMs’ effectiveness in common industrial applications, such as tone modification tasks (e.g., funny, serious, professional). We propose an evaluation […]

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arXiv:2601.02553v1 Announce Type: new Abstract: To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundancy, or rely on iterative reasoning to filter noise, incurring high token costs. To address this challenge, we introduce SimpleMem, an efficient memory framework based on semantic lossless compression. We propose a three-stage pipeline designed to maximize information density and token […]

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