arXiv:2601.02522v1 Announce Type: new Abstract: The rising energy demands of machine learning (ML), e.g., implemented in popular variants like retrieval-augmented generation (RAG) systems, have raised significant concerns about their environmental sustainability. While previous research has proposed green tactics for ML-enabled systems, their empirical evaluation within RAG systems remains largely unexplored. This study presents a controlled experiment investigating five practical techniques aimed at reducing energy consumption in RAG systems. Using a production-like RAG system developed at our collaboration partner, […]

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Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended. Is such progress avoidable? If not to be avoided, can events be guided so that we may survive? These questions are investigated. Some possible answers (and some further dangers) are presented.

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Key Highlights: Spotify’s New “Messages” Features Are Here It’s been about a year since Spotify introduced its “Messages” feature, and the numbers are in: people love talking about music. According to Spotify’s official website, nearly 40 million users have already exchanged more than 340 million messages inside the app. Spotify says the updates are designed to make listening more social, though similar features have existed on desktop for years. Following an announcement on Spotify’s website, the platform is […]

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arXiv:2601.03359v1 Announce Type: new Abstract: Large Language Models (LLMs) often generate substantively relevant content but fail to adhere to formal constraints, leading to outputs that are conceptually correct but procedurally flawed. Traditional prompt refinement approaches focus on rephrasing the description of the primary task an LLM has to perform, neglecting the granular constraints that function as acceptance criteria for its response. We propose a novel multi-agentic workflow that decouples optimization of the primary task description from its constraints, […]

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arXiv:2601.04221v1 Announce Type: new Abstract: This paper presents a new approach to algorithmic composition, called predictive controlled music (PCM), which combines model predictive control (MPC) with music generation. PCM uses dynamic models to predict and optimize the music generation process, where musical notes are computed in a manner similar to an MPC problem by optimizing a performance measure. A feedforward neural network-based assessment function is used to evaluate the generated musical score, which serves as the objective function […]

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OpenAI team has released their openai/circuit-sparsity model on Hugging Face and the openai/circuit_sparsity toolkit on GitHub. The release packages the models and circuits from the paper ‘Weight-sparse transformers have interpretable circuits‘. https://arxiv.org/pdf/2511.13653 What is a weight sparse transformer? The models are GPT-2 style decoder only transformers trained on Python code. Sparsity is not added after training, it is enforced during optimization. After each AdamW step, the training loop keeps only the largest magnitude entries in every weight matrix […]

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