Transformer-based reinforcement learning has emerged as a strong candidate for sequential control in residential energy management. In particular, the Decision Transformer can learn effective battery dispatch policies from historical data, thereby increasing photovoltaic self-consumption and reducing electricity costs. However, transformer models are typically too computationally demanding for deployment on resource-constrained residential controllers, where memory and latency constraints are critical. This paper investigates knowledge distillation to transfer the decision-making behaviour of high-capacity Decision Transformer policies to compact models that […]

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In this article we develop a new method for summarizing a ranking distribution, textit{i.e.} a probability distribution on the symmetric group $mathfrak{S}_n$, beyond the classical theory of consensus and Kemeny medians. Based on the notion of textit{local ranking median}, we introduce the concept of textit{consensus ranking distribution} ($crd$), a sparse mixture model of Dirac masses on $mathfrak{S}_n$, in order to approximate a ranking distribution with small distortion from a mass transportation perspective. We prove that by choosing the […]

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arXiv:2506.06185v2 Announce Type: replace-cross Abstract: We systematically study antithetic initial noise in diffusion models, discovering that pairing each noise sample with its negation consistently produces strong negative correlation. This universal phenomenon holds across datasets, model architectures, conditional and unconditional sampling, and even other generative models such as VAEs and Normalizing Flows. To explain it, we combine experiments and theory and propose a textit{symmetry conjecture} that the learned score function is approximately affine antisymmetric (odd symmetry up to a […]

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Mutuum Finance (MUTM), a new crypto project focused on decentralized lending and borrowing, has confirmed the completion of its V1 smart contract audit conducted by Halborn Security. The update marks an important step in the project’s roadmap as it prepares for the initial deployment of its protocol. With core contracts reviewed and development milestones progressing, Mutuum Finance continues to move from design into execution within the DeFi crypto sector. Overview of The Mutuum Finance (MUTM) Mutuum Finance is […]

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I’m planning to fine-tune OSS-120B (or Qwen3-30B-A3B-Thinking-2507) on a mixed corpus: ~10k human-written Q&A pairs plus ~80k carefully curated synthetic Q&A pairs that we spent a few months generating and validating. The goal is to publish an open-weight model on Hugging Face and submit the work to an upcoming surgical conference in my country. The model is intended to help junior surgeons with clinical reasoning/support and board-style exam prep. I’m very comfortable with RAG + inference/deployment, but this […]

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arXiv:2602.21408v1 Announce Type: cross Abstract: Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian predictive distributions limit their reach. We propose Generative Bayesian Computation (GBC) via Implicit Quantile Networks (IQNs) as a surrogate framework that targets all three limitations. GBC learns the full conditional quantile function from input–output pairs; at test time, a single forward pass per quantile level produces draws from the predictive distribution. Across fourteen […]

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arXiv:2603.16877v1 Announce Type: new Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model. We conduct systematic evaluation using the FinDER benchmark dataset, comprising […]

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arXiv:2603.20311v1 Announce Type: new Abstract: Modern machine learning systems rely on complex data engineering workflows to extract, transform, and load (ELT) data into production pipelines. However, constructing these pipelines remains time-consuming and requires substantial expertise in data infrastructure and orchestration frameworks. Recent advances in large language model (LLM) agents offer a potential path toward automating these workflows, but existing approaches struggle with under-specified user intent, unreliable tool generation, and limited guarantees of executable outputs. We introduce kRAIG, an […]

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