Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic operation. However, model mismatch across fidelities obscures the sources of performance loss and complicates the quantification of architecture-to-operation performance gaps. We propose an online, machine-learning-accelerated multi-resolution optimization framework that estimates an architecture-specific upper bound on achievable performance while minimizing expensive high-fidelity model evaluations. We demonstrate the approach on a pilot energy system supplying a 1 MW […]

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Ever since Meta announced its quarterly earnings, the upcoming foundational model, codenamed “Avocado,” has got all the hype. Now, according to an exclusive report by The Information, Avocado has completed its pre-training phase and is already outperforming leading open-source pre-trained models in testing. That’s according to an internal memo shared by Megan Fu, Product Manager of Meta’s Super Intelligence Lab. Why Avocado’s early lests have everyone buzzing inside Meta The memo, sent out last month, on January 20, […]

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We provide a computational complexity lens to understand the power of machine learning models, particularly their ability to model complex systems. Machine learning models are often trained on data drawn from sampleable or more complex distributions, a far wider range of distributions than just computable ones. By focusing on computable distributions, machine learning models can better manage complexity via probability. We abstract away from specific learning mechanisms, modeling machine learning as producing P/poly-computable distributions with polynomially-bounded max-entropy. We […]

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arXiv:2604.18761v1 Announce Type: new Abstract: This paper examines Web3 ecosystems not merely as markets for digital assets, but as networked social spaces where economic transactions give rise to enduring social ties, shared narratives, and collective identities. Leveraging large-scale data mining of fused on-chain blockchain transactions and off-chain social media activity, we analyze over one hundred NFT collections to uncover how different forms of participation structure community formation in decentralized environments. Using network analysis, we identify distinct ecosystem roles, […]

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arXiv:2604.17021v1 Announce Type: new Abstract: Video editing aims to modify input videos according to user intent. Recently, end-to-end training methods have garnered widespread attention, constructing paired video editing data through video generation or editing models. However, compared to image editing, the high annotation costs of video data severely constrain the scale, quality, and task diversity of video editing datasets when relying on video generative models or manual annotation. To bridge this gap, we propose LIVE, a joint training […]

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Meta, as you may know, has been pouring billions into AI ($65 billion to be precise in 2025), as it looks to keep up with the intensifying competition. While the company hoped its Llama model would come out strong. Sadly, it never did. But, that setback alone doesn’t appear to have hampered Meta’s future AI goals. Meta is now reportedly getting ready to push its next wave of AI products, and it seems that users won’t have to […]

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arXiv:2509.00923v2 Announce Type: replace-cross Abstract: Monte Carlo Counterfactual Regret Minimization (MCCFR) has emerged as a cornerstone algorithm for solving extensive-form games, but its integration with deep neural networks introduces scale-dependent challenges that manifest differently across game complexities. This paper presents a comprehensive analysis of how neural MCCFR component effectiveness varies with game scale and proposes an adaptive framework for selective component deployment. We identify that theoretical risks such as nonstationary target distribution shifts, action support collapse, variance explosion, […]

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Architectural Paradigms and Market Consolidation in Production AI Systems The AI tooling landscape of 2026 represents a fascinating inflection point in the maturation of large language model (LLM) applications. What began as a Cambrian explosion of specialized tools has rapidly consolidated into distinct architectural patterns, each optimizing for specific computational trade-offs and user interaction paradigms. The Foundation Model Triumvirate: Diverging Optimization Strategies The dominance of ChatGPT, Claude, and Gemini isn’t merely about brand recognition — it reflects fundamentally different approaches to […]

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We introduce a lightweight experimentation pipeline designed to lower the barrier for applying machine learning (ML) methods for classifying images in ecological research. We enable ecologists to experiment with ML models independently, thus they can move beyond off-the-shelf models and generate insights tailored to local datasets and specific classification tasks and target variables. Our tool combines a simple command-line interface for preprocessing, training, and evaluation with a graphical interface for annotation, error analysis, and model comparison. This design […]

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