arXiv:2601.00172v1 Announce Type: new Abstract: Forecasting high-dimensional spatiotemporal systems remains computationally challenging for recurrent neural networks (RNNs) and long short-term memory (LSTM) models due to gradient-based training and memory bottlenecks. Reservoir Computing (RC) mitigates these challenges by replacing backpropagation with fixed recurrent layers and a convex readout optimization, yet conventional RC architectures still scale poorly with input dimensionality. We introduce a Sequential Reservoir Computing (Sequential RC) architecture that decomposes a large reservoir into a series of smaller, interconnected […]

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arXiv:2601.02414v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) aims to perceive human emotions through three modes: language, vision, and audio. Previous methods primarily focused on modal fusion without adequately addressing significant distributional differences among modalities or considering their varying contributions to the task. They also lacked robust generalization capabilities across diverse textual model features, thus limiting performance in multimodal scenarios. Therefore, we propose a novel approach called Modality Interaction and Alignment Representation (MIAR). This network integrates contextual […]

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Database design is more than schemas and tables—it’s storytelling. This piece reframes relational modeling as narrative construction, showing how clear predicates create meaning, why ignoring foundational rules leads to poor performance, and why AI can’t fix broken data structures. Strong databases come from humans who understand the story their data must tell.

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Cultural historian Thomas Berry issues a profound summons to reimagine humanity’s place within the living fabric of the Earth. He envisions a radical transformation—from a species that wounds its home to one that nurtures it—calling for the “resacralization of nature” and a renewal of our ecological consciousness. More than a philosophical reflection, Berry’s work is a moral and spiritual manifesto, demanding an overhaul of our ethics, politics, economics, and education. Only through this sweeping reorientation, he insists, can […]

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Piotr Niedźwiedź, CEO/CTO and founder of neptune.ai I’m excited to share that we’ve entered into a definitive agreement to be acquired by OpenAI, subject to closing conditions. We are thrilled to join the OpenAI team and help their AI researchers build better models faster. We started in 2017, when the pivotal transformer paper came out. And from then until today, we have focused on helping people who build ML models during the iterative, messy, and unpredictable phase of […]

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In 2025, the pressure on business leaders to adopt AI and demonstrate immediate financial returns has reached unprecedented levels. With technology budgets steadily climbing to a projected 14% of total revenue a significant increase from previous years the C-suite has definitively shifted from speculative experimentation to demanding a rigorous quantification of AI’s financial impact. But the true path to generating sustainable value from AI is filled with counter-intuitive realities and hidden complexities that extend far beyond the mainstream […]

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Built a text classifier that automatically rates code comment quality to help with documentation reviews. **Quick Stats:** – 🎯 94.85% accuracy on test set – 🤖 Fine-tuned DistilBERT (66.96M params) – 🆓 MIT License (free to use) – ⚡ Easy integration with Transformers **Categories:** 1. Excellent (100% precision) – Comprehensive, clear documentation 2. Helpful (89% precision) – Good but could be better 3. Unclear (100% precision) – Vague or confusing 4. Outdated (92% precision) – Deprecated/TODO comments **Try […]

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arXiv:2601.04365v1 Announce Type: new Abstract: In evolutionary reinforcement learning tasks (ERL), agent policies are often encoded as small artificial neural networks (NERL). Such representations lack explicit modular structure, limiting behavioral interpretation. We investigate whether programmatic policies (PERL), implemented as soft, differentiable decision lists (SDDL), can match the performance of NERL. To support reproducible evaluation, we provide the first fully specified and open-source reimplementation of the classic 1992 Artificial Life (ALife) ERL testbed. We conduct a rigorous survival analysis […]

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Existing methods for fault detection in cloud and quantum systems are powerful but brittle. They struggle with unknown failures, rely on inflexible recovery playbooks, and use fixed quantum error correction (QEC) schemes, a significant problem in diverse multi-cloud settings. To overcome these issues, we introduce textbf{Intelligent Multi-Cloud Fault Detection with Adaptive Quantum Error Correction}. Our framework is built on three pillars: hierarchical multi-agent learning, adaptive multi-cloud execution, and predictive QEC. Specialized agents learn from experience, while the system […]

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