February 2026

After my stint with Openclaw, I decided to build mine. While deciding how to move forward, I discovered Nanobot. It caught my attention immediately: only ~4,000 lines of Python versus OpenClaw’s 430,000+ lines of TypeScript. Clean, readable architecture. Easy to audit and extend. Actually designed for research and modification. Despite its elegance, it was still lacking in the security department. I decided to use it as a foundation and extend it myself. Enter Minion The name was inspired […]

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When people talk about marketplaces, they usually talk about demand – growth, users, and marketing channels.  But in practice, marketplaces live or die by supply – and by how early you turn supply into a system rather than a manual operation. I learned this the hard way while building and scaling a two-sided marketplace in the local services space. Over several years, my team and I grew the platform from an early proof-of-concept to tens of thousands of […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results D. Additional Results The hyperbolic partitioning trees of Cora is visualized in […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results C. Technical Details C.1. Notations The mathematical notations are described in Table […]

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Scientific discovery pipelines typically involve complex, rigid, and time-consuming processes, from data preparation to analyzing and interpreting findings. Recent advances in AI have the potential to transform such pipelines in a way that domain experts can focus on interpreting and understanding findings, rather than debugging rigid pipelines or manually annotating data. As part of an active collaboration between data science/AI researchers and behavioral neuroscientists, we showcase an example AI-enhanced pipeline, specifically designed to transform and accelerate the way […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results B. Hyperbolic Space B.1. Riemannian Manifold B.2. Models of Hyperbolic Space B.3. […]

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RAG systems fail because of broken data pipelines, not bad models. Poor chunking orphans meaning. Stale indexes serve outdated answers. Missing metadata can’t filter irrelevant results. Fix Monday: audit your corpus completeness, rebuild indexes regularly, and measure retrieval recall before blaming the LLM. RAG is search with a generative frontend—treat it like one.

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results Appendix The appendix is structured in four sections. A. Proofs on differential […]

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Coordinating actions is the most fundamental form of cooperation in multi-agent reinforcement learning (MARL). Successful decentralized decision-making often depends not only on good individual actions, but on selecting compatible actions across agents to synchronize behavior, avoid conflicts, and satisfy global constraints. In this paper, we propose Action Graph Policies (AGP), that model dependencies among agents’ available action choices. It constructs, what we call, textit{coordination contexts}, that enable agents to condition their decisions on global action dependencies. Theoretically, we […]

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