The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on parameter uncertainty, such as variable processing times, and typically adopt the actor-critic framework. In this paper, we explore a different but prevalent form of uncertainty in JSSP: structural uncertainty. Structural uncertainty arises when a job may follow one of several routing paths, and the selection is determined not by policy, but by situational factors (e.g., the […]

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I built an open-source Python pipeline that scans multiple codebases, extracts structured information using LLMs, and generates Markdown documentation with Mermaid diagrams — all with incremental processing so only changed files get re-analyzed. No wasted LLM calls, no stale docs. ⭐ ==The entire project is open source — grab it, fork it, ship it: full source code on GitHub. Star CocoIndex if it is helpful!== The Problem Every Engineering Team Knows Too Well Documentation rots. It’s one of the […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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Numbers such as π, e, log 2 or √2 have binary digits (bits) that look randomly distributed. They are very good candidates to generate randomness especially in cryptography. One way to assess their randomness is by proving that they are normal numbers. Such a proof has remained elusive for centuries. Here I focus on a different type of numbers that seem a lot easier to handle, such as (2π)-1 arccos(3/5). To test normality, the most well-known tool is […]

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arXiv:2603.26667v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a widely adopted paradigm for enhancing the reliability of large language models (LLMs). However, RAG systems are sensitive to retrieval strategies that rely on text chunking to construct retrieval units, which often introduce information fragmentation, retrieval noise, and reduced efficiency. Recent work has even questioned the necessity of RAG, arguing that long-context LLMs may eliminate multi-stage retrieval pipelines by directly processing full documents. Nevertheless, expanded context capacity alone […]

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Real-world machine learning on tabular data relies on complex data preparation pipelines for prediction, data integration, augmentation, and debugging. Designing these pipelines requires substantial domain expertise and engineering effort, motivating the question of how large language models (LLMs) can support tabular ML through code synthesis. We introduce SemPipes, a novel declarative programming model that integrates LLM-powered semantic data operators into tabular ML pipelines. Semantic operators specify data transformations in natural language while delegating execution to a runtime system. […]

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