AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,967 papers
GATK-SV A structural variation discovery pipeline for Illumina short-read whole-genome sequencing (WGS) data. Table of Contents Requirements Citation Acknowledgements Quickstart Pipeline Overview Cohort mode Single-sample mode gCNV model Generating a reference panel Module Descriptions GatherSampleEvidence - Raw caller...
This pre-decision research note applies the verified SECM–SEFSC Augmented ACM Model III to the Federal Open Market Committee meeting of September 15–16, 2026. With a 25-basis-point increase in the federal funds target range heavily priced into markets, the analysis argues that the dominant market signal is likely to co...
Preprint, version 1.0 (September 2026) The Transformer of 2017 still sits at the core of every current large language model, yet almost everything around it has been rebuilt, some parts repeatedly. This survey traces the architectural development of text-only LLMs from 2017 to early 2026 as four epochs, each driven by...
GATK-SV A structural variation discovery pipeline for Illumina short-read whole-genome sequencing (WGS) data. Table of Contents Requirements Citation Acknowledgements Quickstart Pipeline Overview Cohort mode Single-sample mode gCNV model Generating a reference panel Module Descriptions GatherSampleEvidence - Raw caller...
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Authorship, provenance, and verification. The scientific problem, original method, and research direction belong to W. V. Pogosov. This project builds on his published and unpublished work developed before the involvement of an advanced large language model (LLM). During the subsequent LLM-assisted phase, the LLM produ...
Large language models can readily produce diverse and unusual outputs, yet increased variation does not by itself explain when a system becomes more capable of creative thought. This paper develops a preliminary hypothesis that prompt design may alter the effective dynamical regime of inference and, under some conditio...
Industrial low-altitude multi-object tracking is challenged by overhead viewpoints, dense small targets, mutual occlusion, homogeneous appearances, and domain-shifted backgrounds. Vision-language pre-trained models provide useful semantic priors, but their category-level representations can mislead instance-level assoc...
GATK-SV A structural variation discovery pipeline for Illumina short-read whole-genome sequencing (WGS) data. Table of Contents Requirements Citation Acknowledgements Quickstart Pipeline Overview Cohort mode Single-sample mode gCNV model Generating a reference panel Module Descriptions GatherSampleEvidence - Raw caller...
✨ Highlights Review skill updates as a real diff. Update previews render through a shared source diff viewer — file headers with change counts, line-number gutter, dashed red deletion and solid green addition rails, optional syntax highlighting, and readable fallbacks for malformed or oversized patches. (#2632) Side c...
Classical software memory leaks are allocations that outlive their intended lifetime. Tool-using large language model (LLM) agents exhibit an analogous failure that current “forgetting” mechanisms do not measure. A user or policy issues a forget request for a memory m*; the underlying store correctly drops the record,...
Composed Image Retrieval (CIR) asks a system to retrieve a target image from a reference image and a natural-language modification. Although large vision-language models provide strong global image-text representations, CIR remains difficult because training data often contains verbose or weakly pragmatic modification...
GATK-SV A structural variation discovery pipeline for Illumina short-read whole-genome sequencing (WGS) data. Table of Contents Requirements Citation Acknowledgements Quickstart Pipeline Overview Cohort mode Single-sample mode gCNV model Generating a reference panel Module Descriptions GatherSampleEvidence - Raw caller...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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