AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A dual-engine, AI-powered resume screening system designed for transparency and reproducibility, with a reproducible SBERT→XGBoost→SHAP classification pipeline, and a practitioner-oriented user interface that operationalizes explainability and auditability is presented.
A reproducible SBERT→XGBoost→SHAP classification pipeline, an LLM comparator with a standardized evaluation template, and a practitioner-oriented user interface that operationalizes explainability and auditability are presented.
This work investigates LLM-based evaluators of natural language generation quality mechanistically through an eight-attack perturbation taxonomy across the Readability and Adequacy dimensions of NLG quality, a generation pipeline that produces paired clean and corrupt summaries with controlled error intensity and expli...
The cluster loop yields the strongest held-out rubric on both evaluator tasks from a commercial search vertical, and is the only method robustly positive on both.
Treating supervision format as a first-class hyperparameter for multi-task reasoning SFT in large language models—at least in this benchmark-and-model setting—rather than a mere rendering detail is supported.
A comprehensive end-to-end intelligent recruitment system that exploits Natural Language Processing (NLP), supervised Machine Learning (ML) and predictive analytics to automate the process of resume parsing, skill extraction, ATS score prediction and candidate ranking is proposed.
Most automated essay scoring (AES) systems output a single holistic score without interpretable evidence and rely on closed APIs that introduce data privacy and cost barriers. We present ArguLens, an opensource, locally deployable system that decomposes AES into three decoupled components: a discourse-move classifier (...
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