What do these badges mean?
- 🚀ShippingCode exists. Multiple GitHub repos already reference this paper — people are building on it.
- 📈ClimbingCitation velocity is rising. Researchers are starting to pick it up.
- 💤QuietPublished but no notable signal yet. Most papers live here — could become anything later.
- 🎭HypeHeavy social buzz but no shipping signal. The counter-signal — defer until Twitter/X data is wired up.
- 🚀Shipping2605.16215·May 15, 2026·~13 mincs.AIcs.CL
Fully Open Meditron: An Auditable Pipeline for Clinical LLMs
Xavier Theimer-Lienhard, Mushtaha El-Amin, Fay Elhassan, Sahaj Vaidya, +4
⭐ 434 stars / 25 repos📚 0 citesELI5Researchers built the first completely transparent medical AI model where you can see everything: what data it learned from, how it was cleaned, how it was trained, and how it works. They combined medical question datasets, added clinician-verified practice guidelines, and had doctors validate every step.
Problem solvedMedical AI systems need to be trustworthy and auditable for doctors to use them, but most 'open' models hide their training data and methods. This makes it impossible to validate they're safe or understand why they give certain answers—a critical problem in healthcare.
- 🚀Shipping2605.16113·May 15, 2026·~12 mincs.CLcs.AI
DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation
Rui Chu, Bingyin Zhao, Thanh Quoc Hung Le, Duy Cao Hoang, +5
⭐ 197 stars / 10 repos📚 0 citesELI5A system that fixes biased outputs from AI language models by automatically retrieving and inserting fairness-promoting text snippets into the model's context—no retraining needed, just smarter retrieval.
Problem solvedLanguage models produce biased, stereotyped responses about race, gender, and age. Fine-tuning fixes are expensive and degrade performance; this approach removes bias at inference time without touching the model.