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.
- 🚀Shipping2605.16217·May 15, 2026·~13 mincs.CLcs.AIcs.IR
Argus: Evidence Assembly for Scalable Deep Research Agents
Zhen Zhang, Liangcai Su, Zhuo Chen, Xiang Lin, +6
⭐ 123 stars / 23 repos📚 0 citesELI5A research AI system where one agent searches for evidence pieces while another agent tracks what's been found, spots what's missing, and assembles everything into a final answer—like coordinating a team to complete a jigsaw puzzle instead of having everyone solve it separately.
Problem solvedCurrent AI research agents waste compute by running parallel searches that duplicate effort instead of finding new information, and they struggle to fit all the results into context windows. This system makes parallel searching actually efficient by tracking what's been gathered and targeting searches at gaps.
- 🚀Shipping2605.16117·May 15, 2026·~9 mincs.CL
SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
Xin Zhang, Yang Cao, Baoxing Wu, Kai Song, +1
⭐ 199 stars / 10 repos📚 0 citesELI5A system that helps AI language models answer tricky questions by first building a small, focused map of relevant facts from a knowledge base, then walking through that map step-by-step to reach a reliable answer.
Problem solvedLanguage models often hallucinate or give inconsistent answers on complex reasoning tasks because they're working from just their training data. This grounds them in real, structured facts and makes their reasoning process traceable and verifiable.
- 🚀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.