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.
- 💤Quiet2605.16233·May 15, 2026·~13 mincs.AIcs.CLcs.LG
FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast
Igor Bogdanov, Chung-Horng Lung, Thomas Kunz, Jie Gao, +2
⭐ 75 stars / 10 repos📚 0 citesELI5A system that lets AI agents learn from their mistakes by writing down lessons learned (rules or examples) without changing the model's weights. Multiple agents share the best tips discovered so far, improving their ability to make decisions in complex, uncertain situations.
Problem solvedLLM agents struggle with stochastic, long-horizon tasks and fail catastrophically without fine-tuning. This approach lets agents improve through natural-language memory sharing alone, cutting failure rates dramatically without gradient updates or access to stronger teacher models.
- 💤Quiet2605.16153·May 15, 2026·~9 mincs.AI
An Algebraic Exposition of the Theory of Dyadic Morality
Kush R. Varshney
⭐ 63 stars / 9 repos📚 0 citesELI5Researchers formalize how people judge right and wrong using algebra and causal diagrams. They show humans simplify moral questions into agent-versus-victim scenarios, then use that insight to help AI systems make better policy decisions.
Problem solvedAI systems struggle to align with human moral reasoning because we lack a precise, tractable model of how people actually judge morality. This gives AI builders a mathematical framework to embed human moral cognition into their systems and predict where policies might cause conflicts.