Development Roadmap
Seton Labs
community
AI & ML interests
Generalization
Recent Activity
Organization Card
Who We Are
An open research community where contributors work together to expand the limits of AI capability.
What We Do
- Build benchmarks and datasets
- Evaluate models with partners
- Coordinate with other communities
Principles
We prioritize quality over quantity — focused on meaningful research impact, not volume.
Latest Releases
Why Generalization?
Modern AI performs well on familiar data but struggles with distribution shifts and unseen domains. At Seton Labs, we tackle out-of-distribution (OOD) challenges to build systems that generalize beyond their training conditions.
Name Conventions
We use simple and consistent naming rules to keep benchmarks easy to read, compare, and scale over time.
Difficulty levels: effortless · easy · mid · hard · ultra hard
Each level is based on three factors: number of rows · output size (tokens) · variety of categories and subcategories
Dataset naming format:
bench-(tier)-(month)-(year)