Reading Group (+🧋): Continual Learning Bench

Join the Snorkel AI Reading Group, a recurring forum to explore the latest frontier developments in AI while building meaningful connections within the community.

In this afternoon's session, Parth Asawa from UC Berkeley will present his recent paper, Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments, a collaboration with Snorkel AI and the University of Wisconsin-Madison.

Agenda:

• 5:15 pm - Doors open
• 5:30 pm - Talk begins

🧋🧋🧋 Boba tea and other refreshments will be provided! 🧋🧋🧋

Key Takeaways:

• Understand what separates an agent that truly learns on the job from one that just appears smart on a single task.
• Discover how Continual Learning Bench tests six real domains, from coding to poker to epidemiology, where agents must adapt across sequences, not solve problems in isolation.
• Learn why a new "gain" metric is the only fair way to measure learning, stripped of raw model skill.
• Find out why simple context memory beats expensive, dedicated memory systems like Mem0 and ACE.
• Explore why even the best AI system today only captures a quarter of the learning that's possible.

Continual Learning Bench is a collaboration between UC Berkeley, Snorkel AI, and the University of Wisconsin-Madison, supported by Snorkel AI's Open Benchmarks Grants program and the Laude Institute's Laude Slingshots program.

Location: 101 Second Street, San Francisco, CA 94105, USA