Abstracts
We invite students, fellows, and early-career researchers to submit abstracts for talks or posters related to the workshop themes. Abstracts will be reviewed on a rolling basis and must be submitted by Wednesday, July 22, 2026, at 8:00 p.m. EST; all speakers and poster presenters will be notified by August 1. Please submit your abstract here.
Evolution, Adaptation, and the Foundations of Efficient Learning
This workshop will convene researchers from artificial intelligence, neuroscience, cognitive science, and related disciplines to examine the principles governing learning and training dynamics across natural and artificial systems.
Organized around themes at the intersection of evolution, biological intelligence, and AI, the meeting will explore how biological and artificial systems each achieve — or fail to achieve — efficiency, flexibility, robustness, and generalization. Researchers will explore how inductive bias, adaptation, inheritance, developmental constraints, selection, and multi-agent interaction may help illuminate the mechanisms underlying these similarities and differences and consider the forces that drive systems toward more generalized, versatile intelligence versus narrower specialization.
By bringing these perspectives into dialogue, the workshop aims to clarify shared theoretical questions, identify productive points of methodological and conceptual overlap, and stimulate new interdisciplinary collaborations on the foundations of learning in both biological and artificial domains.
Thematic areas:
- Comparative learning in biological and artificial systems
Why humans and other biological systems learn with greater efficiency, flexibility, and robustness than current machine learning models. - Training dynamics and inductive bias
How architectural constraints, priors, and learning dynamics shape what systems can learn, how quickly they learn, and how well they generalize. - Evolution, inheritance, and selection
What evolutionary processes can teach us about the emergence of adaptive behavior, structure, and learning efficiency in both natural and artificial systems. - Multi-agent learning and adaptation
How interaction among agents, environments, and populations influences learning, coordination, competition, and the development of complex behavior. - Foundations for next-generation learning systems
How cross-disciplinary perspectives might inform the design of more efficient, adaptive, and robust artificial intelligence systems.
Workshop Agenda:
Wednesday, September 2
- 10:00 AM–4:30 PM – The workshop will open with welcoming remarks, followed by invited talks in the morning and afternoon. Lunch will be provided.
- 4:30–6:30 PM – Poster reception.
Thursday, September 3
- 10:00 AM–4:15 PM – The day will include invited talks, lightning talks, lunch, and discussion.
Friday, September 4
- 10:00 AM–3:00 PM – The final day will include invited talks, discussion, lunch, and a closing address.
A more detailed schedule, including talk titles and timing, will be provided closer to the workshop date.