Continuous Learning FOR AI Agents: NOT YET
Skills:
Reading ML Papers80%
Key Takeaways
Explores the concept of continuous learning for AI agents using in-context learning and evaluating frontier AI systems in real-world stateful environments
Original Description
The Problem w/ AI Was Never Intelligence. It Was Learning.
Continual Learning on the job.
Frontier AI fails in new benchmark on real world tasks.
Continuous Learning FOR AI Agents: NOT YET
In-Context Learning ICL Beats Claude Code
AI Is Smart. So Why Doesn’t It Learn?
All rights w/ authors:
CONTINUAL LEARNING BENCH: Evaluating Frontier
AI Systems in Real-World Stateful Environments
Parth Asawa∗
UC Berkeley
Christopher M. Glaze
Snorkel AI
Gabriel Orlanski
University of Wisconsin-Madison
Ramya Ramakrishnan
Snorkel AI
Benji Xu
UC Berkeley
Asim Biswal
UC Berkeley
Vincent Sunn Chen
Snorkel AI
Frederic Sala
University of Wisconsin-Madison, Snorkel AI
Matei Zaharia
UC Berkeley
Joseph E. Gonzalez
UC Berkeley
#airesearch
#aiexplained
#aicode
#aiagents
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