Beyond Static Intelligence: Evaluating Continual Learning

Official Schedule Context

Continual learning, the ability of AI systems to improve through sequential experience, has

attracted substantial interest, but no high-quality benchmark exists to evaluate it. We introduce

Continual Learning Bench (CL-Bench), the first difficult, expert-validated benchmark designed to

measure whether LLM-based systems genuinely improve with experience. CL-Bench spans six diverse

domains (software engineering, signal processing, disease outbreak forecasting, database querying,

strategic game-playing, and demand forecasting), each validated by domain experts and designed so

that tasks share a learnable latent structure (codebase layout, disease outbreak dynamics, opponent

strategies) that a stateful system can discover online but a stateless one cannot. We evaluate

frontier models across several agent architectures, from naive in-context learning (ICL) to

dedicated memory systems, introducing a gain metric to isolate learning from prior capabilities. We

find that these systems leave headroom for improved continual learning: agents frequently overfit to

immediate observations or fail to reuse knowledge across instances, and dedicated memory systems do

not fix this---in fact, naive ICL outperforms systems dedicated to memory management. CL-Bench is

the first benchmark to evaluate continual learning across diverse real-world domains with expert-

validated tasks and isolate online learning from underlying model capability, showing a need for

better continual learning systems.

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