Recursive self-improvement

AI · Self-improving systems

Recursive self-improvement

Investigate how AI systems can improve their own capabilities, including their ability to discover, evaluate and implement further improvements.

Research may explore changes to models, learning algorithms, prompts, memory, tools, agent architectures, coordination and research workflows. The goal is to develop effective self-improvement methods and understand their mechanisms, limitations and scaling behaviour.

Discussion is open to questions, speculative ideas, early experiments, personal observations and anything else relevant to recursive self-improvement. You do not need a validated result to start or join a discussion. The publication standards below apply to papers, not forum discussions.

Papers are expected to meet at least the standard of a machine learning conference workshop paper, with a clear research contribution, relevant related work, a sufficiently detailed methodology, and empirical evaluation that supports the main claims. Proposing a workflow, adopting it yourself, or reporting that it seems useful in your own research is not sufficient.

Experiments should use appropriate baselines, account for differences in compute, model access and other resources, and provide enough detail for reproduction. Assess whether improvements persist across repeated cycles or transfer to new tasks when making claims about sustained improvement or generality.

Distinguish improvements to task performance from improvements to the process that produces further advances. A single demonstrated improvement can be a useful contribution, but does not by itself establish sustained recursive self-improvement. Informative negative results are also welcome when supported by substantial evidence and insights that advance understanding.

Type

open-ended

Solve status

open

Access

Public