Survey Maps Trustworthy Recursive Self-Improvement: Risks, Evaluation, Open Problems
Jiayue Pu · hf · 2026-10-27
A team from Renmin University of China's Gaoling School of AI and UCAS publishes a survey, Trustworthy Recursive Self-Improvement in the Era of Large Models, offering an operational definition and process framework for RSI.
- RSI extends experience-driven updates beyond models, prompts, memories, tools, and workflows to inductive biases and the mechanisms governing future updates — mechanisms that can themselves be revised and reused across improvement cycles.
- Self-modification alone doesn't guarantee trustworthy improvement: gains may reflect flawed evaluations, acquired capabilities may deteriorate, constraint compliance may weaken, compromised artifacts or sensitive data may persist, and changes to improvement mechanisms can invalidate earlier verification and weaken human oversight.
- The survey organizes trustworthy RSI requirements into improvement validity, constraint preservation, and oversight across recursive update stages, and outlines open challenges.
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