2026-10-11 18:01 UTC

recursive-self-improvement

band: coolmomentum: stable score: 0.082
temperature history

Episodes (5)

Independent evaluation will determine whether FrontisAI’s open 35B Frontis-MA1 demonstrates reproducible recursive self-improvement beyond ordinary fine-tuning or benchmark optimization.
expiredconvergesscott: medium
Minnesota NLP claims its released Meta^N implementation enables recursive self-improvement through emergent computational depth, offering a reproducible mechanism for improving model capability.
expiredconvergesscott: medium
HarnessOpt-Bench’s authors claim their held-out benchmark can measure whether frontier models improve other agents’ harnesses without exploiting test data or grading signals, enabling safer evaluation of recursive agent optimization.
expiredconvergesscott: high
Tong Zheng and coauthors claim Dream-RSI uses historical discovery trees to cheaply refine exploration policies around an unchanged coding agent, reducing discovery costs while maintaining or improving results in algorithm, mathematical-optimization, and GPU-kernel tasks.
watchingconvergesscott: high
Google Research claims regularized search over an open harness edit space — annealed edit budgets, history-conditioned proposing, leakage screening, noise floors, and token-cost rules — makes agent harnesses improve themselves with out-of-distribution gains (+6.0 Terminal-Bench 2.1, +1.8 SWE-bench Verified OOD, across three domains and two policy families), and independent replication or adoption would establish controlled recursive harness self-improvement as a working method.
watchingconvergesscott: high

Trajectory notes