A 97-page survey maps self-improving agents — reality is humbler than the vision
maier_ak · x · 2026-09-22
Andreas Maier's full read of 'Self-Improvements in Modern Agentic Systems: A Survey' (arXiv, July 2026), 97 pages, 670 references, by 12 authors from Jilin University, KAUST, University of Alberta, and IDSIA with Schmidhuber as last author.
- Framing: from I. J. Good's 1966 'last invention' to the question of whether agents can persistently improve themselves
- Core formalism: agent = (θ,Σ); self-improvement updates θ (slow loop) or Σ (fast loop)
- Unifies scattered literatures — meta-learning, self-training, prompt optimization, tool creation, evolutionary program synthesis — under one vocabulary and evaluation protocol
- Reality check: most work only tweaks the scaffold; weight updates are rare and risk collapse; judge metrics may over-optimize to biased evaluators
- Verdict: a good map, but of a smaller territory than the epigraph promises
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