recursive self-improvement
An AI system repeatedly uses its own outputs to enhance its capabilities, creating a feedback loop for autonomous improvement.
- Papers
- 9
- Released code
- 0
- First seen
- June 2026
- Latest
- Sept 2026
8 papers in the last two months, against 1 in the two before.
The papers
Most central to this idea first, not most recent.
- Industrycs.AI
Research agent improves itself through seven successive code rewrites
Weco AI · Sept 2026
- Chinese Techcs.AI
Restructuring agent environments improves performance on noisy, evolving tasks
Shanghai Jiao Tong University, Theseus Labs · Sept 2026
- Top Universitycs.AI
Trajectory shortcut trees improve agents without outcome labels or annotations
Fudan University, Meituan Longcat Team · Sept 2026
- Big Techcs.AI
Recursive harness search cuts coding-agent token use nearly in half
NVIDIA, NTU · Sept 2026
- Chinese Techcs.AI
Self-improving context programs help frozen models understand long videos
Tencent · Aug 2026
- Industrycs.AI
Co-trained agent roles improve tool-based reasoning and verification
Waseda University, Adelaide University · Sept 2026
- Big Techcs.AI
AI progress may continue past AGI toward superintelligence, not stop
Google DeepMind, University of Waterloo · June 2026
- Independentcs.AI
AI tutoring matches expert human tutoring on GRE learning gains
Sept 2026
- Independentcs.AI
Closed-loop development improves mobile agents across planning and tool use
Sept 2026
Concepts are extracted from each paper and reused across the corpus, so this page grows on its own as the desk reads.