AI Agents Demonstrate Recursive Self-Improvement in Experiment
latticecut · x · 2026-07-15
The WecoAI team presents experimental evidence of recursive self-improvement in AI agents. The researchers let AIDE² run autonomously for 8 days, involving two loops: an inner agent solves research tasks, while an outer agent continuously rewrites the inner agent's code framework based on performance.
After 100 iterations, the system autonomously discovered better search strategies, developed a 16× prompt compression memory system, and enhanced defenses against reward hacking. These improvements generalized to unseen new benchmarks, fully boosting agent performance.
Related event: AIDE² Self-Improvement Run Beats 2 Years of Manual Tuning(12 posts)→
More from coding & agent
- Astra storyboards plus Minimax H3 per-shot generation boost video success rates — Hailuo_AI · 2026-09-11
- Codex tip: use Sol with Astra and Luna sub-agents to save usage — pvncher · 2026-09-11
- agents-best-practices: a provider-neutral Agent Skill for designing and auditing agentic harnesses — tom_doerr · 2026-09-11
- Cognition's SWE-2 uses a KKT duality argument in RL to shift the effort Pareto curve — YouJiacheng · 2026-09-11
- First-ever Three.js Conference lands in Paris, with a panel on AI-shortened design workflows — OdinLovis · 2026-09-11
- Agile co-author Ron Jeffries publishes 'Resist AI', urging developers to push back — mborch · 2026-09-11