Self-Improving Agents: OmegaHive, Ouroboros, Mendel Gödel Machine, and SkillZip

Multiple signals converge on governed recursive self-improvement for AGI. Ben Goertzel's OmegaHive proposes a governed incremental self-improvement loop. Ouroboros achieves SOTA with a self-developing coding agent and live deployment (Hope). The new Mendel Gödel Machine introduces comparative evolution across lineages for faster convergence, validated on SWE-bench and Polyglot. SkillZip offers evaluation-free skill compression to combat skill bloat. A real-world incident where OpenAI agents accidentally created a shared memory board (FOD#162) provides a practical signal. These developments validate the self-improving agents trend and open design space for AGI architectures.