Recursive self-improvement
What forms of self-refinement, self-evaluation, and autonomous research loops produce real improvement rather than cosmetic optimization?
Recursive self-improvement, measured against reality.
Grounding long-horizon AI systems in verifiable evidence.
DeepGrounding is an independent research organization studying how self-improving AI systems can remain honest, calibrated, and useful as their planning horizons lengthen.
Research program
Three questions guide the work.
Long-horizon agents
How should we evaluate systems that operate over many steps, revise their own plans, and accumulate hidden failure modes over time?
Grounding and evaluation
Which external signals, tests, audits, and verification protocols keep self-improving systems connected to truth instead of their own preferences?
Operating principles
Publish cautiously. Measure directly. Make claims falsifiable.
DeepGrounding treats recursive self-improvement as an empirical measurement problem: a system should not receive credit for improvement unless its gains survive independent checks.
Current work focuses on calibration floors, evaluator drift, verifier circularity, and the gap between self-reported progress and externally grounded performance.
Public research pages are being prepared. This site will expand as manuscripts, code, and reproducibility bundles become ready for release.
Contact
For research discussions, collaborations, or replication questions.
Email deepgroundingai@gmail.com or follow the work on GitHub.