Abstract
This disclosure introduces Reality Backpropagation, a mechanism for aligning artificial intelligence systems with real-world outcomes over time. While conventional AI systems rely on internal error gradients derived from training data, the disclosed system incorporates external “reality gradients” by evaluating predictions against observed consequences and updating agent influence accordingly.
Implemented within a modular, multi-agent architecture, the system includes structured deliberation, an agreement matrix for mapping convergence and divergence, and an iterative consensus loop in which outputs are treated as provisional rather than final. A Wisdom Module performs a Reality Audit Loop that compares expected and actual outcomes across multiple temporal horizons, dynamically adjusting credibility based on calibration, predictive accuracy, and resistance to repeated error.
By distinguishing between consensus, operational, and validated states of truth, the system reduces short-term optimization bias, preserves dissent, and grounds intelligence in demonstrated alignment with reality. This approach transforms artificial intelligence from a static answer-generation system into a deliberative, self-correcting system accountable to consequence over time.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Walker, Joseph JM, "Deliberative Intelligence, Iterative Consensus, and Outcome-Grounded ‘Wisdom’ via Reality Backpropagation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9840