Abstract
A system and method is needed to mitigate potential alignment erosion in stateful generative models where continuous learning could alter pre-trained safety priors. The technology can introduce a parallel control architecture that may operate alongside a host model to help decouple learning capabilities from alignment constraints. The system can, for example, employ a dual intervention using a runtime residual correction interface to steer immediate output away from undesirable content, and an orthogonal memory projection interface to sanitize signals before they can be written to the model's persistent memory. By mathematically projecting undesirable components out of the memory-write signals, the system may structurally inhibit the model from encoding or remembering undesirable patterns, potentially helping to maintain safety integrity over long-term interactions.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mohbe, Neel, "Orthogonal Memory Projection for Alignment in Stateful Generative Models", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9555