This paper derives a five-variable coupled continuous-time dynamical extension of the Learning System Stability Model (LSSM). The system tracks labile traces (T), prior knowledge (E), relevance (r), synaptic consolidation (Sₛyn), and systems consolidation (Sₛys). Eight structural corrections are incorporated, including mass conservation, logistic knowledge growth bounded by capacity ceiling Eₘax, normalised selection function sigma in 0, 1, and saturated relevance dynamics. The corrected system is well-posed, admits bistability consistent with the original LSSM saddle-node structure, and preserves the stability constraint L ≤ E·Sₛys² as a slow-timescale condition. Reference Python implementation provided separately.
Omri Bankuti (Fri,) studied this question.