Thank you for the profound insight. I completely agree that the path to AGI lies in channel coding (robustness and synchronization) rather than just source coding (compression).In CSCT, we don't just "sample" data; we process it as a continuous Projected Dynamical System. Here is how we address your points:
Structured Temporal Oversampling: Our stream-based approach effectively performs high-density oversampling in the time domain. Instead of random sampling, the theta-phase (hippocampal rhythm) in our MultiGate architecture creates structured, overlapping "integration windows" to capture temporal context.
Phase Error Resolution: Phase errors are resolved not by averaging (as in L2 models), but by NMDA-gating. The gate only opens when the anchor velocity and theta-phase align, physically "locking" the signal to a specific codebook vertex. This is a computational implementation of theta-gamma coupling.
Supersaturated Subspaces: Our Simplex constraint (L1) naturally handles what you call "supersaturated subspaces" by enforcing non-negative competition. This ensures that even with overlapping temporal samples, the resulting internal representation remains discrete and grounded within the convex hull.
By treating cognition as a communication channel between an "Anchor" and "Codebook," we prioritize the stability of the compositional mapping over the mere efficiency of representation.
Structured Temporal Oversampling: Our stream-based approach effectively performs high-density oversampling in the time domain. Instead of random sampling, the theta-phase (hippocampal rhythm) in our MultiGate architecture creates structured, overlapping "integration windows" to capture temporal context.
Phase Error Resolution: Phase errors are resolved not by averaging (as in L2 models), but by NMDA-gating. The gate only opens when the anchor velocity and theta-phase align, physically "locking" the signal to a specific codebook vertex. This is a computational implementation of theta-gamma coupling.
Supersaturated Subspaces: Our Simplex constraint (L1) naturally handles what you call "supersaturated subspaces" by enforcing non-negative competition. This ensures that even with overlapping temporal samples, the resulting internal representation remains discrete and grounded within the convex hull.
By treating cognition as a communication channel between an "Anchor" and "Codebook," we prioritize the stability of the compositional mapping over the mere efficiency of representation.