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Current LLMs struggle with compositional inference because they lack physical boundaries. CSCT implements a neurological multi-gate mechanism (Na⁺/θ/NMDA) to enforce L1 geometry and physical grounding. In my experiments (EX8/9), this architecture achieved 96.7% success in compositional inference within the convex hull—far outperforming unconstrained models.Key features:Stream-based: No batching or static context; it processes information as a continuous flow.Neurological Gating: Computational implementation of θ-γ coupling using Na⁺ and NMDA-inspired gates.Zero-shot Reasoning: Incurs no "hallucination" for in-hull compositions.Detailed technical write-up: [https://dev.to/csctnail/-a-new-ai-architecture-without-prior...]I’m eager to hear your thoughts on this "Projected Dynamical System" approach to cognition.

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Interesting! Been building space-time coordinate system for AI models. Notionally agree in principle w.r.t. convex hull, clocks, etc. since we invoke similar machinery albeit in tokenized models. Need read this work more deeply to grok.

One question is to what extent you dig into or have considered oversampling? One of the core hypotheses we've converged on is that nearly all models are optimized for source coding vs. channel coding. The implication is path to AGI likely involves oversampling to capture channel coding gains and which will resolve phase errors, etc.

Random sampling naturally does this albeit inefficiently. Curious if you do something more structured than random in terms of oversampling and especially partial overlapped samples / think supersaturated subspaces / subchannels, etc.

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.

ya, brain just noisy channel in same way we can treat LLMs; anything possible exists we are just sampling it, which distills to "mere" clock syncing

L1 & L2 constraints unwind that clock compression with suitable dilation; very easy to think only efficiency matters and not averaged out replicas; nature does that inherently via primes, we have to create those artificial waves, recreate that convex hull, etc.

all to say, great to see more work in this direction & perhaps we can compare notes sometime!

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