I’ve been developing a theoretical architecture for persistent AI agent memory and I’m looking for critical feedback before investing further in implementation. I’m an independent researcher without formal credentials, so I’m especially interested in hearing what I’m missing.

The core claim:
Long-context windows solve capacity but not structure. For deployed agents operating over long time horizons, I’m proposing that memory needs geometric organization—not just more tokens.

The architecture (Geometric Mnemic Manifold):
    •    Externalize transformer KV cache to distributed storage
    •    Address memory locations analytically via Kronecker sequences (O(1) coordinate calculation, low-discrepancy coverage on the hypersphere)
    •    Hierarchical abstraction: L0 (raw states) → L1 (summarized patterns) → L2 (abstract axioms)
    •    Entropy-gated instantiation: only allocate compute when uncertainty exceeds threshold
    •    Polynomial temporal decay: preserves heavy-tailed retrieval of old-but-relevant information

What I think this buys you:
    •    Deterministic retrieval paths (auditability for regulated domains)
    •    No index loading (the “index” is an equation)
    •    Configurable forgetting curves per domain
    •    Potential for multi-agent composition via shared address space

What’s proven vs. conjectured:
Proven: The mathematical properties—Kronecker sequences provide uniform coverage, coordinate calculation is O(1), hierarchical structure gives bounded active edges.

Conjectured: That these properties translate to practical advantages in auditability, efficiency, and compositionality.

Unvalidated: Whether any of this works. I haven’t built it.

The critical validation gate:
The architecture assumes a small “Recursive Reasoning Kernel” can learn to detect epistemic gaps, recognizing when context is insufficient and signaling for retrieval rather than hallucinating. If small models can’t learn this reliably, the architecture collapses to standard RAG with extra complexity.

I’ve designed a Phase 0 experiment ...

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