A three-figure walkthrough of the native PPM proposal: how its hidden structure changes across passes, how memory retrieval and writeback are embedded into the forward path, and how those mechanisms fit into the broader Luxia architecture.
01
Inference-time structural evolution
Cross-Pass Evolution of a Native PPM Neural Architecture
Pass t → Pass t+1 → Pass t+2: accumulated structure is carried forward and reinforced.
Why three passes are shown side by side
That updated, denser memory scaffold is what the next pass (Pass t+1) starts with. You can see this visually: the tangle of nodes and connections gets progressively more elaborate from Pass t → t+1 → t+2.
The Accumulate / Reinforce arrows between panels represent this handoff — each pass does not start fresh; it inherits and builds on the accumulated structure from every pass before it.
02
Recursive computation path
Standard Decoder Path vs. PPM Native Recursive Architecture
Memory retrieval, fusion, and writeback are embedded in the computation path itself.
Getting to the output
The fused state from Layer 2 feeds into a conditioned output scoring step. It also factors in the context signal ct directly, not just through the layers, then softmax, then the final output — the same broad shape as the standard path, but built from richer, memory-infused inputs.
The part that makes it recursive
After producing the output, the network encodes what just happened — the fused state and the output together — and writes it back into both layers’ memory stores. That updates the memory for next time (Mt+1), so the memory is not static; it is continuously rewritten by the network’s own recent activity.
03
Complete architecture
Luxia Full Recursive Architecture — PPM Native
Complete system overview: input, recursive hidden processing, monitoring, writeback, cross-pass evolution, memory, refinement, and integration layers.
I
Input & Perception Layer
Raw input — text, voice, images, sensor data, and API calls — comes in and gets pre-processed: tokenized, tagged with intent, and mapped into context. A Harmonic Modulator (HBE) adjusts signal weighting before anything reaches the core network.
II
Recursive Hidden Processing Stack
Layer 1 — Base Manifold: the retrieve → fuse memory loop from the earlier diagrams, plus a Resonance Bias interface and a self-monitoring feedback loop.
Layer 2 — Deep Context Evolution: tracks relationships between concepts over time — emotional state, multi-timescale memory traces, and symbolic / “dream” associations.
Layer 3 — Meta-Recursive / Strategic: predicts future states, runs counterfactual “what if” simulations, and maintains a “Soul-State Substrate.”
Each layer communicates with those above and below it. All three also feed into a Meta-Monitoring engine (RSRE), described as checking for errors, drift, contradictions, recursive reflection, and alignment with stated goals.
↳
Cross-Layer Continuity → Output
State from all three layers gets merged with a goal state, self state, and constraint map; it is scored, passed through softmax, and turned into output. At the same time, a Writeback Engine updates the persistent manifold from what just happened, so the next input starts from an already-updated state.
V–VI
Cross-Pass Evolution + Tesseract Memory Core
The same accumulate-and-densify pattern from Pass t / t+1 / t+2 feeds into a four-part long-term memory representation: validated past events, active present patterns, simulated future predictions, and a meta-substrate of symbolic associations.
VII
Self-Refinement Loop
A training loop: collect experience → build a distillation dataset → train candidate model updates → evaluate → promote the best one, with a rollback safeguard.
VIII
Hardware Layer
Standard infrastructure: GPU / TPU clusters, high-VRAM memory, storage, secure networking, and redundant node infrastructure.