LCNA is an early experimental neural-computing architecture. This page separates the results that have been established from the findings that remain under investigation, and summarizes the controls used to determine where the current evidence stops.
In the current bounded system, controlled experience can modify persistent learned state during active execution. That state can later influence computation without requiring a separate backpropagation or optimizer-driven retraining stage.
The current experiments are explicitly supervised: controlled teaching and contextual signals provide conditions under which local learning occurs.
Before learning, the tested cue produced a response margin of 0. After the controlled learning experience, the response margin was +12 across each of the three development seeds. The learned state persisted beyond the learning episode and remained available to later computation.
5 pre-learning probes and 10 post-learning probes were used per development seed.
The later response depended causally on retained learned state. Resetting that state removed the learned response. Restoring the previously learned state restored it — the sequence +12 → 0 → +12. The same pattern occurred across the tested development seeds.
Persistent learning depended on when the relevant learning signals occurred. A persistent modification was observed in the tested 250 ms condition, while the 1,500 ms and 6,000 ms conditions produced no comparable persistent modification.
This establishes temporal gating of the persistent write under the tested conditions — not memory for temporal order.
After learning, a previously unseen related input accessed the learned pathway under the tested conditions. A prospectively designed control with a physically disjoint route remained neutral. An earlier control that had initially been treated as unrelated also responded, but later analysis showed that it shared physical route overlap with the learned pathway. It therefore cannot serve as a clean unrelated control and is retained only as a descriptive diagnostic.
Together, these results support representation-mediated retrieval under the tested conditions, while the broader specificity of that retrieval remains unresolved.
Together, these interventions argue against explanations based only on cue exposure, instruction alone, elapsed time, isolated causal conditions, or a nonspecific response across the tested inputs. They support the conclusion that the later learned response depended on retained learned state and the tested learning conditions.
Two neutral-control requirements failed across all three development seeds. Those controls showed behavioral or internal-state changes that the protocol required to remain absent. Because of that, the latest complete retrieval-specificity protocol did not meet its full qualification criteria, and no held-out final qualification run was performed.
The failed controls narrow the retrieval claim boundary. They do not erase the earlier bounded persistent-learning, retained-state causality, or temporal write-gating results.
A frozen-runtime verification examined the current sealed execution path and directly tested the learning mechanism. Within the audited project path, no global or local backpropagation, autograd-based learning, optimizer-driven training, fitted decoder, or hidden trained-model pathway was found.
The learned-state change was also positively traced into later computation: local learning modified persistent state, and subsequent activity reused that retained state through the computational pathway.
The experiments remain supervised. Controlled teaching and contextual signals provide conditions for local learning, but they do not perform gradient-based parameter optimization.
Verification is bounded to the current sealed runtime and retains declared provenance and instrumentation limits in the technical audit record.
The linked research communication contains the experimental design, numerical results, controls, limitations, and evidence hierarchy in greater detail.