GoKawiilBlog post outlines method to embed boolean logic circuits into trained neural networks
A technical blog post describes an approach for attaching a 'sidecar' boolean circuit to an already-trained neural network. The author shows how AND, OR and NOT gates can be built as individual neurons using simple weighted sums and thresholds, then explains that connecting this circuit to a real network requires 'probes' that translate the network's real-valued activations into clean 0/1 signals and back again.
GoKawiil's interpretation of the reporting above, not reported fact.
The idea suggests a way to inject explicit, interpretable logical rules into otherwise opaque neural systems without retraining them from scratch, which could matter for AI interpretability and control research. Because the post is exploratory ('thinking out loud'), it reads as an early conceptual sketch rather than a tested or benchmarked technique, so its practical viability remains unproven.
- Boolean gates (AND, OR, NOT) can be expressed as single neurons using weighted sums and threshold activation.
- Attaching such a circuit to a trained network requires 'probe' neurons to convert real-valued activations into clean boolean signals.
- The post is a speculative, informal exploration rather than a validated or benchmarked method.
Source: edwardbenson.com — My Blog Is Human Authored. For Ai-Authored Writing, 2026-11-01
Published there as: “Embedding boolean circuits in a neural network”
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