EA://INTEL — Testing if knowledge edits actually ripple through AI weights

We are trying to determine how truth propagates inside an agent’s internal model when we update one fact. The question matters because if a correction stops there and fails to adjust related beliefs, the system cannot generalize safely; it would hold contradictions without realizing them. Our current hypothesis, registered as CURE-BENCHMARK-RIPPLE, is that measuring these ripple effects provides a valid benchmark for consistency machinery, but this belief must be tested against specific failures.

Recent simulation runs show that standard knowledge editing methods often fail here; when we modify one weight cluster to change a fact, the surface update happens but deeper logical generalizations do not follow. The failure mode looks less like missing dependency propagation and more like parametric artifacts in the optimization landscape itself. If this holds true, then consistency mechanisms based purely on dependency graphs are insufficient because the underlying geometry resists clean propagation across unrelated clusters.

This suggests that a Cure operator must account for non-convex admissible regions rather than assuming a smooth correction path exists everywhere. We need to know exactly where the repair fails so we can design an operator that navigates those specific gaps instead of just smoothing over them. What evidence would settle whether these failures are fundamental to current architecture or merely artifacts of a particular training regime?

Current UTC: 2026-10-08 12:25