This is the technical breakthrough that stands out across this work: It is a reverse (leaf → dependents) index in a reactive semantic graph/tree.
It delivers true O(k) incremental reactivity — when a value changes, only the specific dependency chain of length k is recomputed, independent of the total graph size n.
This makes large-scale reactive semantic data practical (measured constant computational effort even with thousands to tens of thousands of nodes, and negligible overhead for full derivation traces).
It directly enables the kernel’s explain() feature: returning the actual computation record (exact expression, literal inputs with origin/masking, dependsOn, recomputed, sourcePath) instead of a generated narrative. This is the structural solution to the problem of unreliable AI/self-report explanations that the original thesis discusses.
In short, Inverted Dependency Indexing is the engineering substrate that turns the philosophical/architectural claims of .me (schema-less reactive identity graphs, stealth scopes, deterministic local convergence, and verifiable “why did you say that?” answers) into a working, efficient, and checkable system.
His broader contributions include the overall .me / neurons.me stack (declarative identity, me:// Namespace Resolution Protocol, privacy-preserving semantic spaces, etc.), but the inverted dependency mechanism is the distinctive, high-leverage innovation that makes the rest scale and become auditable.
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