A self-distilling neuro-symbolic cascade that amortises LLM cost across knowledge-graph QA and regulatory-compliance checking, with auditable Datalog proof trees.
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Updated
Jun 18, 2026 - Python
A self-distilling neuro-symbolic cascade that amortises LLM cost across knowledge-graph QA and regulatory-compliance checking, with auditable Datalog proof trees.
A verifier-anchored self-distilling neuro-symbolic cascade: a cheap, teacher-independent correctness verifier gates and corrects a frontier LLM before its answers distil into auditable, type-checked Datalog rules with proof trees.
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