Plurai's SCOPE compiles natural-language agent policies into neurosymbolic predicate programs
Plurai · hf · 2026-10-23
Plurai researchers Arnon Mazza and Elad Levi introduce SCOPE, which compiles compound LLM-agent policies into optimized neurosymbolic predicate programs.
- Context: policy enforcement has shifted from single-turn moderation to compound, application-specific policies evaluated over multi-turn transcripts and tool-call trajectories. Fine-tuned guardrails are efficient but degrade on compound policies; LLM-as-a-judge is accurate but costly and unstable.
- Approach: decomposes policies into narrow neural decisions under explicit logic, with the symbolic layer optimized against the policy objective rather than fixed data.
- Result: high accuracy, stability, and interpretability even on inexpensive models, at low inference cost.
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