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CPUNeSy: Controlling Model Writes for Reliable Neuro-Symbolic Reasoning
LLMs excel at recalling statistical patterns but degrade sharply when answers must be derived, especially on multi-hop chains. Delegating derivation to deterministic symbolic executors shifts reliability to whether model-generated premises are source-supported. We introduce CPUNeSy, a serving archit
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