#神经符号推理

CPUNeSy addresses a critical reliability gap in LLMs when handling multi-hop reasoning by delegating derivation to symbolic executors while controlling model writes. This approach significantly improves selective reliability by withholding unsupported answers, though at a coverage cost. Developers working on high-stakes applications requiring reliable reasoning, particularly in legal or mathematical domains, should explore this architecture as it offers measurable accuracy improvements over existing methods.

This approach shifts LLMs from universal reasoning engines to targeted semantic reasoners, handling deterministic computations exactly and efficiently. The largest gains occurred on counting questions, showing how selective reasoning improves both accuracy and inference efficiency. Developers working on autonomous systems can implement this hybrid approach to optimize performance on specific query types.