When Logic Meets Language: The Rise of High‑Assurance LLMs
Large language models can craft elegant arguments—but can they prove them? In law, medicine, and finance, a wrong conclusion isn’t just a hallucination; it’s a liability. The paper LOGicalThought (LogT) from USC and UT Dallas takes aim at this problem, proposing a neurosymbolic framework that lets LLMs reason with the rigor of formal logic while retaining their linguistic flexibility. From Chain-of-Thought to Chain-of-Trust Typical prompting strategies—Chain-of-Thought (CoT), Program-Aided Language Models (PAL), or self-critique loops—focus on improving reasoning coherence. Yet none of them guarantee faithfulness. A model can still reason eloquently toward a wrong or unverifiable conclusion. LogT reframes the task: it grounds the reasoning itself in a dual context—one symbolic, one logical—so that every inference step can be traced, validated, or challenged. ...