Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM

The introduction of the Logits-to-Logic framework marks a significant advancement in the field of artificial intelligence, particularly for Large Language Models (LLMs) that have struggled with logic consistency in structured knowledge reasoning tasks. Traditional methods have attempted to mitigate the challenges posed by Logic Drift, which arises from the representational differences between unstructured and structured knowledge. However, these existing approaches have largely failed to fundamentally resolve the issue, providing only input-level guidance. The Logits-to-Logic framework, proposed on November 12, 2025, aims to rectify this by strengthening and filtering logits during the autoregressive generation process. This innovative approach not only addresses the core issues of Logic Drift but also enhances the overall performance of LLMs, allowing them to achieve state-of-the-art results on various Knowledge Graph Question Answering benchmarks. The implications of this development…

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