Human–Agent Joint Diagnosis with ValidLLP4LLM: A Labeled Logic Framework for Validating LLM Reasoning

Large language models (LLMs) can generate fluent diagnostic explanations that appear coherent while containing unsupported claims, omitted premises, or logically invalid inferences. Such hallucinations are especially dangerous in human–machine collaborative environments, where LLM outputs may influence clinical judgment, escalation decisions, and trust calibration. This paper introduces ValidLLP4LLM, a hallucination detection and reasoning-validation framework based on a Labeled Logic Program architecture for physician–LLM joint diagnosis. The framework integrates several complementary reasoning paradigms, including logic programming, argumentation, probabilistic inference, and abductive explanation. Within this architecture, abduction plays a central role: it generates candidate explanatory hypotheses for observed symptoms or claims, while the LLP layer tests whether these hypotheses are supported by evidence, consistent with domain constraints, resistant to counterarguments, and clinically admissible. Unlike single-formalism validators, ValidLLP4LLM enriches symbolic facts and rules with semantic, epistemic, temporal, provenance, defeasibility, and discourse labels. This allows the system to represent not only what is asserted, but also who asserted it, how strongly it is supported, when it applies, whether it is defeasible, and whether it occurs in a central or peripheral part of the diagnostic discourse. By incorporating discourse-aware weighting, the framework gives greater verification priority to nucleus claims while attenuating satellite or contextual statements. Experiments on benchmark hallucination-detection datasets show that LLP-based validation improves over classical symbolic validators, especially when combined with discourse modeling, while results on a challenging clinical narrative dataset highlight the difficulty of verifying implicit, ambiguous, and underspecified patient language. A human evaluation further indicates that logic-assisted explanations improve hallucination detection accuracy, interpretability, and user trust. Overall, the results suggest that discourse-aware labeled symbolic reasoning provides a robust and interpretable foundation for validating LLM reasoning in safety-critical human–AI diagnostic workflows.