Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction

• URL: http://arxiv.org/abs/2605.27706v1
• Date: Tue, 26 May 2026 21:28:51 GMT
• Title: Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction
• Abstract summary: CAROL is a probabilistic framework for test-time hallucination reduction in large language models.<n>CAROL defines a semantic uncertainty measure based on the consistency between generated responses and a trusted context.<n>CAROL unifies hallucination detection and mitigation within a single framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce CAROL (Chain-based Adaptive Reconfiguration Over Lattices), a probabilistic framework for test-time hallucination reduction in large language models. Rather than relying on token-level uncertainty, CAROL defines a semantic uncertainty measure based on the consistency between generated responses and a trusted context, inducing a string-submodular objective over a lattice of textual sequences. This formulation enables hallucination mitigation to be cast as a Markov chain accept-reject process with provable convergence and near-optimality guarantees, allowing the model to iteratively refine outputs toward semantic consistency. By operating at the level of meaning, CAROL unifies hallucination detection and mitigation within a single framework. Empirical results on question answering and multi-agent reasoning benchmarks show that CAROL significantly reduces hallucinations and improves reliability and interpretability compared to likelihood-based and retrieval-augmented baselines, while maintaining competitive computational efficiency.

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