@inproceedings{you-etal-2025-probabilistic,
    title = "Probabilistic Soundness Guarantees in {LLM} Reasoning Chains",
    author = "You, Weiqiu  and
      Xue, Anton  and
      Havaldar, Shreya  and
      Rao, Delip  and
      Jin, Helen  and
      Callison-Burch, Chris  and
      Wong, Eric",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.382/",
    pages = "7517--7536",
    ISBN = "979-8-89176-332-6",
    abstract = "In reasoning chains generated by large language models (LLMs), initial errors often propagate and undermine the reliability of the final conclusion. Current LLM-based error detection methods often fail to detect propagated errors because earlier errors can corrupt judgments of downstream reasoning. To better detect such errors, we introduce Autoregressive Reasoning Entailment Stability (ARES), a probabilistic framework that evaluates each reasoning step based solely on previously-verified premises. This inductive method yields a nuanced score for each step and provides certified statistical guarantees of its soundness, rather than a brittle binary label. ARES achieves state-of-the-art performance across four benchmarks (72.1{\%} Macro-F1, +8.2 points) and demonstrates superior robustness on very long synthetic reasoning chains, where it excels at detecting propagated errors (90.3{\%} F1, +27.6 points)."
}