@inproceedings{hammerla-mehler-2026-negation,
    title = "Negation in Reasoning Traces: Interpretable Signals of Correctness and Provenance",
    author = "Hammerla, Leon Lukas  and
      Mehler, Alexander",
    editor = "Yanaka, Hitomi  and
      Abzianidze, Lasha",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Meets Logic and Machine Learning ({NALOMA})",
    month = aug,
    year = "2026",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-naloma/2026.naloma-1.4/",
    pages = "19--39",
    ISBN = "979-8-89176-389-0",
    abstract = "Chain-of-thought (CoT) reasoning is widely used in large language models (LLMs), but the resulting reasoning traces remain underexplored.We study these traces through the lens of discourse-level negation.Specifically, we distinguish between corrective negation, which rejects a prior reasoning step, and refining negation, which narrows or qualifies it, and introduce metrics to quantify their use in human- and LLM-authored reasoning traces.Across multiple benchmarks, we find that negation occurs much more frequently in intermediate reasoning traces than in final response texts.We then test whether negation-based features provide predictive and descriptive signal for correctness, model identity, and human-vs.-LLM authorship.For correctness prediction, negation-based features consistently outperform simple structural baselines and in several settings add complementary signal to embedding-based representations, although embeddings remain stronger overall.In a controlled comparison on correct human and LLM traces from the same dataset, our strongest results arise in human-vs.-LLM classification, where negation features outperform both structural and embedding baselines.Overall, these findings position discourse-level negation as an interpretable feature for reasoning-trace analysis, with especially strong utility for provenance-related classification and modest but consistent value for correctness prediction."
}