@inproceedings{abdaljalil-etal-2025-theorem,
    title = "Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models",
    author = "Abdaljalil, Samir  and
      Kurban, Hasan  and
      Qaraqe, Khalid  and
      Serpedin, Erchin",
    editor = "Zhang, Yuji  and
      Chen, Canyu  and
      Li, Sha  and
      Geva, Mor  and
      Han, Chi  and
      Wang, Xiaozhi  and
      Feng, Shangbin  and
      Gao, Silin  and
      Augenstein, Isabelle  and
      Bansal, Mohit  and
      Li, Manling  and
      Ji, Heng",
    booktitle = "Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models (KnowFM)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.knowllm-1.10/",
    pages = "111--119",
    ISBN = "979-8-89176-283-1",
    abstract = "Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret. Prompting techniques like Chain-of-Thought (CoT) enhance reliability by eliciting intermediate reasoning steps or aggregating multiple outputs. However, they lack mechanisms for enforcing logical structure and assessing internal coherence. We introduce Theorem-of-Thought (ToTh), a novel framework that models reasoning as collaboration among three parallel agents, each simulating a distinct mode of inference: abductive, deductive, and inductive. Each agent produces a reasoning trace, which is structured into a formal reasoning graph. To evaluate consistency, we apply Bayesian belief propagation guided by natural language inference (NLI), assigning confidence scores to each step. The most coherent graph is selected to derive the final answer. Experiments on symbolic (WebOfLies) and numerical (MultiArith) reasoning benchmarks show that ToTh consistently outperforms CoT, Self-Consistency, and CoT-Decoding across multiple LLMs, while producing interpretable and logically grounded reasoning chains. Our findings suggest a promising direction for building more robust and cognitively inspired LLM reasoning. The implementation is available at https://github.com/KurbanIntelligenceLab/theorem-of-thought."
}