The paper investigates whether large language models (LLMs) follow Occam's Razor when performing inductive and abductive reasoning. It introduces a synthetic framework for generating questions that require both types of reasoning and a new automated metric to evaluate the simplicity and correctness of generated hypotheses. Experiments show that while LLMs can handle simple scenarios, they struggle with complex world models and producing high‑quality, simplest hypotheses, even when using advanced reasoning techniques.
By Yunxin Sun, Abulhair Saparov
OmouAI is an interactive deliberation system that combines large language models with computational argumentation to facilitate policy debates involving humans and simulated personas such as stakeholders, experts, or devil’s advocates. Each persona generates its own arguments, which are assembled into a shared argumentation framework that users can contest, add to, or revise, ensuring human oversight. The system evaluates arguments using deterministic argumentative semantics against external goals like the UN Sustainable Development Goals, providing faithful explanations and indicating how policy recommendations affect those goals.
By Stylianos Loukas Vasileiou, Antonio Rago, William Yeoh, Georgina Curto
arXiv:2603. 06114v2 Announce Type: replace-cross Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.
By Xuyao Feng, Anthony Hunter
arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).
By Marco Aruta, Francesco Improta, Vadim Malvone, Aniello Murano, Vladana Perlic
The paper introduces a meta‑study that reviews state‑of‑the‑art end‑to‑end argument mining (AM) pipelines. It proposes a triple‑perspective framework—linguistic, computational, and domain—to analyze how these pipelines model, compute, and incorporate domain knowledge into argument structures. The authors also outline a general design for the linguistic and computational aspects, aiming to standardize methodology descriptions and enable clearer comparisons among AM approaches.
By Siddharth Bhargava, Sara Tonelli, Patricia Mart\'in-Rodilla
arXiv:2608.30413v1 Announce Type: new
Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of n...
By Jayanta Sadhu, Sayem Shahad, Kenneth Marino