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:2609.13808v1 Announce Type: new
Abstract: Structured knowledge fact checking aims to determine the truthfulness of natural language claims by reasoning over structured evidence. Recent program-...
By Yifei Li, Xiaohan Zheng, Wentao Qian, Liansheng Zhuang
arXiv:2609.39225v1 Announce Type: new
Abstract: Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. Whil...
By Siddharth Bhargava, Sara Tonelli, Patricia Mart\'in-Rodilla, Javier Parapar
arXiv:2606. 05704v1 Announce Type: cross Abstract: Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning mistakes, and unreliable reasoning results in complex mathematical reasoning problems.
By Muhammad Talha Sharif, Abdul Rehman
Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying...
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
Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning mistakes, and unreliable reasoning results in complex mathematical reasoning problems. In this study, we introduce a critic-based heterogeneous multi-agent approach to improve the dependability of mathematical reasoning.
arXiv:2608.29263v1 Announce Type: new
Abstract: Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While inte...
By Yuwei Lou, Hao Hu, Yuzhou Jiang, Zongfei Zhang, Liang Wang, Jincai Liu, Jidong Ge, Xianping Tao
The paper proposes a theory for judging post-hoc debates in AI, focusing on properties like reproducibility, robustness, groundedness, and explainability. It evaluates two debate‑judgement methods—LLM judges and formal computational argumentation semantics—finding similar accuracy but noting that argumentation semantics offers stronger formal guarantees. The study suggests that argumentation semantics is a preferable framework for principled debate judges in AI systems.
By Xiang Yin, Adam Dejl, Antonio Rago, Lihu Chen, Francesca Toni
arXiv:2607. 26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems.
By Quim Motger, Marc Oriol, Jordi Marco, Xavier Franch
arXiv:2607. 11307v1 Announce Type: new Abstract: Full-proof autoformalization bridges extensive mathematical proofs in natural language with formally validated reasoning, offering a pathway to elevate the ceiling of verifiable mathematical reasoning.
By Tian-Shuo Liu, Shiyuan Zhang, Zijie Geng, Haoyu Liu, Runjie Xu, Pengyuan Wang, Lei Yuan, Yang Yu
arXiv:2604.13706v2 Announce Type: replace
Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reas...
By Dhruv Sahnan, Subhabrata Dutta, Tanmoy Chakraborty, Preslav Nakov, Iryna Gurevych