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ArgGYM introduces a procedural, engine-verified benchmark and RLVR-compatible training environment for structured defeasible reasoning, comprising twelve tasks, 1,440 verified instances, and symbolic argumentation engine-based scoring, with results showing frontier and open-weight models differ substantially in handling incomplete, revisable information.
This paper benchmarks the ability of Large Language Models (LLMs) to handle character attacks (ad hominem) in argumentative dialogues, revealing that LLMs prioritize logical defenses due to safety constraints, unlike human debaters.
This paper introduces a dataset and benchmark for evaluating large language models' understanding of competitive Chinese-language debate, featuring 148 matches with professional adjudication and tasks at match, stage, and speaker levels.
This position paper advocates computational argumentation as a formal foundation for Evaluative AI, which supports human decision-making by presenting competing hypotheses with evidence for and against, rather than single recommendations.
Introduces CANOE, a multi-agent neuro-symbolic framework for open-ended care plan coordination that uses argumentative computation and human-in-the-loop contestation to improve transparency, safety, and clinical correctness.
This paper presents a 17k-sentence corpus with annotations for argumentative passages across three German political arenas during COVID-19, and a pilot study on automatically identifying such passages, finding that boundaries are hard to pin down and models exhibit confirmation bias.
This paper proposes a framework that uses the Toulmin model of argumentation to structure ML-based retinal diagnosis from OCT images, integrating biomarker extraction, medical LLM reasoning (MedGemma), and similarity measures (MedSigLip) for interpretable and evidence-based diagnostic assistance.
This paper introduces a categorical framework for constructing verifiable, local truth-preserving foundation models using composable foundries, implemented in the Odyssey system, and scheduled for a tutorial at ICML 2026.