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Jointly generating inline argument and entity tags beats sequential extraction pipelines by over 40% relative F1, revealing that argumentative structure cannot be accurately decoupled from the specific real-world entities being debated.
LLMs can now engage in transparent, verifiable reasoning about debates by fusing argument mining with fuzzy description logics, moving beyond black-box statistical analysis.