@inproceedings{spanakis-etal-2026-ails,
    title = "{AILS}-{NTUA} at {S}em{E}val-2026 Task 10: Agentic {LLM}s for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection",
    author = "Spanakis, Panagiotis  and
      Lymperaiou, Maria  and
      Filandrianos, Giorgos  and
      Voulodimos, Athanasios  and
      Stamou, Giorgos",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.161/",
    pages = "1175--1204",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents a novel agentic LLM pipeline for SemEval-2026 Task 10 that jointly extracts psycholinguistic conspiracy markers and detects conspiracy endorsement. Unlike traditional classifiers that conflate semantic reasoning with structural localization, our decoupled design isolates and addresses these challenges separately. For marker extraction, we propose Dynamic Discriminative Chain-of-Thought (DD-CoT) with deterministic anchoring to resolve semantic ambiguity and character-level brittleness. For conspiracy detection, an ``Anti-Echo Chamber{``} architecture, consisting of an adversarial Parallel Council adjudicated by a Calibrated Judge, overcomes the ``Reporter Trap{``}, where models falsely penalize objective reporting. Our system achieves 0.24 Macro F1 (+100{\%} over baseline) on S1 and 0.79 Macro F1 (+49{\%}) on S2, ranking 3rd on the S1 development leaderboard and 8th on the test set, demonstrating that structured agentic deliberation is an effective alternative to fine-tuning for interpretable psycholinguistic NLP."
}