@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection
%A Spanakis, Panagiotis
%A Lymperaiou, Maria
%A Filandrianos, Giorgos
%A Voulodimos, Athanasios
%A Stamou, Giorgos
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F spanakis-etal-2026-ails
%X 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.
%U https://aclanthology.org/2026.semeval-1.161/
%P 1175-1204
Markdown (Informal)
[AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection](https://aclanthology.org/2026.semeval-1.161/) (Spanakis et al., SemEval 2026)
ACL