# AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection

arXiv: https://arxiv.org/abs/2603.04921 · HTML: https://mlsanity.com/paper/2603.04921/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 66 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 17 of 17)

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- [An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web](https://mlsanity.com/paper/2008.09961/) (2020)
- [YouNICon: YouTube's CommuNIty of Conspiracy Videos](https://mlsanity.com/paper/2304.05274/) (2023)
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- [Hallucinated Span Detection with Multi-View Attention Features](https://mlsanity.com/paper/2504.04335/) (2025)
- [Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections](https://mlsanity.com/paper/2404.00141/) (2024)
- [Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models](https://mlsanity.com/paper/2404.17985/) (2024)
- [XML Prompting as Grammar-Constrained Interaction: Fixed-Point Semantics, Convergence Guarantees, and Human-AI Protocols](https://mlsanity.com/paper/2509.08182/) (2025)

## Abstract (arXiv)

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 these challenges. 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. Achieving 0.24 Macro F1 (+100% over baseline) on S1 and 0.79 Macro F1 (+49%) on S2, with the S1 system ranking 3rd on the development leaderboard, our approach establishes a versatile paradigm for interpretable, psycholinguistically-grounded NLP.