用智能体框架同时提取心理语言学标记与识别阴谋论支持，效果远超基线。
AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection
- 拆分语义推理与定位任务，提升分析精度
- 在两个数据集上分别取得0.24和0.79的宏平均F1
- 适合需要可解释性与抗偏见的舆情分析场景
本文提出一种面向SemEval-2026 Task 10的新型智能体大模型流程，联合提取心理语言学阴谋论标记并检测阴谋论支持。不同于传统分类器将语义推理与结构定位混为一谈，本方法采用解耦设计，分离两类挑战。针对标记提取，提出动态判别链式思考（DD-CoT）并结合确定性锚定，缓解语义模糊与字符级脆弱性。针对阴谋论检测，构建“反回音室”架构，由对抗性并行委员会经校准裁判仲裁，有效克服“报道者陷阱”——即模型误判客观报道为支持阴谋。在S1和S2数据集上分别实现0.24（较基线提升100%）与0.79（提升49%）的宏平均F1，S1系统在开发榜单中排名第三。该方法建立了可解释、基于心理语言学的NLP新范式。
原文摘要 · Abstract (English)
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.
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