---
title: "AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for"
description: "The paper describes the AILS-NTUA entry to SemEval-2026 Task 10. 44 references, cited by 2 papers here."
authors: ["Panagiotis Spanakis", "Maria Lymperaiou", "Giorgos Filandrianos", "Athanasios Voulodimos", "Giorgos Stamou"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.161/"
doi: "10.18653/v1/2026.semeval-1.161"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/spanakis-2026-ails-ntua-semeval-2026-task/"
---
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# AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection

**Authors:** Panagiotis Spanakis, [Maria Lymperaiou](https://md.research.elamur.ai/free/en/authors/maria-lymperaiou/), [Giorgos Filandrianos](https://md.research.elamur.ai/free/en/authors/giorgos-filandrianos/), [Athanasios Voulodimos](https://md.research.elamur.ai/free/en/authors/athanasios-voulodimos/) and [Giorgos Stamou](https://md.research.elamur.ai/free/en/authors/giorgos-stamou/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.161/) · [PDF](https://aclanthology.org/2026.semeval-1.161.pdf) · [DOI 10.18653/v1/2026.semeval-1.161](https://doi.org/10.18653/v1/2026.semeval-1.161) · [OpenAlex](https://openalex.org/W7166838098)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/spanakis-2026-ails-ntua-semeval-2026-task/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper describes the AILS-NTUA entry to SemEval-2026 Task 10. The task has two parts on Reddit data: S1 asks systems to locate and label psycholinguistic conspiracy markers (ACTOR, ACTION, EFFECT, VICTIM, EVIDENCE), and S2 asks them to decide whether a comment endorses a conspiracy.

The authors use no fine-tuning. Instead, they build an agentic LLM pipeline that separates semantic reasoning from exact span placement. For S1, a Self-Refine loop chains a generator, a critic and a refiner, and the generator uses Dynamic Discriminative Chain-of-Thought (DD-CoT). DD-CoT requires a counterargument against a confusable label for each span. A deterministic matcher then converts the extracted strings into character offsets.

For S2, an "Anti-Echo Chamber" design targets the "Reporter Trap," where news reports or debunking texts get labeled as endorsement. It combines contrastive retrieval of hard negatives, a rule-based Forensic Profiler, a council of four opposing persona jurors and a Calibrated Judge.

On the dev set, the system roughly doubles S1 Macro F1 and lifts S2 by about half over a zero-shot baseline. This suggests structured deliberation can replace task-specific supervision while keeping outputs interpretable.

## Key points
- Dataset: 4,800 annotations covering 4,100 unique Reddit submission statements from more than 190 subreddits. The official dev set has 100 documents and the test set has 938.
- Results: S1 reaches 0.24 Macro F1 on dev (baseline 0.12) and 0.21 on test. S2 reaches 0.79 Macro F1, a 49% gain over baseline. The system ranked 3rd on the S1 dev leaderboard and 8th on test.
- Ablations on dev:
  - DD-CoT raises ACTOR F1 from 0.263 to 0.290.
  - The Parallel Council raises S2 F1 from 0.680 to 0.795.
  - Contrastive retrieval cuts the false-positive rate from 0.16 to 0.08.
- Cost: the full pipeline is about 2.9× (S1) and up to 7.6× (S2) more expensive in tokens than a single zero-shot prompt. Per-document latency rises from 10.5s to 30.2s for S1 and from 4.6s to 29.1s for S2.
- Models and open issues: the main backbone is the proprietary GPT-5.2, with transfer tested on the open-weights Qwen-3-8B. Irony, sarcasm and implicit stance remain the main failure cases. Code is published on GitHub (PsyChoMark_Semeval).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Panagiotis Spanakis, Maria Lymperaiou, Giorgos Filandrianos, Athanasios Voulodimos and Giorgos Stamou. 2026. AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{spanakis2026ails,
  title = {{AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection}},
  author = {Panagiotis Spanakis, Maria Lymperaiou, Giorgos Filandrianos, Athanasios Voulodimos and Giorgos Stamou},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.161},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.161}
}
```

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44 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*