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Self-Reflective Multi-modal Reasoning for Short-Video Fake News Detection — arXiv:2608.26787

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26787
License (as archived): CC0-1.0
Archived: 2026-10-17T18:55:15.707Z
SHA-256 of the archived text: a61b66ae100ce3946252180567dbab472abeabcf2f4b8f41ca08f47cacfb110b
Stable URL of this page: https://metraipolis.com/documento/dc_85b2e41386bc

How to cite

Self-Reflective Multi-modal Reasoning for Short-Video Fake News Detection — arXiv:2608.26787 — arXiv (en), https://arxiv.org/abs/2608.26787 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_85b2e41386bc (Metraipolis archive dc_85b2e41386bc, sha256 a61b66ae100ce3946252180567dbab472abeabcf2f4b8f41ca08f47cacfb110b)

BibTeX

@misc{dc85b2e41386bc,
  title = {Self-Reflective Multi-modal Reasoning for Short-Video Fake News Detection — arXiv:2608.26787},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26787},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_85b2e41386bc; sha256 a61b66ae100ce3946252180567dbab472abeabcf2f4b8f41ca08f47cacfb110b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26787. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent fake news detection pipelines increasingly leverage large language models and vision-language models for reasoning-based analysis. However, several challenges remain open: improving reasoning quality through self-reflection without ground-truth chain-of-thought supervision, using improved reasoning to benefit downstream model fine-tuning, and connecting single-sample fraudulent-pattern discovery with cross-sample verification. We propose SRM-FND, a self-reflective multimodal reasoning framework for short-video fake news detection. SRM-FND develops higher-quality reasoning through contrastive deliberation, iterative root-cause diagnosis, and corrective prompt refinement. A Blind Analyst, Counter-Conclusion Reasoner, and Self-Consistency Arbiter collaboratively identify and retain discriminative rationales. The framework also incorporates dual-phase, topic-adaptive vision-language model fine-tuning to improve multimodal grounding and enable lightweight topic specialization. For uncertain cases, it performs confidence-driven cross-sample review by retrieving credible and suspicious co-event examples. Experiments on FakeSV and FakeTT show that SRM-FND outperforms strong baselines, produces more reliable and interpretable predictions, and delivers noticeable improvements in cross-dataset performance.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.MM).
Categorie arXiv: Multimedia (cs.MM).
Pagina del lavoro: https://arxiv.org/abs/2608.26787 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T18:31:14.482Z dalla pagina https://arxiv.org/catchup/cs.MM/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).