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Abstract <p>This paper presents a new multiagent architecture with probabilistic data aggregation for detecting fake messages in text, images, and audio sources. The development of such approaches is increasingly in demand for a variety of information systems due to the growing volume of heterogeneous content, the limitations of single models in credibility assessment, and the lack of sufficiently large labeled datasets in real-world conditions that would allow the effective detection of unreliable content, which leads to the need to use probabilistic approaches. The fundamental novelty of the solution presented in this work lies in the probabilistic aggregation of agent inferences with confidence calibration and interpretable justifications. The architecture includes an intelligent router, modality-specific agents, and a context-weighted aggregation module. To evaluate the quality of the proposed architecture and compare it with various large language models, a custom graph dataset of real and synthetic messages with various combinations of modalities was created. In the experimental series, the aggregator outperformed single text (Mistral-7B-Instruct-v0.3, Phi-4, etc.) and vision language models (for example, LLaVa‑v1.5‑7B and Qwen2‑vl‑2B‑Instruct, etc.): the values of the accuracy metrics (Accuracy) and F<sub>1</sub><b>-</b>score of the proposed architecture are 10–17% higher; the Weighted F<sub>1</sub>‑score values improved by 16%; the values of the Brier score and log loss metrics, reflecting the calibration of probabilistic predictions and the severity of penalty for overconfident errors, were reduced by 13–30%. The system operates in zero-shot mode based on open pretrained models, including YandexGPT‑5‑Lite‑8B‑Instruct, Qwen3‑8B and those vision language models mentioned above, and supports fast domain adaptation using system prompts and standardized output formats. The results demonstrate the effectiveness of the multiagent approach for reliable assessment of the credibility of multimodal messages and its applicability to intelligent content quality control tools in modern general-purpose information systems, including social networks.</p>

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A Multiagent Multimodal Neural Architecture with Probabilistic Evidence Aggregation for Detecting Fake Messages

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • A. K. Gorshenin,
  • M. A. Fedorov

摘要

Abstract

This paper presents a new multiagent architecture with probabilistic data aggregation for detecting fake messages in text, images, and audio sources. The development of such approaches is increasingly in demand for a variety of information systems due to the growing volume of heterogeneous content, the limitations of single models in credibility assessment, and the lack of sufficiently large labeled datasets in real-world conditions that would allow the effective detection of unreliable content, which leads to the need to use probabilistic approaches. The fundamental novelty of the solution presented in this work lies in the probabilistic aggregation of agent inferences with confidence calibration and interpretable justifications. The architecture includes an intelligent router, modality-specific agents, and a context-weighted aggregation module. To evaluate the quality of the proposed architecture and compare it with various large language models, a custom graph dataset of real and synthetic messages with various combinations of modalities was created. In the experimental series, the aggregator outperformed single text (Mistral-7B-Instruct-v0.3, Phi-4, etc.) and vision language models (for example, LLaVa‑v1.5‑7B and Qwen2‑vl‑2B‑Instruct, etc.): the values of the accuracy metrics (Accuracy) and F1-score of the proposed architecture are 10–17% higher; the Weighted F1‑score values improved by 16%; the values of the Brier score and log loss metrics, reflecting the calibration of probabilistic predictions and the severity of penalty for overconfident errors, were reduced by 13–30%. The system operates in zero-shot mode based on open pretrained models, including YandexGPT‑5‑Lite‑8B‑Instruct, Qwen3‑8B and those vision language models mentioned above, and supports fast domain adaptation using system prompts and standardized output formats. The results demonstrate the effectiveness of the multiagent approach for reliable assessment of the credibility of multimodal messages and its applicability to intelligent content quality control tools in modern general-purpose information systems, including social networks.