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New framework SyRHM enhances harmful meme detection with symbolic reasoning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SyRHM, a new framework designed to improve the detection of harmful memes. This system decomposes the detection process into two main stages: meaning-grounded retrieval and symbolic-language-enhanced multi-stage reasoning. SyRHM retrieves relevant memes by analyzing multimodal content and converts inputs into symbolic representations for interpretable analysis. Experiments on benchmark datasets like FHM, HarM, and MultiOff show SyRHM outperforms existing multimodal and reasoning-based methods. AI

IMPACT This framework could lead to more effective tools for content moderation and online safety.

RANK_REASON The cluster contains a research paper detailing a new framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework SyRHM enhances harmful meme detection with symbolic reasoning

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The cluster contains a research paper detailing a new framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hanling Wang, Chenlong Wei, Yingjuan Li, Di Wu, Yuchao Zhang, Xiaohui Zhu, Yao Zhu ·

    SyRHM: Symbolic-Language-Enhanced Reasoning with Associative Retrieval for Zero-shot Harmful Meme Detection

    arXiv:2609.13794v1 Announce Type: new Abstract: Detecting harmful memes is critical for maintaining safe online communities. However, harmful intent is often implicit, arising from visual-textual incongruity and cultural stereotypes, which challenges existing multimodal detectors…

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