I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

The paper introduces Query-Retrieve-Conclude, a zero-shot framework designed to enhance multimodal meme understanding and detection by acquiring up-to-date open-web knowledge. Existing methods often fail with emerging memes due to reliance on outdated or incomplete parametric knowledge. This framework identifies missing context, retrieves external evidence, and synthesizes evidence-grounded background knowledge. It was evaluated on three meme understanding datasets, including a new KYM benchmark of recent memes from 2024–2026, and five meme detection tasks. Experiments show Query-Retrieve-Conclude significantly improves knowledge recovery (e.g., +32% recall on KYM with Qwen3) and downstream detection performance, achieving a 0.71 F1 score with Gemma3-12B, outperforming zero-shot baselines and agent-based methods.

Key takeaway

For AI Scientists and Machine Learning Engineers developing robust multimodal systems, you should integrate explicit open-world knowledge acquisition to handle dynamic content like internet memes. Relying solely on parametric knowledge for emerging cultural references or events leads to significant performance degradation. Implement a structured query-retrieve-conclude pipeline to identify knowledge gaps, fetch real-time evidence, and ground your models' interpretations, improving both understanding and detection accuracy, especially for nuanced tasks like sarcasm or misogyny detection.

Key insights

Meme understanding requires dynamic, open-world knowledge acquisition beyond static model parameters.

Principles

• Explicitly identify knowledge gaps before retrieval.
• Ground answers solely in retrieved external evidence.
• Synthesize QA pairs into explicit background statements.

Method

The Query-Retrieve-Conclude framework involves three stages: Query (identifies missing knowledge via reverse image search, caption/question generation), Retrieve (acquires open-web evidence for questions), and Conclude (synthesizes QA pairs into explicit background knowledge statements for tasks).

In practice

• Use reverse image search to identify visual context.
• Formulate search-oriented questions for knowledge gaps.
• Employ external web search for time-sensitive information.

Topics

• Meme Understanding
• Open-World Knowledge
• Zero-Shot Frameworks
• Multimodal AI
• Information Retrieval
• Meme Detection

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• I Know What You Meme, Even If it Emerged Today: Understanding Evolving Memes through Open-World Knowledge Acquisition — Dynamic meme understanding requires open-world knowledge acquisition to overcome outdated parametric model knowledge.
• Memes-as-Replies: Can Models Select Humorous Manga Panel Responses? — LLMs show promise in understanding social cues for humor, but struggle with visual information and subtle wit in meme reply selection.
• M$^3$Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions — Realistic user-agent interactions demand multimodal memory benchmarks that evaluate cross-modal grounding and implicit inference.
• I built a public voting benchmark where models have to make memes out of daily news — Memebench evaluates LLM humor and creativity by generating memes from daily news for public A/B voting.
• Learning to Think Like a Cartoon Captionist: Incongruity-Resolution Supervision for Multimodal Humor Understanding — Explicitly supervising reasoning structure, not just scale, improves multimodal humor understanding.
• From Visual Question Answering to multimodal learning: an interview with Aishwarya Agrawal — VQA research has evolved from basic perception to complex multimodal challenges like cultural alignment and embodied AI.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.