# TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation

arXiv: https://arxiv.org/abs/2608.27127 · HTML: https://mlsanity.com/paper/2608.27127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 20 of 20)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://mlsanity.com/paper/2010.05700/) (2020)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [MemeCap: A Dataset for Captioning and Interpreting Memes](https://mlsanity.com/paper/2305.13703/) (2023)
- [Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/2105.14462/) (2021)
- [Imagination improves Multimodal Translation](https://mlsanity.com/paper/1705.04350/) (2017)
- [An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance](https://mlsanity.com/paper/2404.01247/) (2024)
- [Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting](https://mlsanity.com/paper/2005.03119/) (2020)
- [Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models](https://mlsanity.com/paper/2109.03415/) (2021)
- [Image Pivoting for Learning Multilingual Multimodal Representations](https://mlsanity.com/paper/1707.07601/) (2017)
- [On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing](https://mlsanity.com/paper/2412.17242/) (2024)
- [MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation](https://mlsanity.com/paper/2403.14652/) (2024)
- [memeBot: Towards Automatic Image Meme Generation](https://mlsanity.com/paper/2004.14571/) (2020)
- [TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors](https://mlsanity.com/paper/2503.08708/) (2025)
- [GasAgent: A Multi-Agent Framework for Automated Gas Optimization in Smart Contracts](https://mlsanity.com/paper/2507.15761/) (2025)
- [CHASM: Unveiling Covert Advertisements on Chinese Social Media](https://mlsanity.com/paper/2604.20511/) (2026)
- [What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media](https://mlsanity.com/paper/2606.06784/) (2026)
- [Behind EvoMap: Characterizing a Self-Evolving Agent-to-Agent Collaboration Network](https://mlsanity.com/paper/2605.25815/) (2026)

## Abstract (arXiv)

Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes across cultures and languages is a central challenge for enabling mutual understanding in online communication. Unlike ordinary translation or standalone text rewriting, cross-cultural meme transcreation must jointly preserve communicative intent, adapt culture-dependent meaning for the target audience, and maintain coherence between text and image. In this work, we first provide an explicit task analysis of cross-cultural meme transcreation and identify three core challenges: culture-specific knowledge understanding, intent and tone preservation, and multimodal consistency. Based on this analysis, we propose a multi-agent framework with specialized agents that are coordinated to address these challenges through cultural adaptation, target text rewriting, revision, and conditional visual adjustment. The framework strengthens target text adaptation with coordinated feedback to handle difficult cases that require deeper cultural or visual intervention. We evaluate the framework on bidirectional Chinese-English meme transcreation using both human evaluation and LLM-as-a-Judge. Our method consistently outperforms all baselines across both evaluation settings. In human evaluation, it achieves the best performance on all four dimensions and delivers a 33.1% average improvement over the strongest baseline, while in LLM-as-a-Judge, it attains the highest Top-1 ranking rate (60% versus 26% for the second-best baseline). Further analysis indicates that each component contributes to the performance. Our error analysis suggests that the remaining bottlenecks lie in humor reconstruction and image-text alignment rather than simple cultural knowledge gaps, pointing to future work on humor transfer.