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Unified Hallucination Fuzzing for Multimodal Large Language Models

Source
Pengfei Zhou, Jiajun Song, Zhiwei Tang, Yixing Ma, Xiaopeng Peng, Donghui Si, Yuhang Xu, Huiqi Song, Yiyuan Miao, Yichen Qian, Weihua Chen, Wangbo Zhao, Bohan Zhuang, Jiasheng Tang, Yang You
Author
Pengfei Zhou, Jiajun Song, Zhiwei Tang, Yixing Ma, Xiaopeng Peng, Donghui Si, Yuhang Xu, Huiqi Song, Yiyuan Miao, Yichen Qian, Weihua Chen, Wangbo Zhao, Bohan Zhuang, Jiasheng Tang, Yang You
Date
Terms in this piece · Glossary
  • hallucination — When a model states something false with full confidence — inventing facts, citations, or APIs that don't exist.
  • multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
  • benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
  • agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
Why it matters

Anyone relying on static benchmarks gets evidence those scores overstate robustness, plus an open fuzzing for stress-testing their own stack.

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