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Unified Hallucination Fuzzing for Multimodal Large Language Models
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduced a unified hallucination fuzzing framework for multimodal LLMs to address static benchmark saturation in safety testing.
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Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 6 November 2026
- Collected by OneBench
- 11 Aug 2026, 15:58 UK
- Original headline
- Unified Hallucination Fuzzing for Multimodal Large Language Models ↗
Stored source excerpt
arXiv:2608.07525v1 Announce Type: new Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applications. Existing evaluations,…
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