“This study reinforces what Anh Totti Nguyen has been saying for a long time, in a series of underappreciated papers like Vision Language Models are Blind that I keep trying to draw attention to.”
Our reading, the sentence that was checked:
The new Stanford study reinforces what Anh Totti Nguyen has long been saying in a series of underappreciated papers, such as 'Vision Language Models are Blind'.
Assessment: Supported Evidence as of
Where and when Gary Marcus wrote it
Marcus on AI, The mirage of visual understanding in current frontier models, paragraph 4.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment marcus-the-mirage-of-visual-understanding-a52bfa-a1
We recorded 5 claims from this post. See every claim from this post.
The wording above is the version this site observed on 2026-10-28. The source may have been edited after 2026-03-29. This page does not claim the wording was captured then.
What the claim commits to
- Kind
- Observation
- Deadline, as the speaker wrote it
- None stated. This site does not add one.
- Deadline state when assessed
- No horizon
- Deadline state on 2026-10-29
- No horizon (the release’s reference date)
From the speaker
- This study reinforces
- what Anh Totti Nguyen has been saying for a long time
- a series of underappreciated papers
- like Vision Language Models are Blind
Our interpretation
- Read 'reinforces' as providing additional empirical support for the earlier finding that strong vision-language benchmark performance can coexist with important failures of visual understanding.
- Corroboration need not use identical models, datasets, or failure mechanisms; it must substantively support the shared conclusion.
- Assess the research record through the post date. The start is unspecified because 'for a long time' gives no date.
- Treat 'underappreciated' as an evaluative aside, and 'I keep trying to draw attention to' as personal commentary. The outcome concerns the substantive comparison of studies.
Counts as supported if: The new study demonstrates visual-grounding or evaluation failures that substantively corroborate limitations documented in Nguyen's earlier coauthored papers, including the named paper.
Counts as contradicted if: The studies' actual results do not share that substantive conclusion, or the new study instead refutes the earlier visual-understanding limitations.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Was it true for the period described?
Outcome: Supported
The dated evidence matches what the claim committed to, under the criteria frozen before grading.
Scored on a plain reading. The speaker named no measure for this claim, so it was judged on the reading an informed reader would take, stated below under our interpretation. Both reviews had to find that reading fair.
- Evidence as of
- Deadline state when assessed
- No horizon
- Assessment recorded
- Policy version
- 2026-10-28.7
Rationale
The comparison holds as broad corroboration. MIRAGE finds that benchmark accuracy can overstate visual understanding. Nguyen's named paper had already documented failures on simple visual tasks, and another earlier coauthored paper showed textual and knowledge biases interfering with image-based answers. These are different experiments supporting a shared concern. This does not establish direct replication, complete visual incapacity, or the separate description 'underappreciated'.
Limitations
- The studies test different model versions, tasks, and failure mechanisms.
- The named paper's quoted human baseline is expected performance, not a measured human sample in that passage.
- The verdict covers substantive corroboration, not the subjective claim about appreciation.
Evidence (3 items)
Supports the claim: arXiv,
high benchmark accuracy does not reliably indicate visual understanding.
Why it matters here: The study reports strong image-free benchmark performance and fabricated visual descriptions. Its published conclusion corroborates the broader warning that benchmark success can overstate visual understanding; the event date identifies the publication of this finding.
- Publisher
- arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Supports the claim: arXiv,
human expected accuracy of 100%.
Why it matters here: The named Nguyen-coauthored paper documents failures on simple visual tasks despite strong conventional benchmark performance. Its initial publication was July 2024; this quoted revision predates Marcus's post, and the human comparison is explicitly an expected accuracy.
- Publisher
- arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Supports the claim: arXiv,
further decreases VLM accuracy.
Why it matters here: This second Nguyen-coauthored paper shows prior knowledge and textual cues interfering with answers about altered images. It establishes an earlier related research finding, although its image-present experiments differ from MIRAGE's image-absent tests.
- Publisher
- arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Where it was said (1)
Said once in the sources this register covers.
: Marcus on AI, The mirage of visual understanding in current frontier models
This study reinforces what Anh Totti Nguyen has been saying for a long time, in a series of underappreciated papers like Vision Language Models are Blind that I keep trying to draw attention to.
- Where
- paragraph 4
- Published
- 2026-03-29
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment marcus-the-mirage-of-visual-understanding-a52bfa-a1: Current. Outcome: Supported. Evidence as of 2026-10-28. Public date not recorded.
Recorded events
Assessment publishedBy this site
First assessment published after both reviews and the final-text check passed.
About assessment marcus-the-mirage-of-visual-understanding-a52bfa-a1.