A.I.’S MIRAGE REASONING—ITS LATEST HALLUCINATIONS 

I’VE COME TO RESPECT GARY MARCUS’ knowledge and opinions concerning A.I. His latest analysis  “The Mirage of Visual Understanding in Current Frontier Models,” Substack, March 29, 2026, introduces me to yet another form of Large Language Model hallucinations. Here are tidbits gleaned from this article, together with my usual Internet sleuthing (which, one hopes, is rather more discerning than the LLM process).

A.I.’s Occasional B.S. It has long been recognized that in LLM’s scraping of data collections and algorithmically predicting things, they have been known to er… make stuff up. In a sense, it’s like a kid lying his way out of quandary: It’s done only when seemed necessary.

Medical A.I. Gary Marcus observes, “When a model achieves a ‘top rank on a standard chest X-ray question-answering benchmark without access to any images’ you know something is deeply wrong.” This seems like a new high for “Fake It Till It Makes It,” as described in SimanaitisSays, November 19, 2025.

Marcus cites “Mirage: The Illusion of Visual Understanding,” by Mohammad Asaki, et al., ariv.org, March 26, 2026. From their Abstract: “Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual–language reasoning remain surprisingly poorly understood.”

The researchers recount, “We report three findings that challenge prevailing assumptions about how these systems process and integrate visual information. Frontier models readily generate detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided, we term this phenomenon mirage reasoning. Second, without any image input, models also attain strikingly high scores across general and medical multimodal benchmarks, bringing into question their utility and design. In the most extreme case, our model achieved the top rank on a standard chest X-ray question-answering benchmark without access to any images. Third, when models were explicitly instructed to guess answers without image access, rather than being implicitly prompted to assume images were present, performance declined markedly.”

As Marcus notes, “AGI [Artificial General Intelligence] this stuff ain’t.”

A.I. and Blindness. Marcus describes, “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.” 

Yet Many Jobs Require Visual Understanding. Marcus observes, “Also, re the very active discussion on A.I. and jobs: although some white collar jobs (e.g., entry-level coder or market research assistant) may be in near-term jeopardy, many of those that require visual understanding (architect, cartographer, civil engineer, film editor, medical illustrator, urban planner, etc) probably aren’t vulnerable until entirely new techniques are developed.”

The Home Robot? “And humanoid home robots?,” Marcus posits. “Don’t make me laugh. If your humanoid robot can’t understand the visual world, it’s just a demo, and not something you can trust.” 

The thought of a robovac skinning the cat is particularly disturbing.

I sense that Gary Marcus is among the more conservative of A.I. specialists. And, to me, this makes his opinions all the more important in the hellbent profit-chasing that characterizes A.I. these days. 

A Coming Squabble. Not only in Silicon Valley, note, but in the White House’s “President Donald J. Trump Unveils National AI Legislative Framework”as well. For reading the latter press release with its required grain of salt, see also  “California to Impose New A.I. Regulations in Defiance of Trump Call,” The Guardian, March 30, 2026. 

Hmm… Which to believe? A White House press release? Or The Guardian?  

Let’s see what Gary Marcus’ Substack has to say as well. ds

© Dennis Simanaitis, SimanaitisSays.com, 2026