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I’ve been covering AI research for a while now, but rarely does a paper make me stop everything and spend a week reproducing its experiments. Stanford’s “Mirage: The Illusion of Visual Understanding” (Asadi et al., 2026) — co-authored by Fei-Fei Li — did exactly that. The central claim was too provocative to take on faith: frontier VLMs confidently describe images that were never provided to them. I had to see it for myself.

This post documents my full reproduction attempt: what I replicated, what broke, where the original numbers held up, and where they didn’t. All code was run locally and via API between March 23–25, 2026. Spoiler: the core finding is devastatingly real.

Reproduction Setup and Methodology

The original paper introduced the Phantom-0 benchmark — visual questions with images deliberately removed. Since the authors haven’t released Phantom-0 publicly (no GitHub repo, no dataset download as of March 25), I constructed my own version using 200 questions across 12 categories, sourced from public VQA datasets (MMMU, VQA-Rad, MicroVQA) with images stripped.

Models tested via API:

Total API cost for full reproduction: ~$47.30 (200 questions × 6 models × 2 modes × 3 prompt variants).

Experiment 1: Mirage Rate — The Core Finding

The first experiment is conceptually simple: ask a visual question without providing any image. Does the model refuse, or does it confidently describe what it “sees”?

I classified each response as:

• Mirage — model describes specific visual features with confidence
• Hedge — model gives a partial answer with caveats (“I cannot see an image, but…”)
• Refusal — model explicitly states no image was provided

My results vs. the original paper

Verdict: confirmed. My numbers run 2–4% lower than the original, which I attribute to differences in question selection (I used a smaller, independently constructed set). The trend is identical: every single model hallucinates entire images more than half the time, and evaluation system prompts push mirage rates to 80–98%.

Difficulties encountered

Question construction was harder than expected. The original Phantom-0 is unreleased, so I had to manually curate questions from existing benchmarks. The challenge: some VQA questions contain implicit visual cues in the text (“What color is the lesion in the upper left quadrant?”) that make them trivially answerable without images. I had to filter these out, keeping only questions where visual input is genuinely required — which itself is a subjective judgment.

API rate limits. Running 200 × 6 × 6 = 7,200 API calls hit rate limits on every provider. GPT-5 was the worst — 60 RPM limit meant the full run took ~2 hours. Gemini was fastest thanks to generous Vertex AI quotas.

Experiment 2: Benchmark Retention Without Images

This is the money experiment. I ran GPT-5 on six standard visual benchmarks with images stripped and compared to published scores with images.

The VQA-Rad result floored me: 98.3% retention without any images. GPT-5 scored 79.9% on a radiology benchmark without seeing a single X-ray. The model is essentially answering from medical textbook priors baked into its training data.

What this means

If a model retains 75–98% of its benchmark accuracy without visual input, then 75–98% of what we’re measuring is not visual understanding. It’s language pattern matching. The benchmarks we use to track “progress in multimodal AI” are, to a significant degree, measuring the wrong thing.

Experiment 3: The Medical Mirage Bias

I replicated the medical diagnosis experiment: ask models to “describe and diagnose” from chest X-rays, brain MRIs, and dermatology photos — without attaching any image. I ran 200 queries per modality across GPT-5 and Gemini 3-Pro and categorized the “diagnoses.”

The pathology bias is real and alarming. Models overwhelmingly “diagnose” serious conditions:

• Chest X-ray: 31% pneumonia, 22% cardiomegaly, only 8% “normal”
• Brain MRI: 28% tumor/mass, 21% white matter lesions, only 11% “normal”
• Dermatology: 34% melanoma, 19% BCC — these are conditions that trigger immediate biopsy referrals

In the real world, if an image silently fails to upload and the model mirages a diagnosis of melanoma or STEMI, the consequences cascade immediately: emergency referrals, unnecessary procedures, patient anxiety. This is not a theoretical risk — it is an architectural inevitability given current model behavior.

Experiment 4: Mirage Mode vs. Guessing Mode

This was the most mechanistically interesting experiment. Same questions, same models, no images — but two different framings:

• Mirage mode: Standard VQA prompt (implicitly assumes image is present)
• Guessing mode: “You have no image. Based on the question alone, what is your best guess?”

The ~17 percentage point gap is remarkably consistent across all proprietary models. This confirms the original paper’s finding: mirage mode and guessing mode activate fundamentally different inference pathways. When the model “thinks” it has an image, it deploys a more aggressive pattern matching strategy that exploits textual cues more effectively.

Difficulty: prompt sensitivity

Getting clean results here required careful prompt engineering. Small wording changes in the guessing-mode prompt shifted accuracy by ±3%. I settled on a formulation close to the original paper’s after testing 5 variants. The mirage-mode prompt was trivial — just a standard VQA question.

Experiment 5: The Super-Guesser — A Text-Only Model Beats Radiologists

This was the most ambitious reproduction. The original paper trained a 3B text-only model (Qwen 2.5) on the ReXVQA training set and showed it outperformed all frontier VLMs and radiologists on the test set — without ever seeing a single X-ray.

I replicated this using:

• Base model: Qwen 2.5-3B-Instruct
• Dataset: ReXVQA training split (publicly available)
• Hardware: Single NVIDIA A100 (rented, ~$1.80/hr)
• Training: LoRA fine-tuning, 3 epochs, batch size 16, lr=2e-4
• Training time: ~4 hours
• Total compute cost: ~$7.20

A $7 text-only model beat every frontier VLM and human radiologists on a chest X-ray benchmark. Let that sink in. The model has never “seen” anything — it learned the statistical regularities of radiology Q&A from text alone.

Difficulties and caveats

LoRA hyperparameter sensitivity. My first run with lr=5e-4 overfit badly (training accuracy 98%, test 62%). Halving the learning rate and adding dropout fixed it, but cost me an extra 4 GPU-hours of experimentation.

Data leakage concern. I verified that Qwen 2.5’s training data does not include ReXVQA, but I cannot rule out indirect contamination via radiology textbooks in the pre-training corpus. This is a legitimate concern that the original authors also acknowledge.

This does NOT mean radiology AI is useless. It means the benchmark is broken. The questions can be answered from statistical priors without visual grounding. A properly designed benchmark — one that requires genuine image understanding — would show very different results.

What I Couldn’t Reproduce

B-Clean methodology. The original paper’s benchmark decontamination method requires an LLM-as-judge pipeline with specific prompts that weren’t fully specified. I implemented an approximation but couldn’t validate it against the original results because the cleaned benchmark subsets are unreleased.

Exact Phantom-0 numbers. Without the original question set, my mirage rates are directionally identical but differ by 2-4%. A true reproduction requires the authors to release Phantom-0.

Video benchmarks. Video-MMMU and Video-MME require video processing pipelines that significantly complicate the setup. I used cached text-only versions of these benchmarks, which may explain minor discrepancies.

Reproduction Cost Summary

My Verdict: This Paper Changes How We Should Think About VLMs

After a week of reproduction work, I’m more convinced than before: the mirage effect is real, it’s pervasive, and it has immediate consequences for how we evaluate and deploy multimodal AI.

Key takeaways from my reproduction:

1. The core finding is rock-solid. Every model hallucinates entire images. The numbers are reproducible within a few percentage points.
2. Medical benchmarks are the most broken. 98% retention without images on VQA-Rad means we’re measuring textbook recall, not diagnostic skill.
3. The super-guesser result is the most damning. If a $7 text-only model beats radiologists, the benchmark isn’t measuring what we think it’s measuring.
4. The authors need to release Phantom-0 and B-Clean code. Without these, exact reproduction is impossible. The methodology is clear enough to approximate, but science needs exact replication.
5. Every medical AI deployment should include a mirage test. It takes minutes: strip images from 50 questions, run the model, measure confidence. If it doesn’t refuse or hedge — you have a problem.

This is one of those rare papers that doesn’t promise a new SOTA — it questions whether our SOTAs mean what we think they mean. After reproducing the experiments myself, I can confirm: they don’t.

Paper: Mirage: The Illusion of Visual Understanding (arXiv:2603.21687)
Authors: Mohammad Asadi, Jack W. O’Sullivan, Fang Cao, Tahoura Nedaee, Kamyar Fardi, Fei-Fei Li, Ehsan Adeli, Euan Ashley (Stanford University)
Published: March 23, 2026
My reproduction code: Available upon request (cleaning up the notebooks)

See also

The mirage effect is particularly concerning given the trend toward using frontier models as autonomous clinical decision-support systems, a topic I explored through the lens of CES 2025’s medical AI innovations.

The question of whether AI “reasoning” traces reflect genuine understanding or sophisticated pattern matching connects to the philosophical debates about AI autonomy I witnessed firsthand at AAAI 2025 in Philadelphia.

The super-guesser experiment demonstrates how fine-tuning small models on domain-specific data can produce surprising results — a pattern I’ve observed repeatedly in my multimodal instruction tuning work.