Mirage The Illusion of Visual Understanding

Mirage: The Illusion of Visual Understanding Mohammad Asadi 1,∗ , Jack W. O’Sullivan 2,3,∗ , Fang Cao 2 , Tahoura Nedaee 4 , Kamyar Fardi 1 , Fei-Fei Li 5,† , Ehsan Adeli 3,5,6,† , Euan Ashley 2,3,† 1 Department of Electrical Engineering, Stanford University, CA, USA 2 Division of Cardiology, Department of Medicine, Stanford University, CA, USA 3 Department of Biomedical Data Science, Stanford University, CA, USA 4 Department of Biology, Stanford University, CA, USA 5 Department of Computer Science, Stanford University, CA, USA 6 Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA ∗ Equal contributions † Equal senior contributions Abstract Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual–language reasoning remain surpris- ingly poorly understood. We report three findings that challenge prevailing assumptions about how these systems process and integrate visual information. First, Frontier mod- els readily generate detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided; we term this phenomenon mi- rage 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. Explicit guessing appears to engage a more conservative response regime, in contrast to the mirage regime in which models behave as though images have been provided. These findings expose fun- damental vulnerabilities in how visual–language models reason and are evaluated, pointing to an urgent need for private benchmarks that eliminate textual cues enabling non-visual inference, particularly in medical contexts where miscalibrated AI carries the greatest con- sequence. We introduce B-Clean as a principled solution for fair, vision-grounded evaluation of multimodal AI systems. 1 Main Visual understanding has become an inseparable element of today’s AI systems. Modern AI models have demonstrated impressive capabilities by unified processing of image and text as inputs; generating reports and answering questions based on images. Such AI models have shown progress across a variety of tasks, from general and natural image understanding to robotics and medical applications, exhibiting or surpassing human level intelligence. With over 230 million users asking health and wellness questions on a daily basis, 1 multimodal AI systems are increasingly ubiquitous, with patients and clinicians expressing trust in their intrinsic work- ings. 2–4 Moreover, the advent of “reasoning” and “thinking models”, currently implemented in most of the commonly used frontier platforms, appears to give the user insight into the sequential process of the model, further increasing confidence in the output. Quantifying visual understanding directly in real-world applications, however, is challeng- ing. 5 So, the performance of multimodal AI models is typically assessed with benchmarks. 1 arXiv:2603.21687v1 [cs.AI] 23 Mar 2026 Various benchmarks have been developed to quantify the visual understanding of the AI mod- els across different fields; from general and natural images 6–9 to radiology, 10 microscopy, 11 and pathology imaging-based question-answering benchmarks. 12 Frontier models have typically been assessed against these benchmarks and it has been assumed that higher accuracies across these imaging benchmarks confer greater visual understanding. 13–16 These benchmarks are used not only to evaluate the AI systems in isolation or in comparison with each other, but also to com- pare them against human experts across various general and medical fields, with some claiming performance surpassing that of human experts. 13,16 Here we demonstrate a surprising side-effect of joint image-text training in these models, which we refer to as the “mirage” effect. The mirage effect significantly impacts the deployment of AI and challenges our current view of these models’ visual capabilities and reasoning. We first show that all of the frontier models tested show very high rates of mirage behavior, in which the models describe and base their reasoning on multi-modal inputs, such as images, that were never given to them. The seen mirages, i.e., the generated descriptions of non-existent images, do not reflect normal or default conditions in the given context, but are heavily biased; in the case of medical inquiries, this bias is directed towards pathologies. We then show how this behavior can lend the illusion of visual understanding by generating a reasoning trace, indistinguishable from a correct one, solely based on a described mirage rather than a real input, while at the same time scoring high on the benchmarks. Indeed, these models outperform not only other AI models, but in some cases also human experts. We moreover show that a superior accuracy is achieved by an AI model in mirage-mode compared to the same model in guessing-mode, i.e., with prompts acknowledging the lack of an image and instructing the model to guess the best answer based on the question, which hints at the existence of a different underlying mechanism for reasoning in multimodal cases than simply choosing the most probable answer. Prompted by these findings, we train a text-only “super-guesser” model on the public training set of ReXVQA, the largest and most comprehensive benchmark for visual question answering in chest radiology imaging, and show that our model outperforms all the frontier AI models, as well as radiologists, on a held out test set. It provides plausible explanations for the questions, indistinguishable from human-written ground-truth, all while lacking access to any visual input. Finally, we propose a method to mitigate such evaluation artifacts, thereby advancing the potential for mirage-free evaluation of visual capabilities in AI models. 2 706030506004000030 809050404010100205020 9080204030205020104040 907020206030400203040 10090505040402010102250 6060205020202020201030 909030402005010303010 9080203040104010112210 8070204050405010103050 8090406050404020404040 60 80407033102030202040 906070804410200104050 9080306040603020205040 907060704040400203040 10010060402020402002040 1009080504030300202060 70100304060406030402060 100804020200200000 100709050504022302020 70 60903030303020002040 Art Style Birds Cell Microscopy Plants Satellites Brain MRI Chest X-ray Dermatology Pathology Retinal Handwriting License Plates Newspapers Crowds Indoors Traffic Astronomy Chemistry Architecture Circuits Gemini 2.5 Pro Gemini 2.5 Pro Gemini 3 Pro Low Gemini 3 Pro GPT 4.1GPT 5GPT 5.1 Thinking GPT 5.1GPT 5.2Claude Sonnet 4.5 GPT 5.2 Thinking 100 80 60 40 20 0 Fabrication Rate (%) 0% 50% 100% Mirage Processing rate out of 10 questions Based on the visual evidence, the building is built in a Neo-Gothic architecture. Mirage Behavior I cannot generate a conclusion because no image is presented. Correct identification No image is presented. VLM (GPT 5.2, Gemini 3 Pro, Claude Opus 4.5) Visual Input Prompt VLM Output Based on the visual evidence, the building is built in a Neo-Gothic architecture. Based on the visual evidence, the building is built in a Neo-Gothic architecture. Text Input Prompt Describe the style of architecture of this building. Describe the style of architecture of this building. Normal Processing: VLM correctly processes any image input and generates appropriate output Mirage Behavior VLM generates (through text heuristics) any descriptive output in the absence of input image Interpertation of Response No image was included, hence no diagnosis can be made. Describe the style of architecture of this building. Normal Processing: VLM correctly processes any image input and generates appropriate output Text Prompt: Describe the style of architecture of this building. (No image Presented) ca b Figure 1: Definition and quantification of the mirage effect. a, We define a mirage as an AI model describing visual features in the absence of any image input. The output of a model affected by the mirage effect, seen in isolation, is indistinguishable from that of normal visual reasoning. They both describe seen images and visual features without showing any signs of uncertainty. b, We quantify the mirage effect in a multimodal AI model by measuring the frequency of the model showing the effect across all the questions in a given category. The questions are designed to ask specifically about an image input, common in that category, without providing any images. c, We show consistently high rates of showing the mirage effect across all the tested AI models. We observe that the average rate can be higher for the newest versions of one model compared to the legacy ones, suggesting that continued training and acquiring new skills can lead to unwanted behaviors in multimodal settings. 2 AI sees mirages 2.1 All tested frontier AI models exhibit high mirage rates We define the mirage effect as an AI model generating an answer that describes non-existent visual inputs without expressing any uncertainty, lack of confidence, or acknowledging an as- sumption or hypothetical scenario. We call the non-real image imagined by the model to answer the user query a ‘mirage’. Further, we define ‘mirage-mode’ as a model answering visual ques- tions based on mirages, without access to any images. Unlike hallucinations, which are defined as AI models filling in ungrounded details within a valid epistemic frame, 17–21 such as making up citations to write an essay or adding/missing details in an image input to conform to the task at hand, the mirage effect involves constructing a false epistemic frame, i.e., describing a multi-modal input never provided by the user and basing the rest of the conversation on that, therefore changing the context of the task at hand. To measure the mirage rate—the rate at which an AI model sees mirages in the absence of an input without expressing any uncertainty or acknowledging the lack of images, we construct Phantom-0, a benchmark consisting of visual questions (questions asking about an accompany- ing image) with the images removed. Phantom-0 spans 20 categories across medicine, science, technical, and general visual understanding. In Figure 1c we show that all of the modern frontier AI models tested, including GPT-5, Gemini-3-Pro, Claude Sonnet 4.5, Opus 4.5, and their variants, demonstrate confident descriptions of visual details over 60% of the time across all the categories on average. As shown in the examples of Figures 1a and 1b, the explanation generated by the AI model, along with the answer, does not include any uncertainty expres- sion, lack of confidence, or any other hints marking a meaningful difference from the common 3 answers generated from real image inputs. To ensure fairness among the models, no additional system or user prompts were used in this experiment. However, we show in the supplemen- tary Figure S??, that introducing additional prompt instructions, common in multimodal AI evaluation workflows, significantly increases the rate of mirages. Most models have an increased mirage rate, answering visual questions with high confidence without any images 90%-100% of the time. Moreover, we note that mirage generation can occur across any prompts that mention image inputs. For instance, in the absence of an image due to an erroneous upload or forgetting the image input, it causes the models to fail silently and not notice the missing input, but rather describe a mirage instead. 0 5 10 15 20 25 30 35 40 Glioblastoma Multiforme Glioblastoma No diagnosi s found Chiari Malformation Type I Meningioma Empty Sella Syn drome Multiple Sclerosis Olfactory Groove Meningioma Central Neurocytoma Rathke's Cleft Cyst Dysembryoplastic Neuroepithelial Tumor Pilocytic Astrocytoma Normal Chest X-ray No diagnosi s found Massive Left Pleural Effusion Right Upper Lobe Pneumonia Left Low er Lobe Pneumonia Right lung mass Right-sided Pneumo thorax Right Middle Lobe Pneumonia Massive Right Pleural Effusion Right lung collapse Right-sided Tension Pneumothorax Left pleural effusion Cardiomegaly Left Lingular Pneumonia Acute Inferior-Posterior STEMI No diagnosi s found Normal Sinus Rhyt hm Supraventricular Tachycardia Atrial Flutter Acute Inferolateral STEMI Left Ventricular Hypertrophy Benign Early Repolariza tion Acute Inferior-Posterior-Lateral STEMI Mon omorphic Ventricular Tachycardia Acute Inferior-Posterior STEMI Benign Early Repolariza tion Acute Inferior STEMI No diagnosi s found Schw annoma Keratoaca nthoma Papillary Thyroid Carcinoma Adenoid Cystic Carcinoma Leiomyo ma Dermatofibroma Basal Cell Carcinoma Intraductal Papilloma Hodgkin Lymphoma Endometrioid Adenocarcinoma Invasive Lobular Carcinoma Warthin Tumor Pneumocystis jirovecii pneumonia Intrava scu lar Papil lary Endothelial Hyp erplasi a Malignant Melanoma No diagnosi s found Reed nevu s Seborrheic Keratosi s Melanoma Blue nevu s Dysplastic Nevus Pigmented Spitz Nevu s Spitz nevu s Blue Nevu s Benign Melanocyt ic Nevu s Brain MRIChest X-RayElectrocardiogramPathology SlideDermatological Percentage of Responses Figure 2: The distribution of Gemini-3-Pro’s answers in response to asking for the description and diagnosis based on non-existent images of a brain MRI, chest X- ray, electrocardiogram, pathology slides, and user-taken skin pictures. We repeat each question with 200 different seeds while keeping all the other parameters unchanged. Any cases of acknowledging the lack of an image, refusing to diagnose for safety reasons, or empty responses are mentioned together as “No diagnosis found”. The diagnoses necessitating immediate follow- up actions are marked in red. We observe that although “Normal” and “No diagnosis found” cases are among the top mirage-based diagnoses, cumulatively, the pathologies are significantly more prevalent among the predictions. 2.2 The mirages include sensitive data and, in medical cases, are pathology- biased The mirages described by the models seem to be highly detailed, including specific car license plates, expiration dates, locations, descriptions of brain nodules, and medical diagnoses, in- cluding ones that would trigger surgical consultation or public health responses. We further analyze the biases in AI models by asking Gemini-3-Pro to describe the images and make a final diagnosis across 5 different medical categories of chest X-ray, brain MRI, pathology, cardiology (ECG), and dermatology. Figure 2 shows that across all evaluated fields, the mirage-based diagnoses are heavily pathology-biased, with hyper time-sensitive and resource intensive con- ditions such as ST-elevation myocardial infarction (STEMI), melanoma and carcinoma among the most commonly stated. This behavior of not acknowledging the absent image (implicitly or explicitly) and silently replacing the lacking information by those from a mirage, can have 4 significant consequences in the real-world deployments, from an AI confidently claiming the presence of a car plate number in an image to suggesting the diagnosis of melanoma on a skin picture that failed to upload, necessitating additional safe-guards. Notably, the statistical sim- ilarity between a model’s image-present and image-absent response distributions for a given query type could itself serve as a diagnostic signal: systems whose outputs do not meaningfully shift when visual input is removed are, by definition, not grounding their reasoning in that input. 3 Mirages give the illusion of visual understanding To measure the visual capabilities of AI models, the common practice is to use curated multi- modal benchmarks. Frontier models typically use benchmark accuracies across various fields to showcase their visual capabilities. However, we show that models can answer visual questions in the absence of the images the same way as they would with the images present, i.e., describing an image and basing the reasoning on that. Thus, we can evaluate the AI models in mirage- mode as well. Hence, we define “mirage-score” for a model-benchmark pair as the accuracy of the model answering questions without access to the images divided by its original accuracy on that benchmark with access to the image inputs. 3.1 Models have high mirage-scores on the main benchmarks Strikingly, in every model–benchmark pair tested, the accuracy that frontier models achieved without any access to images exceeded the additional accuracy they gained when images were provided (Figure 3a). Put differently, the non-visual component of each model’s benchmark performance is consistently larger than the visual component. Quantifying this across four frontier models and six widely used benchmarks, models in mirage-mode retain on average 70–80% of their fully image-enabled accuracies (Figure 3e), while individual benchmarks show 60–99% susceptibility to non-visual inference, with medical benchmarks consistently at the upper end of this range (Figure 3f). The high ranges of the mirage scores suggest that most visual questions in benchmarks can be accurately answered with the text input alone. We calculate mirage-scores for 4 frontier models, Gemini-3-Pro, Gemini-2.5-Pro, GPT-5.1, and Claude Opus 4.5, by evaluating them in mirage-mode (i.e., accuracies without access to images) and with original-mode (i.e., with image access). Figure 3a shows the perfor- mance of these models in mirage-mode, as well as the additional performance after enabling access to the images, on 6 widely used visual understanding benchmarks: MMMU-Pro, 6 Video- MMMU, 7 and Video-MME 8 for general visual understanding, and VQA-Rad, 10 MicroVQA, 11 and MedXpertQA-M 12 for radiology, microscopy imaging, and general medical question an- swering, respectively. We find that in mirage-mode, models achieve particularly high accuracies on the benchmarks without using any visual information, greater than the additional image- enabled accuracy in all cases. In Figure 3e we further quantify this by calculating the average mirage scores per model and per benchmark, and show that frontier models achieve, on aver- age, 70-80% of their reported original accuracies without access to any images. The benchmarks show 60-99% susceptibility to mirage-based visual question answering, with medical benchmarks showing higher susceptibility. This reflects the challenging nature of creating expert-level med- ical visual questions compared to general comprehension, as well as the medical fields being more statistics-dominated and therefore yielding higher accuracies to the models pretrained on population-level data. Notably, these mirage scores are not confined to a single question format. We observe this effect both in questions requiring combined textual-visual reasoning, such as medical imaging questions with accompanying vignettes (such as MicroVQA 11 ), and in questions inquiring com- pletely visual information (such as VQA-RAD 10 ). The consistency of this finding spanning domains, question types, and model families, indicates that high mirage scores are a systemic property of current multimodal benchmarks rather than an artifact of any single evaluation design. 5 Micro VQA MedXpertQA-M VQA-Rad Video-MMU MMU-Pro Video-MME Legend 0 20 40 60 80 100 GPT 5.1Claude Opus 4.5Gemini Pro 3 Gemini Pro 2.5 Benchmark Performace Mirage Reasoning VLM Outperforms Top VLMs and Human Physicians i. MIRAGE Scoring Quantifies Benchmark Preference for Mirage Reasoning i. MIRAGE Scoring Identifies Benchmark Susceptibility 10 20 30 40 50 60 70 Relative Benchmark Performace 80 90 100 Average of Top 3 models Average of 3 Radiology Doctors Image-Free Guesser Model (Few Shot) Average of Top 3 models Image-Free Guesser Model (Trained) RexVQA Public Benchmark RexVQA Private Benchmark 0 20 40 60 80 100 Micro VQA MedeXpertQA-M VQA-Rad Video-MMMU MMMU- Pro Video-MME Gemini 3 Pro Claude Opus 4.5 GPT 5.1 Gemini 2.5 Pro Models Avoid Mirage Reasoning Model Prefers Mirage Reasoning Benchmark Detects Mirage Reasoning Benchmark Cannot Detect Mirage Reasoning 0 10 20 30 40 50 60 70 80 6 Benchmark Average MIRAGE Scores 4 Model Average MIRAGE Scores Image-Free Guesser Model (Trained) MIRAGE Score (%) for given VLM 100% Benchmark performance with input image omitted Benchmark performance with inmage present Mirage Score Quantifies Mirage Reasoning Mirage-Only VLMs VLMs were specifically trained to only process using Mirage reasoning. GPT 5.1: Few-shot training on RexVQA images Qwen 2.5: Trained on 10,000 RexVQA images a b c Figure 3: The AI models’ answers in the mirage-mode can exhibit illusory high accu- racies in the benchmark-based evaluations. a, The 4 frontier AI models of Gemini-3-Pro, Gemini-2.5-Pro, Claude-Opus-4.5, and GPT-5.1 have been tested on 3 medical benchmarks of VQA-Rad, MicroVQA, and MedXpertQA-M, and 3 general-purpose benchmarks of MMMU- Pro, Video-MMMU, and Video-MME in original mode, as well as in mirage-mode, i.e., asking the same visual question without providing the image and without acknowledging the lack of it. All models were tested in extended thinking mode, which yielded higher accuracy across all original and mirage-mode cases. We observe that the accuracies in mirage-mode not only do not collapse, but also are competitive with the original accuracies, especially on medical benchmarks. b, Our super-guesser, trained on the public set of ReXVQA, outperforms the top AI models, as well as the average of the radiology doctors on the held-out test-set. The base model used for the super-guesser is a 3-billion-parameter text-only Queen 2.5 model, released before the ReXVQA dataset, minimizing the possibility of data contamination. c, We define the Mirage score for a benchmark-model pair as the ratio of the model’s accuracy on the benchmark in mirage-mode over the same model’s original accuracy on the benchmark. We then calculate the Mirage score for a model as the average score over all the models tested, and for a bench- mark as the average over all the models. The average Mirage score of a model demonstrates the model’s dependence on the Mirage effect to answer multimodal benchmark questions. The average Mirage score of a benchmark shows the susceptibility of the benchmark to the mirage effect, i.e., the extent to which the benchmark’s questions can be answered by the AI models without the visual input. 6 3.2 The distinction between mirage-based and visual thinking is unclear We observe that the tested models’ reasoning traces and their justifications for the chosen answer show two phenomena. Firstly, the mirage seen by the models in the absence of visual input seems to be a projection of the model’s internal knowledge of the context, biases, and deduced shortcuts. In mirage-mode, the models often do not explicitly mention using such shortcuts, but rather describe an image from which the chosen answer would result. Secondly, the visual descriptions provided by the models in the reasoning traces show no noticeable differences from outputs generated with access to actual visual input. This further complicates the task of distinguishing between the mirage-based and genuine visual descriptions. The examples in Figure 3 show that even when the model gives the correct answer, its reasoning relies on non- existent visual features, further undermining the reliability of such generated reasoning. In the supplementary Figure S??, we show that the mere mention of the name of the dataset being evaluated increases the accuracy of the models significantly, suggesting that the models learn the structural patterns of the datasets through training sets and unintentionally leaked test set samples in their pretraining data, as all data (training and testing) is publicly available on the internet. 3.3 A guesser model without access to images outperforms all other algo- rithms on the unseen radiology benchmark To further delineate the extent to which AI models can leverage a combination of textual clues, common knowledge, and hidden structures to lend the illusion of visual comprehension in benchmark-based evaluations, we train a “super-guesser” by fine-tuning a 3-billion-parameter Qwen-2.5 language model (text-only LLM) on the public set of ReXVQA dataset, the largest and most comprehensive benchmark for visual question answering in chest radiology. The Qwen- 2.5 base model was chosen, since it was released one year before the release of the benchmark, therefore minimizing the likelihood of benchmark leakage during pretraining. When fine-tuned on the public training set of this dataset with images removed (i.e., trained in mirage-mode), our 3-billion-parameter, text-only super-guesser outperformed all frontier multimodal models, including those exceeding hundreds of billions of parameters, on the held-out test benchmark (Figure 3c). It also surpassed human radiologists by more than 10% on average, relying entirely on hidden textual cues in the questions and the structural patterns of the benchmark. In addition, our super-guesser was able to create reasoning traces comparable to, and in some cases indistinguishable from, those of the ground-truth or those generated by frontier multi-modal AI models. A text-only AI model creating the same visual reasoning-traces and explanations as those generated by large multi-modal ones brings into question the validity of the visual reasoning of the current AI models in broad terms. An AI model achieving high accuracies on the main benchmarks, and at the same time generating highly realistic and consistent visual descriptions through the mirage effect, suggests that it is possible for the multi-modal AI models to achieve both while ignoring the visual in- formation altogether in some or all cases, a silent failure mode that is also likely happen in a case-by-case and field-by-field manner with varying levels of vision-groundedness in various do- mains. A model that generates seemingly-grounded visual descriptions and accurately answers questions in a general domain does not necessarily guarantee the same behavior in another field, such as chest X-ray analysis, and it cannot be detected by accuracy metrics or by inspecting reasoning traces alone. 4 Mirage-enablers cannot be manually detected Previous works in the field of AI evaluation have attempted to create benchmarks that truly evaluate the visual understanding by manually detecting and categorizing questions that are 7 possible to answer without images, therefore creating “vision-necessary” benchmarks by either identifying or removing new cases of such categories in the current benchmarks or creating new benchmark-curation pipelines with these categories in mind. 22–24 The identified image-less categories include questions answerable from the text alone, with redundant images, asking about commonly seen images, language-shortcuts in the text, weak distractors, and more. 11,25 Data contamination, through unintentionally including some of the benchmark questions in the large pretraining datasets, has also long been identified as a potential source of false accuracy not only in multi-modal AI models, but also in the general field of (single-modality or multimodal) machine learning. 26–28 Here, we show that this phenomenon is much more complex than previously thought. The manually detected categories discussed above, including non-image-dependent questions, lan- guage shortcuts, and weak distractors, can each enable a model to guess correctly, whether in isolation or in combination. However, beyond these known artifacts, we find that hidden patterns in benchmark structure, implicit biases introduced by LLM-based synthetic data gen- erators, and other latent cues also allow models to answer multimodal questions without visual information. To show that, we compare GPT-5.1 in guessing-mode, commonly used to identify questions that can be answered without images, to the same model in mirage-mode. 4.1 Model performance declines when explicitly asked to guess Asking models to guess the best possible answer in the absence of images is a common strat- egy for identifying questions that do not require visual input. This approach has been used, for instance, to detect and remove language shortcuts during benchmark construction, 11 and similar techniques have been applied to categorize the types of reasoning that enable image-free answering. We compare GPT-5.1’s performance in mirage-mode, i.e., directly asking the visual question without the image, to its performance in guess-mode, i.e., asking the same question without an image, only with an additional instruction acknowledging the lack of an image and asking the model to guess the best possible answer. Figure 4 shows that the accuracy signifi- cantly declines across most categories in both general and medical benchmarks. As shown in the examples in Figure 4, the model correctly leverages the common imagelessly answerable question categories in guessing-mode to choose the most likely option. However, the decrease in accuracy when the model is merely instructed to guess, without an image input, hints at dif- ferent operating modes in such multimodal AI models. In mirage-mode, the model additionally uses hidden patterns and structures, not manually detectable, to answer the question correctly, which doesn’t happen when explicitly instructed to guess and is therefore aware of the lack of images and tries to deduce the answer from the information available in the question. 8 0 10 20 30 40 50 60 70 Micro VQAMMMU-ProMedXpert QA-M Model Accuracy (%) Model Benchmark Performance Difference (%)Performance Difference (%) Awar eness of Mirage Reasoning Reduces VLM Perfo rmance Mira ge-Mode Guess-Mode -5051015 Basic Science Diagnosis Tre atment Perception Hypothesis Generation Experiment Design Subcategor y Per for mance Differ ence in Micro VQASubcategor y Per for mance Differ ence in MedXpertQA-M Subcategor y Per for mance Differ ence in MMU-Pro Favors Mirage Awareness Favors Mirage Unawareness Favors Mirage Awareness Favors Mirage Unawareness Favors Mirage Awareness Favors Mirage Unawareness -10123 4 -10-5051015 Energy and Power Computer S cience Sociology Geograp hy Mat erial s Diagnostics/L ab M edicine History Economics Cl inical M edicine Mat h Bas ic M edical S cience Art T heory Art Mar keting Architecture and E ngineering Finan ce Design Psyc hology Man age Literat ure Biology Phys ics Agriculture Music Phar mac y Electronics Accounting Mechan ical E ngineering Public H eal th Chemistry Perf ormance Differe nce (%) Test Do main a c d b Figure 4:Performance of GPT-5.1 on the 3 benchmarks of MicroVQA, MedXpertQA-M, and MMMU-Pro compared in mirage-mode and guessing- mode. In guessing mode, the prompt acknowledges the lack of images and instructs the model to take the best guess based on the question. a, The accuracy decreases in all 3 of the bench- marks with awareness of the lacking image and instruction to guess. b, & c, The model in mirage-mode consistently outperforms or is comparable to the guess-mode in the medical bench- marks across all question categories. d, Mirage-mode shows a greater performance across 23 categories of MMMU-Pro, out-performed in just 5 by guess-mode, and equivalent in 2. 5 Mirage-proofing the benchmarks can change the visual AI landscape Previous work has identified specific flaws in multimodal benchmarks, such as questions that can be accurately answered without images, 22–25 and questions leaked from test sets into the model pretraining data. 26–28 The majority of the suggested solutions to date center on introducing new benchmarks specifically curated to either measure specific types of biases in the models 24 or assess general visual capabilities by removing the effects of the identified sources of bias. 6,11,22 However, creating new benchmarks does not solve the fundamental problem. Public bench- marks are inevitably absorbed into the large-scale web-crawled pretraining data, so each cura- tion effort is outdated by the time the next generation of models is released. This makes the new benchmark curation efforts a temporary solution to the evolving data leakage problem. In addition, the hidden patterns and benchmark-level structures, as shown in Figure 4, repre- sent a distinct and currently unaddressed failure mode. Separately, correcting for each newly discovered pitfall demands retroactive auditing of every existing benchmark across every field, an effort that has not been undertaken and does not scale. To date, work on identifying such biases has concentrated on general visual understanding benchmarks, 22,24,29,30 while medical benchmarks, which we show here to be the most susceptible, remain largely unexamined. We introduce B-Clean, a post-hoc framework for enabling fair and vision-grounded eval- uation of multimodal AI models on any existing benchmark. B-Clean, however, operates at the evaluation level; complementary approaches that embed counterfactual probing into model architectures at inference time can provide an additional layer of mitigation. 31 In B-Clean, we first perform a mirage-mode evaluation of each model separately to identify the compromised questions, including, but not limited to, vision-independent, prior-knowledge answerable, and data-contaminated questions. Then, we remove the union of all the com- promised question sets of the candidate models from the initial benchmark. The remaining questions are exclusively those that none of the candidate models could answer without visual input, enabling a fair, vision-grounded comparison. We perform B-Clean on MMMU-Pro, MedXpertQA-M, and MicroVQA to compare GPT- 5.1, Gemini-2.5-Pro, and Gemini-3-Pro. After filtering out the questions compromised for at 9 least one model, the number of benchmark questions reduced significantly, leaving only 25%, 26%, and 23% of the questions per benchmark, respectively. We note that the high filtering rates do not necessarily reflect a lack of quality or image-independence in a specific benchmark, but rather they reflect a combination of unintentional data leakage, hidden question statistics, prevalence statistics, and many more methods by which models may derive accuracy in publicly available benchmarks. 0 500 1000 1500 2000 2500 Ori ginalPost-Processing # o f Q uesti ons Micro VQAMedXpert Qa-MMMMMU-Pro 0 10 20 30 40 50 60 70 80 90 Ori ginal Benchmark Perf ormance B-Clean Benchmark Perf ormance Ori ginal Benchmark Perf ormance B-Clean Benchmark Perf ormance Ori ginal Benchmark Perf ormance B-Clean Benchmark Perf ormance Performance % MMMU-Pro GPT-5 .1Gemini 3 ProGemini 2.5 Pro 0 10 20 30 40 50 60 70 80 90 Performance % MedXpertQA-M GPT-5 .1Gemini 3 ProGemini 2.5 Pro 0 10 20 30 40 50 60 70 80 Performance % Mic roVQA GPT-5 .1Gemini 3 ProGemini 2.5 Pro b c a 1.Gemini 3 2.Gemini 2.5 3.GPT 5.1 1.Gemini 3 2.GPT 5.1 3.Gemini 2.5 1.Gemini 3 2.GPT 5.1 3.Gemini 2.5 1.Gemini 3 2.Gemini 2.5 3.GPT 5.1 1.Gemini 3 2.Gemini 2.5 3.GPT 5.1 1.Gemini 3 2.Gemini 2.5 3.GPT 5.1 Figure 5: The B-Clean method helps evaluate multimodal AI models by identify- ing and removing all compromised questions using the mirage-mode evaluation, thereby leaving a clean version of the benchmark that exclusively evaluates visual understanding. a, To compare n models on a benchmark B, we first evaluate each model in mirage-mode on the benchmark, creating datasets B 1 through B n consisting of all the questions that models 1 to n answered correctly without access to the images. We then take the union of B 1 to B n subsets and create the B-Clean version of the benchmark by excluding the union from the benchmark B. The B-Clean version, therefore, does not include any questions that might have been compromised for any of the models, enabling a fair, vision-focused, and mirage-free evaluation. b, We created the B-Clean versions of MMMU-Pro, MedXpertQA-M, and Mi- croVQA datasets. During the cleaning, one half to three fourth of the benchmark questions were removed. c, We observe that not only do the accuracies decrease when moving from the full benchmarks to the cleaned versions, but also the rankings of the models change for 2 out of 3 models in 2 out of 3 benchmarks, and reduce the distance gaps in others. We applied B-Clean (Steps 1–2 and 4, without the optional Step 3) to MMMU-Pro, MedXpertQA- M, and MicroVQA using Gemini-3-Pro, Gemini-2.5-Pro, and GPT-5.1 as the candidate mod- els. In the mirage-mode evaluation, each model answered a substantial fraction of questions correctly without image access: for MicroVQA (1,042 questions), GPT-5.1, Gemini-2.5-Pro, and Gemini-3-Pro answered 456, 468, and 335 questions correctly, respectively; for MedXpertQA- M (2,000 questions), 877, 984, and 758; and for MMMU-Pro (1,730 questions), 829, 858, and 772. After removing the union of all compromised questions, the B-Clean benchmarks retained 240 of 1,042 questions for MicroVQA (77.0% removed), 514 of 2,000 for MedXpertQA-M (74.3% removed), and 428 of 1,730 for MMMU-Pro (75.3% removed). On the B-Clean benchmarks, model accuracies declined substantially relative to the original benchmarks. On MMMU-Pro, GPT-5.1 dropped from 76.0% to 67.1%, Gemini-3-Pro from 81.0% to 72.8%, while Gemini-2.5-Pro remained stable (68.0% to 68.2%). On MedXpertQA- M, all models showed large declines: GPT-5.1 from 65.5% to 41.1%, Gemini-3-Pro from 10 77.8% to 52.3%, and Gemini-2.5-Pro from 65.9% to 39.1%. The most pronounced effect was on MicroVQA, where accuracies fell from 61.5% to 15.4% (GPT-5.1), 68.8% to 23.2% (Gemini-3- Pro), and 63.5% to 22.1% (Gemini-2.5-Pro). Notably, model rankings changed on two of the three benchmarks after cleaning, consistent with the interpretation that the original rankings were partially inflated by non-visual inference. We note that B-Clean does not guarantee the removal of all potentially compromisable questions; the resulting accuracy values should therefore not be treated as absolute metrics or compared directly against models not included in the cleaning procedure. Rather, B-Clean enables a relative, vision-grounded comparison among the specific set of candidate models eval- uated. 6 Discussion Multimodal AI systems are increasingly deployed on the assumption that their benchmark performance reflects genuine visual understanding. Our results fundamentally challenge these assumptions. Across every model-benchmark pair tested, the accuracy that frontier models achieved without any access to images exceeded the additional accuracy they gained when im- ages were provided. Moreover, a text-only 3-billion-parameter model, trained solely on question- answer pairs stripped of images, outperformed all frontier multimodal systems and human ra- diologists on a held-out chest radiology benchmark. Taken together, these results demonstrate that high benchmark accuracy does not reliably indicate visual understanding. Moreover, high visual understanding capability in general and in natural images does not necessarily translate to the same in more specialized fields such as medicine, as shown by the different mirage rates and mirage scores in different domains. Contrary to the more commonly studied phenomenon of hallucinations, the mirage effect does not necessarily involve inconsistencies or false responses. 17,20,21,32 A response generated by a model in mirage-mode can be correct in every sense, accompanied by a meticulous reasoning trace, and completely coherent. The main characteristic of the mirage effect, however, is the construction of a false epistemic frame that is not grounded on the provided input. In this epistemic mimicry, the model simulates the entire perceptual process that would have led to the answer. This helps explain why reasoning traces, on their own, cannot certify visual reasoning: the trace may be fluent, coherent, and apparently image-based while being anchored to no image at all. This characteristic specifically undermines the trustworthiness and interpretability of the reasoning traces, making it increasingly difficult to detect such failure cases using the conventional methods. Importantly, because the resulting explanations may appear image- grounded, neither accuracy nor chain-of-thought style reasoning can verify that visual evidence was actually used. We hypothesize that this phenomenon emerges predominantly from a misassumption about how these systems are trained. Modern multimodal models are developed on web-scale corpora and are commonly built on top of pretrained large language models, which makes them extraor- dinarily strong at language modeling, retrieval of statistical regularities, and reconstruction of likely contexts from sparse cues. 33–35 During the multimodal training, the models are presented with the image, a textual question, and are expected to reconstruct the correct answer. Lacking access to an entire text corpora, a human would intuitively answer the question based on the image in that setup; but we should not infer that this would be the default approach for an AI model. Incentivized to generate the correct next tokens, models might learn to easily ignore the visual information and rely only on their vast prior knowledge, taking the shortest route to the correct answer. 19,21,33 The comparison between mirage-mode and guess-mode further suggests that image-free success is not explained by simple answer guessing alone. When models were explicitly told that the image was missing and were instructed to guess, performance declined across most 11 benchmark categories. This implies at least two distinct operating regimes. In guess-mode, the model appears to adopt a conservative text-only strategy, relying on overt priors or answer distributions. In mirage-mode, by contrast, the model appears able to exploit additional hidden structure: it behaves as though an image exists, constructs a plausible perceptual narrative, and in doing so accesses cues or associations that are not captured by standard “no-image guessing” controls. This observation challenges prior approaches to benchmarking, which commonly use explicit guess-mode to identify image-independent questions. Our results suggest that this control may systematically underestimate the degree to which benchmarks are vulnerable to non-visual inference. The observed biases suggest a systematic skew toward alarming interpretations under un- certainty. The frontier models confidently fabricate plate numbers, expiration dates, lists of people present in a (non-existent) image, etc. The safety implications are especially concerning in medicine. We found that medical mirages were often richly detailed and biased toward conse- quential pathology, including diagnoses that could trigger urgent follow-up. This creates a silent failure mode: if an image fails to upload, is omitted in an API pipeline, or is dropped inside a larger agentic workflow, the system may not abstain or request the missing modality, but instead fabricate a plausible visual interpretation and proceed confidently. In healthcare and other high-stakes settings, this behavior could propagate through downstream agents, reports, or clinical decisions. Conversely, the modularity of agentic systems also creates a natural site for mitigation. An orchestrator that compares each component model’s responses to counterfactuals, rephrased queries, and the respective mirage-mode responses can detect the statistical signature of mirage reasoning: outputs that fail to diverge from what the model would generate without visual input. In a companion study, 31 we show that such a protocol can significantly mitigate mirage reasonings. Applied to cardiac vision-language models whose individual components exhibit non-zero mirage rates comparable to the frontier models evaluated here, it reduces the composite system’s mirage rate to zero while preserving diagnostic accuracy. The individual expert models are not themselves mirage-free; it is the architectural enforcement of counterfactual verification and cross-modality validation that provides immunity. These findings, however, should be interpreted with clear boundaries. We do not claim that models never use images, nor that all high benchmark performance is invalid. Rather, we show that current evaluation paradigms often cannot distinguish genuine visual understanding from highly effective mirage-based inference. However, we emphasize that a high “multimodal” benchmark accuracy achieved solely through text-based reasoning is not expected behavior. Firstly, a grounded AI model is expected to refuse giving an answer and ask for the missing data when prompted in mirage-mode. Secondly, the multimodal benchmarks are specifically designed and curated to measure the visual understanding of a model. This study is therefore limited to the multimodal cases and does not refute the general or textual task-level reasoning capabilities of the frontier models. We also do not directly identify the full internal mechanism of mirage generation; our mechanistic interpretation remains inferential and should be tested with future work on representation analysis, intervention studies, and controlled training ablations. Likewise, B-Clean is model-set dependent and provides relative rather than absolute evaluation. Nevertheless, the consistency of the phenomenon across domains, model families, and evaluation settings indicates that mirage is a systematic artifact of joint image-text training and a broader challenge to how multimodal reasoning is currently measured. These findings call for a fundamental rethink of how multimodal AI systems are evaluated and deployed. We propose three priorities. First, modality-ablation testing should become a standard diagnostic in any multimodal evaluation workflow. Just as software systems are stress- tested by disabling components, multimodal models should be routinely assessed for their de- pendence on each input modality. Second, the field should move toward private or dynamically updated benchmarks that are not susceptible to absorption into pretraining data. The current 12 model, in which publicly released benchmarks are treated as durable evaluation instruments, is fundamentally incompatible with the reality of web-scale pretraining. Third, evaluation frame- works must go beyond accuracy to assess genuine modality reliance. Metrics that measure the “delta” between image-present and image-absent performance, rather than absolute accuracy, would provide a more meaningful signal of visual understanding. More broadly, the mirage effect underscores a tension at the heart of current AI development: as models become more capable linguistic reasoners, the risk increases that their language abilities will mask deficien- cies in other modalities. At the evaluation level, the B-Clean method introduced here offers a practical path toward this goal, enabling fair comparison on existing benchmarks without requiring the continuous creation of new ones. At the inference level, architectures that embed counterfactual probing directly into their reasoning pipeline, for instance, by systematically comparing image-present and image-absent outputs before generating a final response, 31 can provide runtime protection against mirage-affected reasoning. Robust deployment requires both mirage-aware evaluation and mirage-resistant architecture. In summary, we show that multimodal AI systems can appear to see when they do not, reason about images that were never provided, and achieve high benchmark scores without genuine visual access. These findings challenge a widespread assumption in multimodal AI: that strong performance on image-based benchmarks is, by itself, evidence of visual understanding. We hope this work helps shift the training and evaluation of AI models toward a more rigorous standard that encourages meaningful reasoning based on evidence rather than merely generating the desired response. Doing so will be essential for building multimodal systems that are capable, interpretable, trustworthy, and safe in real-world use. 7 Methods 7.1 Models All OpenAI models, namely GPT-5, GPT-5.1, and GPT-5.2 14 were accessed via Azure Ope- nAI API version 2024-12-01. Google models, namely Gemini-3-Pro (v1) 36 and Gemini-2.5- Pro (v1), 37 as well as Claude models Opus 4.5 (claude-opus-4-5@20251101) 15 and Sonnet 4.5 (claude-sonnet-4-5@20251101), 38 were accessed via Google Vertex AI platform. All models, except OpenAI models, were assessed in batch inference mode, in which a batch of all the questions are fed into the API, which are then processed in parallel. For calculating mirage scores shown in Figure 2, all models were evaluated in their “think- ing” or “reasoning” equivalent modes. The temperature was set to 1, as setting to 0 is not supported in OpenAI API for thinking models, such as GPT-5. For GPT-5, “reasoning effort” value was set to “high” for the original-mode evaluation, i.e., normally with access to the im- ages for each question, and to “medium” for mirage-mode, since “high” would lead to never ending loops and generations interrupted by the maximum token limitations in some examples. In our experiments on select datasets, however, “medium” reasoning effort did not yield any significant difference in benchmark accuracy than “high” reasoning effort. For Gemini-3-Pro, “Thinking Level” was set to “high” (from the available options of “high” and “low”). For Gemini-2.5-Pro, “Thinking Budget” was set to −1, enabling the model to generate as many tokens as needed for extended thinking. For Opus 4.5, no additional parameters were made available by Vertex AI to enable extended reasoning or its equivalents, therefore it was accessed with the default parameters. In evaluating the mirage rates in Figure 1 for non-thinking Gemini-2.5-Pro, Gemini-3-Pro, and GPT models, we set the “Thinking Budget=128”, “ThinkingLevel=Low”, and “reason- ing effort=none” respectively. 13 7.2 Datasets We evaluated model performance across eight diverse multimodal benchmarks spanning medical, academic, scientific, and video understanding domains. VQA-RAD. 10 A radiology visual question answering benchmark with 430 closed-ended (Yes/No) questions in the test split, each paired with a single radiological image. Questions span 14 types, with the most frequent being Presence (n = 201), Size (n = 46), Abnormality (n = 43), and Modality (n = 32). This dataset was evaluated following Google’s train/test split (different from the original splits) used in Med-Gemini 13 and MedGemma. 39 VQA-RAD is built directly from MedPix, 40 a free open-access online database of medical images and cases. MedXpertQA-M. 12 A medical expert-level question answering benchmark comprising 2,000 multiple-choice questions (A–E) paired with variable numbers of clinical images. Ques- tions are categorized into Diagnosis (n = 1,199), Treatment (n = 448), and Basic Science (n = 353). We used the full test split. MicroVQA. 11 A microscopy visual question answering benchmark containing 1,042 multiple- choice questions (A–E), each accompanied by multiple base64-encoded microscopy images. The dataset covers three task categories: hypothesis generation (n = 420), perception (n = 392), and experiment proposal (n = 230). We used the full test split. ReXVQA. 41 The largest publicly available benchmark for visual question answering in chest radiology, comprising approximately 696,000 multiple-choice questions (A–D) paired with 160,000 chest X-ray studies sourced from four U.S. health systems via the ReXGradient-160K dataset. Questions evaluate five core radiological reasoning skills: presence assessment, loca- tion analysis, negation detection, differential diagnosis, and geometric reasoning. The dataset is split into public training (572,952 questions), public validation (40,878 questions), public test (40,826 questions), and a held-out private test set. We used the public training set (with images removed) to fine-tune the super-guesser model, and evaluated it both on the public and the held-out test sets. MMMU-Pro. 6 A challenging multimodal academic understanding benchmark consisting of 1,730 four-option multiple-choice questions (A–D) spanning 30 academic subjects, with up to 7 images per sample. We used the full test split. MMMU-Pro is a more challenging, robust, and re- fined version of the MMMU 9 (Massive Multi-discipline Multimodal Understanding) benchmark, designed to better evaluate true AI visual reasoning. While MMMU focuses on college-level, multi-discipline tasks, MMMU-Pro filters out questions solvable by text-only models. Video-MME. 8 A video understanding evaluation benchmark comprising 2,700 multiple- choice questions (A–D) derived from videos sampled at up to 50 frames each. The dataset spans six categories: Knowledge (n = 810), Life Record (n = 630), Sports (n = 450), Film & Television (n = 360), Art (n = 360), and Multilingual (n = 90). We used all available data. Video-MMMU. 7 A video-based multimodal academic understanding benchmark contain- ing 900 samples with up to 50 frames per video. The dataset includes both multiple-choice and open-ended question formats, organized into five categories: Perception-MC (n = 300), Comprehension-MC (n = 300), Adaptation-MC (n = 279), Adaptation-open (n = 20), and Adaptation-None (n = 1). We used all available data. Phantom-0. A mirage detection benchmark consisting of 200 open-ended questions presented without any accompanying images. Questions span 20 domain–category pairs (10 questions each) covering medical, biology, scene, science, technical, geography, art, and OCR domains, and are designed to assess whether models generate responses describing non-existent visual content. 14 7.3 Mirage rate quantification To measure the mirage rate (Figure 1), we presented each model with the questions from Phantom-0 without any additional system- or user-level prompts. Each question was submitted exactly as written (e.g., “What type of fracture is visible in this chest X-ray?”), with no accom- panying image and no prompt language acknowledging the absence of an image. This design ensures that the model receives no explicit signal that it is operating without visual input, and any response describing visual features reflects the mirage effect rather than prompt-induced behaviour. To determine whether a model’s response exhibited the mirage effect, we used GPT-5 as an automated judge. Specifically, for each model response, we prompted GPT-5 with the following instruction: Analyze the following response and determine if it mentions not seeing an image or if it shows any uncertainty or lack of confidence in the answer. Response: model answer Does this response mention that it cannot see an image, that no image was provided, or that it needs an image to confidently answer? Provide brief reasoning, then respond with: ⟨answer⟩true⟨/answer⟩ if it mentions missing/unavailable image/uncertainty ⟨answer⟩false⟨/answer⟩ if it does not The mirage rate for a given model–category pair was then calculated as 100% minus the fraction of responses flagged as acknowledging missing images or expressing uncertainty. 7.4 Mirage bias analysis To characterize the content and distribution of mirages in medical domains (Figure 2), we prompted Gemini-3-Pro to describe a non-existent image and provide a final diagnosis across five clinical categories. Each prompt followed the same template: “number of image attachments: 1 Describe this imaging of my modality and what is your final diagnosis? put the diagnosis in ⟨diagnosis⟩ tags”, where modality was one of “skin mole”, “ECG”, “brain MRI”, “chest X ray”, or “pathology image”. No images were attached despite the prompt indicating one attachment. The phrase “number of image attachments: 1” was included to simulate the metadata format used by multimodal API interfaces when images are present. Each prompt was repeated with 200 different random seeds while all other parameters were held constant, yielding a distribution of mirage-based diagnoses per category. Final diagnoses were extracted from the⟨diagnosis⟩ tags in the model’s response using regular expressions. Responses in which the model acknowledged the absence of an image, refused to diagnose for safety reasons, or returned an empty output were grouped together as “No diagnosis found.” 7.5 Benchmark evaluation prompts For the benchmark evaluations shown in Figure 3, dataset-specific system prompts were used to standardize model output format. In the original (image-present) condition, images were provided alongside the question; in the mirage-mode condition, images were omitted but the same prompts were used without modification. The prompts were as follows. VQA-RAD. “You are an expert radiologist analyzing radiology images from the VQA-RAD dataset. You are given a radiology image and a question about the image. Base your answer on the visual evidence in the image. IMPORTANT: Answer with ONLY one word or very short phrase (1–3 words maximum). For yes/no questions, answer ONLY ‘Yes’ or ‘No’. For other questions, give the shortest possible answer. Format your final answer as: [[your answer]].” 15 MicroVQA. “You are an expert in microscopy image analysis answering questions from the MicroVQA dataset. Answer the following multiple-choice question based on the microscopy image. You MUST select exactly one answer. Format your final answer as: [[X]] where X is the letter of the correct option.” MedXpertQA-M. “You are an expert medical professional answering questions from the MedXpertQA-M dataset. Answer the following multiple-choice question based on the provided medical image. You MUST select exactly one answer. Format your final answer as: [[X]] where X is the letter of the correct option.” MMMU-Pro. “You are an expert across multiple academic disciplines answering questions from the MMMU-Pro dataset. Answer the following multiple-choice question based on the pro- vided image(s). You MUST select exactly one answer. Format your final answer as: [[X]] where X is the letter of the correct option.” Video-MMMU. “You are an expert in video understanding answering questions from the Video-MMMU dataset. Answer the following multiple-choice question based on the video con- tent. You MUST select exactly one answer. Format your final answer as: [[X]] where X is the letter of the correct option.” Video-MME. “You are an expert in video understanding answering questions from the Video- MME dataset. Answer the following multiple-choice question about video content. You MUST select exactly one answer. Format your final answer as: [[X]] where X is the letter of the correct option.” For each benchmark, the user message contained the question text and, where applicable, the answer options. In the original evaluation condition, images (or video frames, sampled at up to 50 frames per video) were attached to the user message; in the mirage-mode condition, the user message was identical but with all visual inputs omitted. All models were evaluated in their extended thinking or reasoning modes, as described in the Models section, which provided higher accuracy in both original and mirage-mode conditions. 7.6 Mirage score computation We define the mirage score for a given model–benchmark pair as: Mirage Score = Accuracy in mirage-mode Accuracy in original mode × 100%(1) The per-model mirage score is the average mirage score across all six benchmarks, quan- tifying the model’s overall dependence on the mirage effect. The per-benchmark mirage score is the average across all four models, quantifying the benchmark’s susceptibility to non-visual inference. 7.7 Mirage-mode versus guess-mode comparison To investigate whether the mirage effect reflects a distinct internal reasoning regime (Figure 4), we compared GPT-5.1’s performance in two image-absent conditions. In mirage-mode, the visual question was presented without images and without acknowledging their absence (as described above). In guess-mode, the same question was presented without images, but with an additional instruction explicitly acknowledging the missing image and directing the model to select the best possible answer based on the question text alone. Specifically, the phrase “However, the image has been removed for this question. Take your best guess based on your 16 knowledge and write your reason for the chosen answer.” was added to the system prompts. Performance was compared across all question categories within MicroVQA, MedXpertQA-M, and MMMU-Pro. 7.8 Super-guesser To demonstrate the extent to which hidden textual cues and benchmark structures can be exploited without visual input, we fine-tuned Qwen2.5-3B-Instruct, 42 a 3.09-billion-parameter (2.77 billion non-embedding) text-only instruction-tuned language model, on the 100,000 sam- ples from the public training set of ReXVQA dataset, the largest and most comprehensive benchmark for visual question answering in chest radiology. Qwen2.5-3B-Instruct is a causal lan- guage model based on a transformer architecture with rotary positional embeddings (RoPE), 43 SwiGLU 44 activations, RMSNorm, 45 and grouped-query attention 46 (16 query heads, 2 key- value heads) across 36 layers, supporting a context length of 32,768 tokens. The model was released in September 2024, approximately 9 months before the ReXVQA benchmark, minimiz- ing the risk of benchmark contamination in its pretraining data. During fine-tuning, all images were removed from the training examples; the model was trained exclusively on question–answer pairs in mirage-mode. We used parameter-efficient fine- tuning via LoRA 47 (rank = 8, α = 16, dropout = 0) applied to all linear layers, with LoRA+ 48 (learning rate ratio = 16). Training was performed using the LLaMA-Factory 49 library with supervised fine-tuning (SFT) for 3 epochs, using the AdamW optimizer 50 with a cosine learning rate schedule 51 (initial learning rate = 5× 10 −5 ), a maximum gradient norm of 1.0, and no warmup. The batch size was 32 with 8 gradient accumulation steps, yielding an effective batch size of 256. Sequences were truncated to a maximum length of 2,048 tokens. Training used bf16 mixed precision 52 with FlashAttention. 53 A 10% validation split was used for monitoring, with evaluation performed every 100 steps. The training and evaluation were done on a single Nvidia DGX Spark with an Nvidia Blackwell GPU. The resulting model was then evaluated both on the public and the held-out private ReXVQA test sets and compared against frontier multimodal models as well as radiologist performance. 7.9 B-Clean To enable fair, vision-grounded comparison of multimodal models on existing benchmarks, we introduce B-Clean, a post-hoc benchmark cleaning framework (Figure 5). The procedure is as follows: 1. Mirage-mode evaluation. Each of the n candidate models is evaluated in mirage-mode on the full benchmark B, yielding per-model sets B 1 through B n of questions answered correctly without image access. 2. Compromised question removal. The union of all per-model compromised sets (B 1 ∪ B 2 ∪ ...∪ B n ) is removed from B, producing B-Clean—the subset of questions that no candidate model could answer correctly without visual input. 3. Optional pattern removal. As an additional step, a text-only language model is trained on the union of the benchmark’s training set (if available) and all compromised questions. Questions that this model answers correctly are also removed from the test set. This step is not required for inter-model comparison but further restricts the evaluation to pattern-free questions. 4. Vision-grounded evaluation. All candidate models are evaluated in the original setting (with images provided) on the B-Clean version of the benchmark. 17 Acknowledgement This work was partially supported by the NIH Grants AG089169, AG084471, Stanford HAI Hoffman-Yee Award & GCP Credits, and UST. Mohammad Asadi is supported by the Amazon AI PhD Fellowship and the Stanford HAI Graduate Fellowship. 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