arXiv:2606.13870v1 Announce Type: cross Abstract: Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided. This mirage behavior inflates benchmark scores without reflecting visual grounding. Prior work treats this as a single failure mode. We argue it is two. Using Mirage Probes, a contrastive probing framework that pairs paraphrased question variants with matched mirage and non-mirage labels on the same image, we show that mirage behavior is linearly decodable from internal activations across residual stream, MLP, post-at
This research is emerging now as the capabilities and limitations of large vision-language models become more apparent and as researchers seek to understand their true 'understanding'.
It highlights a fundamental flaw in how current vision models are evaluated, potentially overstating their real-world applicability and driving a re-evaluation of AI benchmarking standards.
The understanding of VLM 'intelligence' is refined; models will need to be designed to truly ground their responses in visual data rather than relying on textual cues.
- · AI ethicists
- · Developers of robust VLM evaluation techniques
- · Companies with genuinely visually grounded AI models
- · Developers relying solely on current benchmark scores
- · Companies with ungrounded VLM products
- · Benchmarking organizations using flawed metrics
Immediate refocus on developing more rigorous and visually grounded evaluation benchmarks for VLMs.
Increased investment in research that explicitly addresses visual grounding and multimodal fusion within AI architectures.
A potential re-calibration of public and investor expectations regarding the current 'intelligence' of multimodal AI systems.
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Read at arXiv cs.AI