arXiv:2511.21397v2 Announce Type: replace-cross Abstract: How does irrelevant information (i.e., distractors) affect test-time scaling in vision-language models (VLMs)? Prior work on text-only language models has shown that textual distractors can intensify inverse scaling, causing models to reason longer but less effective reasoning traces. In this work, we investigate whether similar phenomena arise in multimodal settings. We introduce Idis (Images with distractors), a visual question-answering dataset that systematically varies distractors along semantic and numerical dimensions. Our analys
The rapid advancement and deployment of large vision-language models necessitates understanding their robustness and limitations before widespread integration into critical systems.
Sophisticated readers should care because understanding how distractors affect VLM reasoning reveals critical vulnerabilities for real-world deployments and highlights areas for foundational AI research.
This research provides empirical evidence of how irrelevant visual information compromises VLM performance, extending previous findings from text-only models to multimodal AI, and enabling better model evaluation and development.
- · AI Safety Researchers
- · VLM Developers (focused on robustness)
- · Academia (computer vision & NLP)
- · Companies deploying brittle VLMs
- · Models without robust attention mechanisms
Research into designing more robust VLM architectures less susceptible to distractors will accelerate.
New benchmarks and evaluation metrics explicitly testing distractor robustness will become standard for VLM development.
The commercial adoption of VLMs in high-stakes environments will be contingent on certified resistance to irrelevant inputs, driving demand for explainable and robust AI solutions.
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