The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images

The "thinking-with-images" paradigm equips multimodal LLMs with active visual operations such as crop-and-zoom. However, models using these operations often achieve only marginal or negative gains over direct inference at substantially higher token cost. They may also repeatedly crop irrelevant regions and fail on questions that direct inference answers correctly. We ask whether the returned visual evidence causally affects the answer. To answer this question, we formulate visual tool-use as a c

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ethics.ai (6 August 2026), “The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images,” evidence record 17334, https://ethics.ai/record/17334 (originally published by arXiv cs.AI).

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