Mind the Gap: Diagnosing Constraint Discovery Failures in Text-in-Image Editing

Rui Gui

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arXiv · 2606.15982

Mind the Gap: Diagnosing Constraint Discovery Failures in Text-in-Image Editing

Rui Gui

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Abstract

A key challenge in multimodal reasoning is determining which visual dependencies become relevant under a specific task, rather than merely recognizing visible content. We study this through edit-induced constraint discovery in text-in-image editing, a controlled diagnostic setting where a local text change can activate secondary consistency constraints: given a valid editing instruction and an image, can a model identify the secondary regions that must also change? Across 461 diagnostic cases, four MLLMs, and 19 constraint subtypes, models recover only 46% case-level macro recall under unguided prompting versus 94% when constraints are explicitly provided, suggesting that a substantial portion of the failure arises when models must decide which unstated dependencies to surface. Oracle-field decomposition shows that case-specific causal explanations are the most effective partial guidance (0.782 recall), above region names (0.610) or type labels (0.646), suggesting that edit-specific causal cues account for much of the oracle gain. A downstream experiment further shows that higher self-discovery recall does not necessarily improve task performance: unverified self-discovery introduces false positives that offset recall gains, motivating precision-aware constraint elicitation.

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Rui Gui. 2026-06-14. Mind the Gap: Diagnosing Constraint Discovery Failures in Text-in-Image Editing. https://arxiv.org/abs/2606.15982

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