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In this framework, we train a lightweight, interpretable proxy model on the VLM's intermediate representations using an auxiliary localizati","title":"Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders","url":"https://arxiv.org/abs/2607.06445","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.06445v1 Announce Type: cross \nAbstract: Vision-Language Models (VLMs) are increasingly utilized as the conditioning backbone for diffusion-based image editing due to their remarkable multimodal reasoning capabilities. While standalone VLMs demonstrate strong localization capabilities, editing pipelines frequently struggle to maintain this accuracy, particularly in complex, multi-entity scenes. In this work, we investigate this performance gap, hypothesizing that it stems from treating the VLM as a condition encoder. In this role, the model is restricted to a single forward pass, preventing the autoregressive generation process for which it was optimized, thereby failing to fully expose its capabilities. To investigate whether this spatial understanding persists when the VLM is used as a condition encoder, we introduce Analysis-by-Proxy. 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