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原文摘要

Auxiliary signal pathways in VLMs are routinely fitted with learnable gates so the optimiser can decide how much of the signal to admit. We study what the optimiser decides, and whether the model then uses the signal, with hyperbolic visual relational graphs as the experimental vehicle. We find that under cross-entropy fine-tuning the optimiser decides on zero: the gate closes in every design we trained, across five injection designs on LLaVA-1.5-7B, 15 of 15 runs on Qwen2-VL-7B spanning four task families, 3 of 3 seeds on InternVL3-8B, and with LoRA on all linear layers. We attribute this suppression phenomenon to two regimes, a dead-gradient regime formalised through the caption-invariance of image-derived signals and measured directly, and a negative-utility regime in which the auxiliary signal actively hurts the loss. A closed gate does not by itself mean an inert pathway, so we ablate each pathway at inference under one protocol with real region proposals: the norm-aligned designs are behaviourally closed (at most 1.5pp change at the learned gate), whereas the un-normalised gated prefix is open and harmful, costing 6.7pp on relational GQA at its learned gate of +0.005 while helping SugarCrepe. Evaluating every configuration under one inference mode shows that an active injected pathway costs up to 15pp on relational GQA and 7-12pp on out-of-distribution VSR, that the geometric training losses give no measurable in-distribution gain once the pathway is dropped, and that a Euclidean containment control ties the hyperbolic one. Embedding-norm alignment remains necessary for prefix injection that preserves generation. We recommend testing signal use by ablation rather than by reading gate values or attention mass. We open-source the implementation, the checkpoints and the result files behind every table at https://github.com/mrfarazi/the-gate-always-closes.