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arXiv AI By Jingtao Lei, Hongji Li, Dexiang Shu

Layer-Wise Gate-Controlled Prompt Truncation in a Multimodal Chest X-Ray Classifier

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The paper presents a layer‑wise gating mechanism for prompt truncation in a multimodal chest X‑ray classifier based on Mixture of Prompt Experts (MoPE). The controller predicts a retention ratio per sample, averages these ratios per mini‑batch, and truncates the static and mixed visual prompts to an integer length, also scaling retained prompts by the individual ratios. In a pilot study, the gated model achieved a best validation accuracy of 0.8996 versus 0.8969 for the fixed‑length baseline, with final values of 0.8963 and 0.8802, while the gate statistics showed a retained length of one token out of a configured maximum of six, reducing the visual sequence from 210 to 200 tokens. "whyItMatters":"The study demonstrates that prompt shortening via a gating strategy can slightly improve validation accuracy in a multimodal chest X‑ray classification task, though it does not yet confirm runtime acceleration or clinical utility."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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