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We identify a recurring failure mode, parse collapse, where the\n  autoregressive decoder produces fluent yet incomplete rankings by silently omitting candidates and terminating early. This\n  failure stems from limited context utilization rather than simple formatting mistakes, making prompt engineering and constrained\n  decoding insufficient. We propose PRISMR (Parameterized Representation Internalization for Semantic Multimodal Ranking), a\n  framework that replaces transient in-context list processing with parametric structural conditioning. PRISMR uses a lightweight\n  hypernetwork to encode multimodal candidates in parallel and generate item-specific LoRA weights, which are synthesized into an\n  instance-specific adapter for a LMM. 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