Seen, Said, or Forgotten? A Causal Audit of Visual KV Memory Across Dialog Turns

Title:Seen, Said, or Forgotten? A Causal Audit of Visual KV Memory Across Dialog Turns

Abstract:Stateful multimodal assistants encode an image once but may answer questions about it many turns later. Attention-guided visual-KV eviction assumes that evidence irrelevant now will remain dispensable, although future questions are unknown. We ask when a visual fact is actually safe to forget and introduce the Causal Visual Memory Audit (CVMA), a paired single-prefill framework that tests what later answers lose when a visual region, the whole image, or prior assistant text becomes unavailable. On VisDial and ConvBench, current attention can rank future-useful regions worse than random even though a diagnostic marginal-utility control shows substantial selection headroom. Aggregate scores hide this failure when later turns do not need vision; controlled and stock-generated histories reveal a second escape route, in which assistant-text KV replaces image KV for facts already stated but not reliably for unstated facts. In the tested stacks, safe forgetting is supported by low future visual dependence or fact-specific verbalization---not by low current attention.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2607.25467 [cs.CV]

(or arXiv:2607.25467v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.25467 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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