VidMem: Measuring Memorization Capacity and Knowledge Displacement in Video-Language Models

How many bits can a video-language model store in its parameters? This capacity constrains corpus fit, knowledge displacement during continued training, and exposure of sample-specific information, yet it has not been measured for visual modalities. Existing studies detect replication through similarity, extraction, or attack success but lack a common unit and capacity limit. We propose VidMem, which binds procedurally generated videos to independent random answer strings of known entropy and measures stored information as the reduction in conditional codelength from a uniform reference. Across multiple sizes of a calibration decoder family, the estimate scales linearly at 2.54 bits per trained parameter over the tested range. The protocol applies to a second decoder family and five frozen pretrained encoders, although the coefficient is architecture- and format-specific. Pooled slopes indicate that a video-keyed association costs 1.28× as many parameters as a text-keyed one; tested ablations associate this gap with perception parameters and key length. A capacity ledger then characterizes unavoidable forgetting relative to a specified ceiling. Across three chunk-entropy ratios, onset is consistent with the stream-specific capacity realized by union training. VidMem therefore gives multimodal memorization an explicit unit and budget.