We study visual persistence in interactive video world models. These models rely on a Key-Value (KV) cache as a growing visual memory to carry forward previously generated frames. However, we find that models can no longer reliably address stored content once rollouts extend beyond the training horizon, because temporal Rotary Positional Embedding (RoPE) offsets can exceed the training range and the model struggles to retrieve the relevant visual information through attention. Moreover, naively compressing the cache in the RoPE-rotated space degrades memory by averaging keys with incompatible positional phases. To address this, we propose WorldTrace, a training-free memory framework for long-horizon visual persistence. WorldTrace keeps compressed memory addressable by assigning each summary slot a distinct, in-distribution virtual position. Within this cache, WorldTrace uses the model's own attention to decide which observations to retain verbatim and which redundant ones to merge into compressed summaries, all within one bounded addressable memory bank. We evaluate this ability with LoopBench, a benchmark that tests whether a compressed cache can reconstruct a previously visited scene after a long detour. On LoopBench, WorldTrace improves average DINOv2 recall similarity by 32.6% over a sliding window at the same cache budget, extending visually persistent generation without retraining.

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