- [VERIFY: one sentence per axis on how CaM and WorldMem build samples, retrieve and supervise, checked against Sec. 2 and their papers.]
- [VERIFY and fill: how the 20 web photos were chosen (style mix, resolution filter, the ten replaced for low light or upscaling); how the 400 s walks were designed (90-degree turns, out-and-back, returns to the start); why existing benchmarks do not fit: they lack continuous camera paths from one photograph with returns minutes later.]
Preview
Thank you for the review. Details and tables: https://memorizon-rebuttal.pages.dev/
Novelty relative to Context-as-Memory and WorldMem.
We agree that sampling from long videos, geometry-based retrieval and conditioning on a few memory frames are not new in themselves. What Memorizon adds is how they are combined in training:
- Span is a sampled variable. Each sample draws a span of m ~ U[9, m_max] chunks (10 s up to 100 / 200 / 400 s). The intervening history is never tokenized.
- Per-chunk retrieval with a shared union bank. Each of the 10 scored chunks takes its own top-6 by frustum overlap minus pose distance. The union (at most 60 slots) forms one shared bank, and each chunk attends only to its own six.
- Loss on the last 10 chunks only. The sequence length is bounded by 1 + bank + recent + targets, whatever the span.
[VERIFY: one sentence per axis on how CaM and WorldMem build samples, retrieve and supervise, checked against Sec. 2 and their papers.]
The closest controlled comparison we have is on one backbone. Replacing per-chunk retrieval with one retrieval for the whole scored block (the analogue of per-clip retrieval) lowers web-photo Revisit from 0.434 to 0.302. Sharing one RoPE index across the bank lets a model trained on 10 s read frames 59 s old. We have reworded the "first to decouple" claims in Sec. 1 to the union-bank mechanism.
Computational cost (https://memorizon-rebuttal.pages.dev/e5/).
- Training: step time 8.02 s -> 9.00 s from 100 s to 400 s spans, peak memory flat (Table 10).
- Bank size: the union is bounded by n_scored x K = 60 slots at training time. At inference one chunk is generated at a time, so the bank is that chunk's own top-6 (6 slots), not kK. Per-chunk retrieval does not enlarge the sequence at inference.
- Retrieval scoring (pose only, one CPU thread): 0.65 ms at 10 s of history, 11 ms at 100 s, 32 ms at 400 s, 70 ms at 800 s per chunk.
- Inference along a 400 s rollout (one H200, 20 steps, CFG, median per chunk): sliding window 2.71 s at every history length; Memorizon (200 s model, k = 6) 3.18 s at 10 s of history and 3.19 s at 300-400 s. That is +17% over a sliding window and constant in history length. Peak allocated memory is 22.6 GB for both. One video each, no variance estimate.
Direct comparison. We could not run the released CaM / WorldMem models under our protocol (Appendix C.6). Reproducing their training recipes on our backbone is future work; we state this as a limitation. The mechanism-level comparison above and the per-segment-retrieval row of Table 2 are the closest controlled evidence we have.
Evaluation data. [VERIFY and fill: how the 20 web photos were chosen (style mix, resolution filter, the ten replaced for low light or upscaling); how the 400 s walks were designed (90-degree turns, out-and-back, returns to the start); why existing benchmarks do not fit: they lack continuous camera paths from one photograph with returns minutes later.]
Longer horizons. Figure 6 already shows Memorizon's return intervals beyond 60 s. Running the Table 1 baselines to 400 s needs each model's own long-rollout pipeline, which we did not do; we now state this limit explicitly.
We also ran a zero-shot test on real capture (https://memorizon-rebuttal.pages.dev/e6/).