Reply to Reviewer btN3

Thank you for the careful review. Both of your points are fair, and we ran new analyses for each. Tables and details are on an anonymous page: https://memorizon-rebuttal.pages.dev/

Q1. How often does a scored chunk actually retrieve a relevant first visit? (https://memorizon-rebuttal.pages.dev/e1/)

We replayed the training sampler (380 segments, 4,000 spans per setting, ten scored chunks each) with the training retrieval score. A latent counts as relevant if it sees at least half of the chunk's view frustum (0.3 and 0.7 give the same picture).

Longer spans help because more first visits are in range and the retrieved ones are older. Retrieval itself does not get better: given that a visit exists, top-6 finds it about 90% of the time at every span.

The 200 s optimum versus the 400 s explanation. We separate them in the revision. "200 s is best" is an observation in our tested setup. "Saturation" is only a hypothesis, and we agree it is not established: raising the cap from 200 s to 400 s also changes the span distribution (spans <= 30 s fall from 10.2% to 5.0%; mean span roughly doubles, 106 s to 206 s). Coverage and span mix are confounded in our runs and we cannot separate them.

Bank content. The paper already tests this for the 100 s model (appendix, Bank Contents): with only the bank's frames replaced at inference, mid-path Revisit falls from 0.440 (retrieved top-K) to 0.207 (random frames from the same history), 0.105 (empty bank) and 0.073 (frames from another episode); Gain falls from 0.319 to 0.109, 0.019 and 0.016. We repeated it for the 200 s and 400 s models (https://memorizon-rebuttal.pages.dev/e2/, with example videos for each arm). With no bank, or a bank from another video, Gain falls to about zero (200 s: 0.359 to 0.041 / -0.002; 400 s: 0.285 to 0.013 / 0.005), and random frames from the same video recover only about a third. So all three models read their retrieved content. This does not explain why 400 s is below 200 s.

Q2. Do the external comparisons hold when realized motion is matched? (https://memorizon-rebuttal.pages.dev/e3/)

We measured camera following on all five rollout seeds and added CaR. CaR follows the path as well as we do (correlation with the prescribed yaw 0.959 vs 0.955-0.958 for Memorizon); LingBot, Matrix-Game, Infinite-World, DreamX and HY-World are clearly worse (0.70-0.89). CaR's implied horizontal field of view is 60 deg, so its camera is narrower, not worse at following.

We then stratified returns by realized yaw error, with a per-video calibration that removes the field-of-view difference. Returns with error <= 10 deg, Revisit (mean over five seeds):

Among well-aligned returns, CaR is within seed noise of our 100 s and 400 s models and about 0.06 Revisit below the 200 s model. The other systems stay far lower, but most of their returns fall in the > 25 deg bin, which is the confound you describe. Fold-back and non-fold-back returns separately give the same picture (<= 10 deg: CaR 0.423 / 0.433; Memorizon 100 s 0.442 / 0.441; 200 s 0.481 / 0.489; 400 s 0.442 / 0.409). The yaw estimate comes from phase correlation of the generated video; it sees rotation, not translation, and is not a pose estimate.

Changes to the paper.

1. The camera-following diagnostic moves from Appendix C.7 into Sec. 4.2, with five seeds and CaR.
2. The cross-system ranking is described as indicative; the within-backbone ablations are the primary evidence for the memory claims.
3. We no longer claim an advantage over CaR in camera following: the two follow the prescribed path equally well.
4. Sec. 4.3 states that 200 s is best in our tested setup and presents saturation only as a hypothesis.

Thank you again; your comments made these claims more careful.