Reply to Reviewer MjTC

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[PENDING: G1]

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Thank you for the review. Tables and details: https://memorizon-rebuttal.pages.dev/

Noise or dropout on clean bank latents. We had tried it once. Noise up to sigma 0.5 on 85% of samples raised seen Revisit slightly (0.405 -> 0.42-0.44) but cut quality (0.560 -> 0.49), and sweeping sigma at inference changed nothing, which suggests the noise was too strong. [PENDING: G1] We trained a milder variant at 200 s (sigma <= 0.2, half of the samples clean): [fill in Revisit and VBench quality against the plain 200 s model].

Co-visibility ignores occlusion; multi-room scenes. You are right: frustum overlap measures containment, not visibility, so two rooms separated by a wall can look overlapping. This is listed in Limitations (Appendix A.4). We also measured it (https://memorizon-rebuttal.pages.dev/e7/). The UE capture has no usable depth, so we used monocular depth (Depth Anything 3) with the known poses, on 399 chunks sampled as in training (spans up to 200 s). Among each chunk's top-6 retrieved frames, 24-49% of the content that falls inside the frustum is occluded (depending on the depth tolerance, tau = 0.25 to 0.1). For random frames passing the same frustum test it is 38-65%. For 79-94% of the chunks that have a frustum hit, at least one retrieved frame has visible overlap >= 0.3. This is indirect evidence: the depth is monocular, not ground truth, and tau is our choice. Retrieval also uses a weighted pose distance, which discourages far-away frames. We did not run a dedicated multi-room test.

O(n) scoring cost (https://memorizon-rebuttal.pages.dev/e5/). Correct, and we clarify that only attention cost is constant. Measured on one CPU thread: 32 ms per chunk at 400 s of history and 70 ms at 800 s. At 400 s that is about 1% of the 3.2 s needed to generate a chunk. A spatial index would make it sub-linear; we have not needed one at these lengths.

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Thank you for the review. Tables and details: https://memorizon-rebuttal.pages.dev/

**Noise or dropout on clean bank latents.** We had tried it once. Noise up to sigma 0.5 on 85% of samples raised seen Revisit slightly (0.405 -> 0.42-0.44) but cut quality (0.560 -> 0.49), and sweeping sigma at inference changed nothing, which suggests the noise was too strong. [PENDING: G1] We trained a milder variant at 200 s (sigma <= 0.2, half of the samples clean): [fill in Revisit and VBench quality against the plain 200 s model].

**Co-visibility ignores occlusion; multi-room scenes.** You are right: frustum overlap measures containment, not visibility, so two rooms separated by a wall can look overlapping. This is listed in Limitations (Appendix A.4). We also measured it (https://memorizon-rebuttal.pages.dev/e7/). The UE capture has no usable depth, so we used monocular depth (Depth Anything 3) with the known poses, on 399 chunks sampled as in training (spans up to 200 s). Among each chunk's top-6 retrieved frames, 24-49% of the content that falls inside the frustum is occluded (depending on the depth tolerance, tau = 0.25 to 0.1). For random frames passing the same frustum test it is 38-65%. For 79-94% of the chunks that have a frustum hit, at least one retrieved frame has visible overlap >= 0.3. This is indirect evidence: the depth is monocular, not ground truth, and tau is our choice. Retrieval also uses a weighted pose distance, which discourages far-away frames. We did not run a dedicated multi-room test.

**O(n) scoring cost (https://memorizon-rebuttal.pages.dev/e5/).** Correct, and we clarify that only attention cost is constant. Measured on one CPU thread: 32 ms per chunk at 400 s of history and 70 ms at 800 s. At 400 s that is about 1% of the 3.2 s needed to generate a chunk. A spatial index would make it sub-linear; we have not needed one at these lengths.