{"id":"bd668ed0-e828-4435-8edc-922fbc66b807","arxiv_id":"2605.28427","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"A VAE-based latent diffusion model trained on incomplete data maintains sample quality and imputation performance up to 50% missingness while pixel-space diffusion degrades.","lead":"The paper proposes a two-stage method that first trains a VAE to extract features from incomplete data and then runs diffusion in that latent space for imputation. Practitioners working with missing data in images or similar domains might read it to see whether latent representations reduce the problems that arise when diffusion models are trained directly on zero-filled incomplete inputs.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"VAE on zero-imputed incomplete data may distort latents, undermining claim that latent diffusion is inherently more robust","rationale":"The reader's weakest assumption is identical to the load-bearing point identified above. Because the full manuscript was not supplied in the query, the concern cannot be checked against actual equations, training details, or ablations, leaving the verdict appropriately UNVERDICTED; the proposed test would directly resolve whether the VAE stage is the hidden source of the reported stability.","tokens_in":1702,"tokens_out":339,"duration_ms":20222,"concrete_test":"Retrain the VAE stage on fully observed data only, then encode the same 50%-MCAR test inputs (zero-imputed) and compare (a) latent-space reconstruction FID and (b) downstream diffusion sample quality against the original zero-imputed VAE; a large gap at 50% missingness falsifies the undistorted-latent assumption.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that the first-stage VAE produces a latent space whose semantic features remain sufficiently undistorted under MCAR up to 50% missingness. The abstract states a 'robust VAE-based imputer' is trained on incomplete observations, but the only handling mentioned is zero-imputation (implied by the later reference to 'artifact amplification from zero-imputed inputs'). If zero-imputation biases the encoder toward learning missingness patterns rather than true semantics, the diffusion model is effectively trained on a corrupted prior; any observed stability would then be an artifact of the VAE stage rather than evidence for latent-space diffusion itself.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes a two-stage framework for missing-data imputation with diffusion models under MCAR: a VAE-based imputer is first trained on incomplete (zero-imputed) observations to produce compact latent representations, after which a diffusion model is trained in that latent space. Controlled comparisons against pixel-space diffusion are reported to show that the latent approach maintains sample quality and stability up to 50% missingness while pixel-space diffusion degrades, and yields better downstream imputation performance. The work concludes that latent-space modeling mitigates zero-imputation artifacts and supplies a more robust generative prior.","tokens_in":1825,"tokens_out":453,"duration_ms":31012,"significance":"If the central empirical claims are supported by rigorous quantitative results, ablations, and controls, the paper would demonstrate a practical route to improving diffusion-model robustness on incomplete data by shifting the generative process to a learned latent space. This addresses a common real-world limitation and could be useful for practitioners. The controlled comparison setup is a methodological strength.","major_comments":[{"comment":"Methods / VAE stage: The central claim that latent diffusion itself confers robustness (rather than the VAE stage) requires that the VAE encoder produces semantically undistorted latents when trained on zero-imputed inputs with up to 50% MCAR missingness. No analysis, ablation, or diagnostic (e.g., comparison of latent statistics or reconstructions on complete vs. zero-imputed data, or alternative VAE imputation strategies) is supplied to substantiate this; without it the observed stability could be an artifact of the first-stage imputer rather than evidence for latent-space diffusion.","section":"Methods / VAE training description"}],"minor_comments":[{"comment":"Abstract: performance advantages are asserted without any numerical metrics, error bars, dataset sizes, or baseline details, which hinders immediate evaluation of effect sizes.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The absence of quantitative results, error bars, and dataset specifications even after the abstract makes it difficult to judge whether the experimental claims can be assessed at all; this may indicate an incomplete manuscript rather than a scope issue for the journal."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comment. We agree that the current manuscript does not provide direct diagnostics isolating the VAE stage from the latent diffusion stage, and we will add the requested analyses in revision to strengthen the central claim.","responses":[{"response":"We agree that the manuscript lacks explicit diagnostics on the VAE encoder under missing data, which is needed to attribute robustness specifically to latent-space diffusion. In the revised version we will add: (1) comparison of latent mean/variance statistics and t-SNE visualizations for VAEs trained on complete data versus zero-imputed data at 10-50% MCAR; (2) reconstruction PSNR/SSIM on held-out complete test images when the VAE is trained only on incomplete observations; (3) an ablation replacing the VAE imputer with mean imputation or a simple linear autoencoder before latent diffusion. These additions will clarify whether the observed stability arises from the latent diffusion prior or from the first-stage imputer.","revision_made":"yes","referee_comment":"The central claim that latent diffusion itself confers robustness (rather than the VAE stage) requires that the VAE encoder produces semantically undistorted latents when trained on zero-imputed inputs with up to 50% MCAR missingness. No analysis, ablation, or diagnostic (e.g., comparison of latent statistics or reconstructions on complete vs. zero-imputed data, or alternative VAE imputation strategies) is supplied to substantiate this; without it the observed stability could be an artifact of the first-stage imputer rather than evidence for latent-space diffusion."}],"tokens_in":1323,"tokens_out":322,"duration_ms":21342,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is a two-stage setup: a VAE imputer trained on incomplete data, followed by diffusion in the resulting latent space. They run the same incomplete-data regime against a pixel-space diffusion baseline and report that the latent version holds sample quality up to 50% missingness while the pixel version degrades.\n\nThe controlled comparison across missing rates is the part that works. It directly tests the practical question of whether moving diffusion off the raw data helps when zero-imputation artifacts are present. That framing is honest about a real issue in the area.\n\nThe soft spots are the lack of any results. The abstract states performance advantages but gives no metrics, error bars, datasets, or ablation details, so the central claim cannot be checked. The stress-test concern about the VAE stage is on target: if the encoder is trained on zero-imputed inputs, its latents may simply encode the missingness pattern rather than the underlying semantics. The abstract calls the VAE \"robust\" but does not show that the latents remain undistorted, which means any observed stability could be coming from the first stage instead of the diffusion step itself.\n\nThis is for people already working on diffusion-based imputation. A reader in that niche might pick up the comparison if the full paper supplies the missing numbers and controls. It does not change broader theory.\n\nI would send it to peer review because the experimental question is the right one and the design is straightforward, even though the current version needs the results section to be evaluable.","headline":"The paper runs a controlled comparison of latent vs pixel diffusion for MCAR imputation and claims better stability, but supplies zero numbers so the robustness claim stays unverified.","tokens_in":2316,"tokens_out":387,"would_cite":false,"duration_ms":31944,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Shifting diffusion to a VAE latent space keeps sample quality stable up to 50% missingness while pixel-space diffusion degrades","keywords":["latent diffusion","missing data imputation","VAE","diffusion models","MCAR","incomplete data","generative models"],"falsifier":"Run both the latent and pixel-space diffusion models on the same datasets with exactly 50% MCAR missingness and check whether the latent model still shows higher sample quality and better imputation metrics than the pixel-space baseline.","tokens_in":2590,"feed_emoji":"","tokens_out":629,"duration_ms":29101,"temperature":0.7,"pith_summary":"The paper tests a two-stage approach for generative modeling with missing data: first a VAE learns compact features from incomplete observations, then a diffusion model is trained in that latent space. Controlled experiments under MCAR corruption show the latent version holds sample quality and stability even at 50% missing rates, whereas direct pixel-space diffusion worsens steadily as missingness grows. The latent model also delivers stronger results on downstream imputation tasks. This occurs because the VAE reduces the effect of simple zero-imputation artifacts before diffusion begins.","feed_headline":"Latent diffusion stays stable to 50% missing data","feed_subtitle":"Pixel-space diffusion degrades with rising missingness, while latent-space training holds quality and improves imputation results.","key_machinery":"The latent space of a VAE-based imputer trained on incomplete data, which serves as the training domain for the diffusion model to learn a generative prior without direct exposure to zero-imputed artifacts","core_discovery":"A VAE-based imputer first extracts semantic features from incomplete observations, after which diffusion operates in the resulting latent space; this two-stage model maintains high sample quality and remains stable up to 50% missingness under MCAR, while pixel-space diffusion degrades progressively, and latent diffusion yields consistently better downstream imputation performance.","pith_inferences":["The same two-stage structure could be tested on missingness mechanisms other than MCAR to check whether the stability advantage persists.","Replacing the VAE imputer with alternative feature extractors might further reduce distortion in the latent space before diffusion training.","The observed robustness suggests latent diffusion could be applied to related incomplete-data problems such as noisy observations or partial sensor readings."],"forward_implications":["Latent diffusion maintains high sample quality and stability up to 50% missingness under MCAR.","Pixel-space diffusion degrades progressively as the missingness rate increases.","Latent diffusion achieves consistently better performance than pixel-space diffusion on downstream imputation tasks.","Latent-space modeling mitigates artifact amplification that arises from zero-imputed inputs."],"fun_headline_variants":["Latent diffusion stable under 50% MCAR with VAE","Pixel-space models degrade with increasing missingness","Latent training preserves quality up to 50% missing data","Better imputation from latent diffusion over pixel space"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"A standard VAE trained on zero-imputed or otherwise handled incomplete observations produces a latent space whose semantic features remain sufficiently undistorted for the diffusion model to learn effectively.","fun_headline_variants_meta":{"raw":{"variants":["Latent diffusion stable under 50% MCAR with VAE","Pixel-space models degrade with increasing missingness","Latent training preserves quality up to 50% missing data","Better imputation from latent diffusion over pixel space"]},"model":"grok-4.3","cost_usd":0.006084,"raw_usage":{"total_tokens":2849,"prompt_tokens":616,"num_sources_used":0,"completion_tokens":62,"cost_in_usd_ticks":60837000,"prompt_tokens_details":{"text_tokens":616,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2171,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":616,"tokens_out":62,"duration_ms":27543,"temperature":1.0,"reasoning_tokens":2171,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T14:23:08.951762+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Run both the latent and pixel-space diffusion models on the same datasets with exactly 50% MCAR missingness and check whether the latent model still shows higher sample quality and better imputation metrics than the pixel-space baseline.","supporting_citations":[],"review_version":1}