{"id":"56dab1e9-8b92-4913-8f9a-b5439f7e62d1","arxiv_id":"2607.07907","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"low","formal_verification":"none","parameter_count":0,"one_line_summary":"A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.","lead":"This survey organizes multimodal unlearning methods for vision, language, video, and audio foundation models by intervention stage rather than by algorithm family. It gives practitioners a shared map of deletion strength, retention, efficiency, and robustness trade-offs for selective forgetting without full retraining.","discovery_kind":"review","skeptic_critique":{"model":"grok-4.5","headline":"The taxonomy's claimed superiority for cross-modal comparison and trade-off clarification is asserted, not demonstrated.","rationale":"The reader's weakest_assumption correctly isolates the load-bearing premise: that organizing by intervention stage/control pathway is a more stable and useful scaffold than algorithm-centric alternatives. That premise is asserted by contrast (Introduction, Table 1) rather than validated. My concern is the same one, sharpened: the Abstract and contribution bullets treat the taxonomy as enabling systematic comparison and clarifying trade-offs, yet no agreement study, user study, or quantitative trade-off analysis is provided, and audio/video coverage remains thin by the authors' own admission. Because the paper is a survey whose primary novelty is organizational, this is the single place where the central claim is least secure. The concrete test (inter-annotator placement + predictive power on published trade-offs) would settle whether the claim lands or whether the work is best read as a useful but unvalidated map. That does not overturn the CONDITIONAL verdict; it confirms it. No stronger internal inconsistency or factual error is present, and the repository plus extensive tables remain genuine strengths. Verdict therefore stays CONDITIONAL; agreement with the reader is full.","tokens_in":36061,"tokens_out":697,"duration_ms":7255,"concrete_test":"Have 3–5 independent multimodal-unlearning researchers independently assign 20–30 methods (stratified across modalities and stages) to the paper's taxonomy and to one prior algorithm-centric taxonomy; report Cohen's/Fleiss' kappa and disagreement rate. Separately, for a fixed set of 8–10 methods with published numbers, check whether co-location under the same intervention stage predicts more similar rank-orderings of the five trade-offs (deletion strength, retention, efficiency, reversibility, robustness) than co-location under the prior taxonomy. If kappa is low or predictive gain is near zero, the superiority claim does not hold.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The paper's central claim (Abstract, Introduction, contributions, Table 1) is that a system-first taxonomy by intervention stage and control pathway (data-side, training-time, architecture-constrained, training-free, decoding-time), with instance- vs concept-level forgetting as the top split, enables systematic comparison across vision/language/video/audio and clarifies trade-offs among deletion strength, retention, efficiency, reversibility, and robustness. That claim rests on the untested premise that this scaffold is more stable and useful than prior algorithm-centric taxonomies. The manuscript never shows: (i) inter-annotator agreement or stability of method placement under the new categories, (ii) any controlled comparison in which the taxonomy improves method selection or prediction of the listed trade-offs relative to earlier surveys, or (iii) quantitative evidence that methods sharing a control pathway actually share similar trade-off profiles. Table 1 only marks coverage checkboxes; Figures 1–2 and §3 organize methods but do not measure comparative utility. The authors themselves note thin audio/video coverage and rapid evolution (Limitations), so the asserted cross-modal clarifying power is especially under-supported. Without such validation the strongest claim reduces to a well-organized literature map rather than a demonstrated advance in comparative methodology.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"This survey reviews multimodal unlearning for foundation models spanning vision, language, video, and audio. It formalizes approximate retraining equivalence via an (ε, δ) criterion and a forget/retain objective (Section 2), then organizes methods by forgetting target scope (instance vs concept) and by intervention stage and control pathway: data-side, training-time, architecture-constrained, training-free, and decoding-time (Figures 1–2, Section 3). It compiles datasets and benchmarks (Tables 2–3 and Appendix Tables 4–6), evaluation metrics (Figure 3, Appendix B), applications (Figure 4, Appendix E), open challenges, and future directions, and releases a curated repository. The central claim is that this system-first taxonomy enables systematic cross-modal comparison and clarifies trade-offs among deletion strength, retention, efficiency, reversibility, and robustness relative to prior algorithm-centric surveys (Abstract, Introduction, Table 1).","tokens_in":36328,"tokens_out":1031,"duration_ms":9873,"significance":"If the organizational claim holds, the paper would be a useful reference for a fast-moving area that currently lacks a unified cross-modal map. Strengths include a coherent formal setup in Section 2, a clear intervention-stage taxonomy with representative citations in Section 3, careful compilation of datasets/benchmarks/metrics (Tables 2–3, Appendix B), and an open repository. These assets can help practitioners locate methods by deployment control point and help the community track gaps (especially audio/video). The contribution is primarily organizational and bibliographic rather than a new theorem, algorithm, or empirical result; its lasting value depends on whether the taxonomy is adopted as a stable scaffold.","major_comments":[{"comment":"Abstract, Introduction, and Table 1 claim that the system-first taxonomy “enables systematic comparison” and “clarifies trade-offs” among deletion strength, retention, efficiency, reversibility, and robustness. Figures 1–2 and §3 organize methods by intervention stage, but the manuscript does not demonstrate that methods sharing a control pathway share similar trade-off profiles, nor does it provide any controlled comparison (e.g., method-selection utility, inter-annotator agreement on placement, or stability under re-labeling) against algorithm-centric taxonomies. Without such evidence the strongest claim reduces to a well-organized literature map; either add a compact comparative analysis (even qualitative, with explicit trade-off columns per category) or soften the claim to “organizes methods by intervention stage to facilitate comparison.”","section":null},{"comment":"The title and Abstract advertise coverage “across vision, language, video, and audio,” yet Limitations and the dataset tables show audio and video remain comparatively thin (e.g., Table 2 lists few audio/video entries; many video/audio methods appear only as brief citations in §3.2–3.4). This imbalance is acknowledged but not reflected in the strength of the cross-modal claims. Please either expand the audio/video synthesis with explicit cross-modal transfer lessons, or qualify the title/Abstract so that the primary evidence base (image–text VLMs and diffusion) is transparent.","section":null}],"minor_comments":[{"comment":"Table 1 marks “Ours ACL’26” while the arXiv header is 8 Jul 2026; clarify venue status (submitted/accepted/under review) to avoid confusion.","section":null},{"comment":"Section 2 introduces image–text pairs then states generalization to video/audio; a short explicit note on how Df/Dr and the (ε, δ) criterion lift to temporal or multi-track modalities would help readers apply the formalization.","section":null},{"comment":"Figure 2 is dense; a small legend or color coding distinguishing instance-level vs concept-level methods would improve readability.","section":null},{"comment":"Appendix B metric definitions are valuable but long; a one-page summary table mapping each metric family to the trade-offs named in the Abstract would better support the “clarifies trade-offs” claim.","section":null},{"comment":"Minor consistency: “V oigt” / “V on dem Bussche” spacing and occasional hyphenation variants (e.g., “text-to-image” vs “text to image”) should be normalized.","section":null}],"recommendation":"minor_revision","confidential_remarks":"The paper is a solid, timely survey with a usable taxonomy and strong bibliographic apparatus. The skeptic’s point about unvalidated taxonomy superiority is fair but, for a survey, is best handled by claim softening and a modest comparative table rather than demanding a user study. Fit for a survey track or special issue is good; main-conference novelty bar may be tighter if the venue expects empirical validation of the organizational claim."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"This is a survey, not a new result. What it actually ships is a system-first map of multimodal unlearning (data-side, training-time, architecture-constrained, training-free, decoding-time) with instance vs concept as the top split, plus dense tables of datasets/benchmarks/metrics and a public Awesome-style repo. That is the useful part.\n\nWhat is new relative to the surveys they list in Table 1 is broader modality reach (they try to include video and audio, not just text-image) and the intervention-stage framing instead of an optimization-objective taxonomy. Section 2 gives a clean formal setup for approximate retraining equivalence and the usual forget/retain objective, then specializes it for VLMs and diffusion. Section 3 and Figures 1–2 place a lot of recent methods into that scaffold without inventing fake categories. Appendices on metrics and applications are careful and usable. Citation pattern looks normal for a survey; self-cites to the authors’ related unlearning work are present but not load-bearing. Math is standard survey formalization, not a new theorem. Data side is compilation, not new measurement.\n\nThe soft spot is exactly the one the stress-test flags, and it is real but not fatal. They assert that the system-first taxonomy enables systematic cross-modal comparison and clarifies trade-offs (deletion strength, retention, efficiency, reversibility, robustness). They never show inter-annotator stability of placements, a controlled comparison against prior taxonomies, or evidence that methods sharing a control pathway share trade-off profiles. Table 1 is coverage checkboxes. Their own Limitations admit thin audio/video coverage and rapid churn. So the strongest claim reduces to “here is a coherent literature map,” not “we validated a better comparative methodology.” That is fine for a survey; it just means you should not over-read the abstract.\n\nWho it is for: people who need a single entry point to multimodal unlearning for governance, privacy, copyright, or safety work, or who want the tables and repo as a starting bibliography. Not for someone hunting a new algorithm or certified deletion result.\n\nI would send it to peer review. It is organized, cites the right literature, and the resource value is real. Referees can push them to tone down the superiority claim and to be more explicit about how thin the audio/video slice still is. Worth engaging if you work in this area; not a must-read if you do not.","headline":"Solid system-first survey of multimodal unlearning with real tables and a repo; the taxonomy is a useful map, not a proven advance over prior algorithm-centric reviews.","tokens_in":36944,"tokens_out":596,"would_cite":true,"duration_ms":7425,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"Selective forgetting in multimodal foundation models can be organized by where you intervene in the pipeline, not by algorithm family, making methods comparable across vision, language, video, and audio.","keywords":["multimodal unlearning","vision-language models","diffusion models","machine unlearning","system-first taxonomy","concept forgetting","evaluation benchmarks","cross-modal privacy"],"falsifier":"If independent annotators cannot place new multimodal unlearning papers into the taxonomy with high agreement, or if practitioners using it do not choose methods with better measured trade-offs (deletion strength, retention, cost) than those using algorithm-centric surveys, the central organizational claim fails.","tokens_in":36931,"feed_emoji":"🧹","tokens_out":970,"duration_ms":14226,"temperature":0.7,"pith_summary":"Multimodal foundation models absorb sensitive, copyrighted, biased, or unsafe cross-modal associations from web-scale training, and full retraining after each deletion request is often impossible. Multimodal unlearning aims to remove specific instances or concepts while keeping the rest of the model useful. This survey argues that the right organizing principle is a system-first taxonomy: methods are grouped by intervention stage and control pathway, with a top-level split between instance-level and concept-level forgetting. That scaffold lets researchers and practitioners compare deletion strength, retention, efficiency, reversibility, and robustness across architectures and modalities. The paper also consolidates datasets, benchmarks, evaluation metrics, applications, and open problems so the field can move toward deployable, accountable unlearning.","feed_headline":"Unlearning multimodal models by where you intervene","feed_subtitle":"A system-first map compares forgetting methods across vision, language, video, and audio","key_machinery":"System-first taxonomy of multimodal unlearning: methods are classified by where and how they intervene in the multimodal pipeline (data path, training, architecture, weight/representation edits, or decoding/conditioning), with instance-level versus concept-level forgetting as the primary scope split.","core_discovery":"A unified, system-oriented view of multimodal unlearning—organized by intervention stage (data-side, training-time, architecture-constrained, training-free, decoding-time) and control pathway, with forgetting target scope as instance versus concept—enables systematic comparison across vision, language, video, and audio and clarifies the practical trade-offs that algorithm-centric taxonomies obscure.","pith_inferences":["If decoding-time and training-free methods remain the only practical options at foundation scale, the field may treat unlearning more as runtime policy control than as true data-deletion guarantees.","Cross-modal leakage implies that unlearning only the text pathway of a VLM is likely incomplete; evaluation that never probes vision or audio recovery of the same concept will systematically overstate success.","A living taxonomy repository is only as valuable as versioned placement rules; without public inter-annotator protocols, the scaffold could fragment as methods multiply.","Sequential and continual unlearning will become the real product requirement: one-shot benchmarks may not predict whether forgotten concepts reappear after fine-tuning or repeated deletions."],"forward_implications":["Method papers can be compared on shared axes—deletion strength, utility retention, efficiency, reversibility, robustness—across VLMs, diffusion models, video, and audio rather than only within one algorithm family.","Deployment choices can target the right intervention point: data hygiene, training edits, architecture freezes, closed-form weight or representation edits, or reversible decoding-time controls.","Evaluation and benchmark design should report forgetting, safety/privacy audits, retained utility, adversarial reactivation, and compute together instead of single proxy scores.","Open problems—certified deletion, sequential unlearning, cross-modal leakage, frontier-scale models, and unified benchmarks—become shared research targets rather than isolated modality-specific issues.","Governance use cases (privacy/RTBF, safety, copyright, fairness, personalization, backdoor cleanup) can be mapped to the same intervention map for accountable model updates."],"fun_headline_variants":["Multimodal unlearning mapped by intervention stage","System map of forgetting across vision language video audio","Unlearning VLMs LLMs by data train architecture or decode stage","Where interventions remove cross-modal knowledge","Taxonomy of multimodal unlearning by control pathway"],"cache_read_input_tokens":32896,"weakest_assumption_plain":"The claim that grouping methods by intervention stage and control pathway is a more stable and useful scaffold for cross-modal comparison and deployment than earlier algorithm-centric taxonomies is asserted by contrast, not proven by a controlled study of how people actually use the taxonomy.","fun_headline_variants_meta":{"raw":{"variants":["Multimodal unlearning mapped by intervention stage","System map of forgetting across vision language video audio","Unlearning VLMs LLMs by data train architecture or decode stage","Where interventions remove cross-modal knowledge","Taxonomy of multimodal unlearning by control pathway"]},"model":"grok-4.5","effort":"low","cost_usd":0.00314,"raw_usage":{"total_tokens":1072,"prompt_tokens":727,"num_sources_used":0,"completion_tokens":74,"cost_in_usd_ticks":31400000,"prompt_tokens_details":{"text_tokens":727,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":271,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":727,"tokens_out":74,"duration_ms":3434,"temperature":1.0,"reasoning_tokens":271,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-10T15:38:00.687173+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"If independent annotators cannot place new multimodal unlearning papers into the taxonomy with high agreement, or if practitioners using it do not choose methods with better measured trade-offs (deletion strength, retention, cost) than those using algorithm-centric surveys, the central organizational claim fails.","supporting_citations":[],"review_version":1}