REVIEW 3 major objections 5 minor 1 cited by
Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models
T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read The paper proposes lifting machine unlearning from tracing training data points to tracing the knowledge and capabilities of foundation models, arguing this should be the default interface for unlearning requests.
desk verdict A clean, honest position paper that reorients FM unlearning toward knowledge-level requests; the case study shows the approach is not there yet, but the framing deserves serious engagement. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central mechanism is a hierarchical knowledge taxonomy used as the request interface, where each unlearning request names a node or leaf such as a dog breed or car model and the unlearner is allowed to translate that request into data-level operations without access to the original training set. In the case study, the unlearner assembles a proxy forgetting set of exemplar images with both fine-grained and coarse-grained labels, then applies gradient-based unlearning built on a bounded hinge-loss gradient ascent that clips when the model no longer misclassifies the forgetting image, plus KL-divergence regularizers that keep the model's uncertainties over coarse parent labels and non-forgotten fine-grained labels close to the original. A quality-utility harmonic-mean score, computed as the retention of parent and sibling classes relative to the forgotten class, measures the trade-off that the whole argument depends on.
What would settle it
Run an unlearning procedure on a concept from a proxy dataset and then confront the model with never-seen images of the same concept from a different source and with paraphrased or adversarial probes; if recognition, representation similarity, or indirect responses still reveal the concept, genuine knowledge erasure has not occurred—this is exactly the failure the paper's own out-of-distribution experiment begins to expose.
Extended reading notes
Core claim
The central claim is normative: foundation model unlearning should be lifted from data-tracing to knowledge-tracing. An unlearning request should consist of a forget set drawn from data or—for most stakeholders—from a knowledge structure such as a taxonomy, with no retention set specified; the unlearner is free to operationalize the request by assembling proxy data, constraints, or auxiliary models. The paper argues this is not merely a convenience but the right level of abstraction, because it matches both the diversity of real unlearning requests and the human brain's tendency to forget specific instances while retaining abstract knowledge. The supporting case study instantiates the paradigm on a vision-language model by unlearning fine-grained object classes, preserving coarse parent classes and other fine-grained classes, and using proxy images with hierarchical labels. The results show that established data-tracing unlearning methods can be repurposed for this setting, but their performance, especially under out-of-distribution evaluation, remains insufficient, which the paper takes as evidence that future unlearning methods should be designed natively for knowledge-level targets.
Load-bearing premise
The whole interface rests on the assumption that a high-level request, such as "stop recognizing this car model," can be turned faithfully into a taxonomy label and a set of proxy images without access to the model's training data; if that translation misses what the requester means, the unlearning is only about the proxy, not the knowledge.
Editorial extensions
If this is right
- If the position is accepted, unlearning requests in the foundation-model ecosystem no longer require requesters to identify training data; regulators, enterprise users, and end users can phrase requests as semantic capability targets.
- The paradigm sets the evaluation question at the knowledge level, so current data-centric metrics must give way to criteria that separate genuine erasure of latent capability from surface-level output suppression.
- Existing data-tracing methods can serve as building blocks, as the case study shows they can be adapted with proxy data and hierarchical labels, but their out-of-distribution failures suggest native knowledge-tracing methods are needed.
- Unlearning difficulty depends jointly on concept granularity and the model's own confidence, so future methods and benchmarks should stratify by both factors.
- Knowledge-tracing unlearning could underpin lifelong learning in foundation models, freeing capacity for new knowledge acquisition just as selective forgetting does in humans.
Reading between the lines
- Editorial inference: if proxy data and taxonomy mapping are the operative implementation, the practical bottleneck shifts from unlearning algorithms to knowledge engineering—how faithfully a concept can be pinned to taxonomies, images, and negative boundaries.
- Editorial inference: the case study's out-of-distribution failure suggests a strong test for any claimed knowledge unlearning: a genuinely unlearned concept should be unrecoverable under paraphrased prompts, adversarial examples, and representation-level probes, not merely misclassified in a closed test set.
- Editorial inference: knowledge-tracing could give regulators an auditable surface, such as certifying that a model no longer recognizes a particular entity, but it may create tension with data-tracing privacy rights because removing a concept is broader and less verifiable than removing a specific data point.
- Editorial inference: extending from visual taxonomies to language models would likely require relational knowledge graphs rather than trees, since facts and reasoning patterns do not fit a two-level hierarchy.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This position paper argues that machine unlearning for foundation models should be lifted from data-tracing (removing specific training points) to knowledge-tracing (removing concepts, capabilities, or knowledge expressed as high-level requests). The authors motivate the shift by noting that many stakeholders—regulators, enterprise users, product teams—lack access to training data, and by analogy to human forgetting at the semantic level. They identify three challenges: formulating knowledge-level requests, fixing their boundaries, and quantifying unlearning success. As a concrete illustration, they present a CLIP case study where unlearning targets are fine-grained classes (e.g., dog breeds) and the unlearner assembles proxy image-label sets from public datasets without access to CLIP's training data. They adapt several data-tracing baselines and propose HGA+KL, a hinge-gradient-ascent method with two KL regularizers. Results show strong in-distribution forgetting and good utility on the proxy datasets, but the paper's own out-of-distribution evaluation (Table 6) shows that all methods, including HGA+KL, fail to remove the targeted concept in a different image distribution.
Significance. If the paradigm were successfully instantiated, it would reframe the interface and evaluation of foundation-model unlearning, making requests accessible to parties without data access. The paper's strengths are its clear problem framing, the honest reporting of the OOD failure, and the release of code. Yet the only empirical demonstration shows that the advocated knowledge-level unlearning is not achieved: in-distribution forgetting does not transfer out-of-distribution. The paper is transparent about this, but it means the central normative claim rests primarily on plausibility arguments and future work rather than on a working instantiation. The position itself is valuable for the community, and the negative result is a useful challenge finding, but the current manuscript oversells the case study as illustrating the instantiation of the paradigm.
major comments (3)
- [5.2, Table 6] The OOD results are the paper's own strongest evidence against the central claim. Table 6 reports Q-U of 42.58 for HGA+KL and 51.37 for NPO+KL after unlearning on ImgnetDogs and evaluating on OxfordPet, meaning the targeted dog-breed concept is largely retained in the new distribution. The paper states that 'all data-tracing MU methods, including ours, fail to tackle knowledge-tracing MU.' This negative result is a major load-bearing point: it shows that the concrete case study does not demonstrate knowledge-level unlearning, only distribution-bound association removal. For a position paper this is not automatically fatal, but the manuscript should either (a) present a successful knowledge-tracing instantiation, or (b) explicitly reframe the case study as a negative result that motivates the need for new methods, and discuss what a knowledge-level evaluation would require beyond in-distribution accuracy.
- [A.6.2, Table 17] The HGA+KL row in Table 17 is numerically identical to the NPO+KL row across all five datasets and the average (92.26, 78.91, 90.95, 93.10, 75.61, 86.17). This is almost certainly a transcription or copy-paste error. Because the paper claims HGA+KL is the proposed method, this duplication undermines the reliability of the CompCars-S generalization results and must be corrected.
- [A.2 and A.3] The experimental setup selects unlearning targets based on CLIP's own accuracy. In A.2, CompCars-S categories are retained only if a pre-trained CLIP model achieves accuracy above 20%, and in A.3 fine-grained classes are selected for unlearning only if CLIP accuracy exceeds 90%. This is a deliberate design choice, but it biases the evaluation toward concepts the model already recognizes strongly and makes the difficulty distribution non-representative. The paper should discuss how this selection affects the difficulty levels and the generality of the conclusions, and it would be useful to report results for a fixed, adversarially selected target set.
minor comments (5)
- [Eq. (3)] The notation in Eq. (3) is ambiguous: the sum is over (x_i, y_i) in D_h^f with y ≠ y_i, but y is not bound in the preceding text. Please clarify that the KL divergence is taken over fine-grained classes not in the forgetting set.
- [Tables 2–6] All results are reported as point estimates from a single run. For claims such as HGA+KL being 'nearly 3% higher' than NPO+KL in Table 2, add multiple seeds, standard deviations, or a significance test.
- [Table 17 and Table 16] The row labels 'Origin CLIP' should be 'Original CLIP' for consistency; there are also occasional inconsistencies between 'GDiff' in the text and 'GDiff' in tables.
- [Introduction] The term 'knowledge tracing' is already established in educational data mining with a different meaning. The paper should acknowledge this existing usage and justify the reuse of the term in the unlearning context.
- [A.1, Table 7] The table lists related work but does not include the proposed distinction between data-tracing and knowledge-tracing in the target column; adding this would make the table more directly informative for the paper's argument.
Circularity Check
No significant circularity; the position paper's case study is transparent about its OOD failure, and the central claim is not derived from the fitted proxy data.
full rationale
The paper is a position paper whose central normative claim—that knowledge-tracing should be the primary interface for foundation-model unlearning—is argued from practical access constraints and cognitive analogy rather than derived from the case study. The case study is explicitly framed as illustrative: Section 1 states 'we stress that this case study is meant to support our position and spark discussion rather than provide a definitive solution to the challenges.' The unlearning target is operationalized as fine-grained class labels with a proxy dataset, and evaluation measures accuracy drops on those classes. Although the proxy set is used both for unlearning and for in-distribution evaluation, the paper includes a held-out out-of-distribution test on OxfordPet and transparently reports failure: Section 5.2 says 'all data-tracing MU methods, including ours, fail to tackle knowledge-tracing MU, which underscores the limitations of current data-tracing MU methods.' This is an acknowledged limitation, not a hidden circular reduction. The only model self-reference is using CLIP's own accuracy to select recognizable target classes in Appendix A.3, which is a deliberate experimental design choice and does not make the unlearning result true by construction. Generalization is checked on external datasets such as CIFAR-100 and Food101, so the evaluation is not contained within the fitted inputs. No load-bearing self-citation chain or imported uniqueness theorem appears. No specific circular step can be exhibited from the paper's equations or claims.
Assumptions & free parameters
free parameters (5)
- margin m in HGA loss =
2
- alpha_c, KL regularizer weight for coarse labels =
10 (for HGA+KL); 5 for other methods
- alpha_f, KL regularizer weight for fine retained labels =
20
- Difficulty thresholds for unlearning target selection =
Difficult: accuracy > 90%; Medium: 82%; Easy: 75%
- CLIP accuracy filter for CompCars-S subset =
20% fine-class accuracy
assumptions (4)
- domain assumption A taxonomy (e.g., WordNet) adequately represents the knowledge to be unlearned.
- domain assumption Proxy data collected from public datasets can stand in for the model's original training data for unlearning purposes.
- domain assumption Human forgetting is a useful model for foundation model unlearning.
- ad hoc to paper Existing data-tracing unlearning methods can be applied to class-level requests by replacing the forgetting set with class-labeled proxy data.
Cite this review
Pith. "Pith review of Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models." pith.science (2026). https://pith.science/paper/W2P34J3F
@misc{pith2026250611253,
author = {Pith},
title = {Pith review of: Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models},
year = {2026},
howpublished = {\url{https://pith.science/paper/W2P34J3F}},
note = {Machine review of arXiv:2506.11253}
}
read the original abstract
Machine unlearning removes certain training data points and their influence from AI models (e.g., when a data owner revokes their consent to allow models to learn from the data). In this position paper, we propose to lift data-tracing machine unlearning to knowledge-tracing for foundation models (FMs). We support this position based on practical needs and insights from cognitive studies. Practically, tracing data cannot meet the diverse unlearning requests for FMs, which may be from regulators, enterprise users, product teams, etc., who have no access to FMs' massive training data. Instead, it is convenient for these parties to issue an unlearning request about the knowledge or capability FMs (should not) possess. Cognitively, knowledge-tracing unlearning aligns with how the human brain forgets more closely than tracing individual training data points does. We further discuss the nontrivial challenges in the knowledge-tracing machine unlearning paradigm. Finally, we provide a concrete case study about a vision-language FM to illustrate how an unlearner might instantiate the knowledge-tracing machine unlearning paradigm. Code is available at: https://1yuwen.github.io/Knowledge-Tracing-MU-Page.
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12 Published in Transactions on Machine Learning Research (May/2026) Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.Advances ...
2026
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[2022]
Knowledge sanitization of large language models.arXiv preprint arXiv:2309.11852,
Yoichi Ishibashi and Hidetoshi Shimodaira. Knowledge sanitization of large language models.arXiv preprint arXiv:2309.11852,
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[2023]
Microsoft coco captions: Data collection and evaluation server.arXiv preprint arXiv:1504.00325,
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server.arXiv preprint arXiv:1504.00325,
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[2024]
Kebench: A benchmark on knowledge editing for large vision-language models.arXiv preprint arXiv:2403.07350, 2024a
Han Huang, Haitian Zhong, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. Kebench: A benchmark on knowledge editing for large vision-language models.arXiv preprint arXiv:2403.07350, 2024a. Han Huang, Haitian Zhong, Tao Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. Vlkeb: A...
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[2025]
Unlearn what you want to forget: Efficient unlearning for llms.arXiv preprint arXiv:2310.20150,
Jiaao Chen and Diyi Yang. Unlearn what you want to forget: Efficient unlearning for llms.arXiv preprint arXiv:2310.20150,
Reviewed August 7, 2026 · model on record in the stance chip above.
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