arxiv: 2604.04982 · v1 · submitted 2026-04-04 · 💻 cs.IR · cs.AI· cs.CL· cs.LG

Recognition: 1 theorem link

· Lean Theorem

CURE:Circuit-Aware Unlearning for LLM-based Recommendation

Ziheng Chen , Jiali Cheng , Zezhong Fan , Hadi Amiri , Yunzhi Yao , Xiangguo Sun , Yang Zhang

Authors on Pith no claims yet

Pith reviewed 2026-05-13 16:43 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.CLcs.LG

keywords LLM unlearningrecommender systemsprivacycircuit analysisgradient conflictLLMRecmodel editing

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The pith

By extracting core circuits for item recommendation and categorizing modules into forget-specific, retain-specific, and task-shared groups, CURE performs unlearning through targeted updates that avoid gradient conflicts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CURE as a way to handle privacy-driven unlearning in LLM-based recommender systems. Existing approaches apply uniform updates across all parameters, which creates conflicts between the goals of forgetting specific user data and retaining overall recommendation quality. CURE first locates the computational subgraphs, called circuits, that drive item recommendation behavior, then measures how each module inside those circuits contributes to forgetting versus retaining. Modules are sorted into three groups and given update rules matched to their group so the two objectives no longer fight during training. Experiments on real-world data show the method produces stronger unlearning while keeping model utility higher than prior baselines.

Core claim

CURE disentangles model components into functionally distinct subsets and selectively updates them: it extracts the core circuits underlying item recommendation, analyzes how individual modules within these circuits contribute to the forget and retain objectives, categorizes those modules into forget-specific, retain-specific, and task-shared groups, and applies function-specific update rules to each group to mitigate gradient conflicts during unlearning.

What carries the argument

Core circuits, computational subgraphs causally responsible for task-specific recommendation behaviors, which are extracted and used to categorize and selectively update individual modules.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same circuit-localization step could be reused for other privacy-sensitive LLM tasks such as personalized dialogue or content filtering.
If circuits prove stable across fine-tunes, periodic circuit re-extraction might become unnecessary, lowering the cost of repeated unlearning requests.
Recommendation behavior in LLMs appears more localized than fully distributed, suggesting future maintenance could target only small subgraphs rather than whole models.

Load-bearing premise

Core circuits for item recommendation can be reliably extracted and modules can be accurately sorted into forget-specific, retain-specific, and task-shared groups based on contribution analysis.

What would settle it

Experiments on the same real-world datasets where CURE fails to show higher unlearning effectiveness than baselines or where circuit extraction yields no distinct module categories.

Figures

Figures reproduced from arXiv: 2604.04982 by Hadi Amiri, Jiali Cheng, Xiangguo Sun, Yang Zhang, Yunzhi Yao, Zezhong Fan, Ziheng Chen.

**Figure 2.** Figure 2: Schematics of CURE: (i) Locating Circuits in LLMREC; (ii)Selective Circuits Updating [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Unlearning effectivenss and model performance on Movie and Goodreads. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Transparent comparison of circuit-aware unlearn [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: The distribution of gradient conflicts ( [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

read the original abstract

Recent advances in large language models (LLMs) have opened new opportunities for recommender systems by enabling rich semantic understanding and reasoning about user interests and item attributes. However, as privacy regulations tighten, incorporating user data into LLM-based recommendation (LLMRec) introduces significant privacy risks, making unlearning algorithms increasingly crucial for practical deployment. Despite growing interest in LLMRec unlearning, most existing approaches formulate unlearning as a weighted combination of forgetting and retaining objectives while updating model parameters in a uniform manner. Such formulations inevitably induce gradient conflicts between the two objectives, leading to unstable optimization and resulting in either ineffective unlearning or severe degradation of model utility. Moreover, the unlearning procedure remains largely black-box, undermining its transparency and trustworthiness. To tackle these challenges, we propose CURE, a circuit-aware unlearning framework that disentangles model components into functionally distinct subsets and selectively updates them. Here, a circuit refers to a computational subgraph that is causally responsible for task-specific behaviors. Specifically, we extract the core circuits underlying item recommendation and analyze how individual modules within these circuits contribute to the forget and retain objectives. Based on this analysis, these modules are categorized into forget-specific, retain-specific, and task-shared groups, each subject to function-specific update rules to mitigate gradient conflicts during unlearning. Experiments on real-world datasets show that our approach achieves more effective unlearning than existing baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CURE splits LLMRec circuits into forget/retain/shared modules for targeted unlearning to dodge gradient conflicts, but the categorization step has no shown robustness checks.

read the letter

The paper's main move is to extract core circuits for item recommendation, score individual modules on their contribution to forget versus retain objectives, and bucket them into forget-specific, retain-specific, or task-shared groups. Each group then gets its own update rule instead of a single weighted loss. That is the concrete difference from earlier LLMRec unlearning work, which just combined forgetting and retaining terms and updated everything uniformly. The framing is cleaner and directly targets the conflict problem the abstract describes. Credit for spelling out the circuit view and the three-way split; it is a structured way to think about what parts of the model actually matter for each goal. The experiments are said to beat baselines on real-world data, which is the right test bed. The soft spot is exactly where the stress-test note points: the whole method stands on the claim that contribution analysis produces stable, accurate partitions. The abstract gives no numbers on how stable those buckets are across runs, no sensitivity plots, and no ablation that swaps in random or noisy categorization to show the selective rules still help. Without that, it is hard to know whether the reported gains come from the circuit logic or from some other detail in the implementation. Circuit extraction details are also missing from the summary, so a reader cannot yet judge how much manual tuning or domain knowledge is baked in. This is the kind of paper that belongs in a reading group focused on privacy or LLM adaptation. People working on unlearning or on recommender systems that must comply with deletion requests will find the framework useful to think with, even if they end up modifying the categorization step. It is worth sending to peer review because the problem is real, the proposed structure is explicit, and the central assumption is falsifiable with the right ablations. A referee can ask for exactly those checks without the paper being dismissed outright.

Referee Report

2 major / 2 minor

Summary. The paper proposes CURE, a circuit-aware unlearning framework for LLM-based recommendation. It extracts core circuits responsible for item recommendation, analyzes module contributions to forget and retain objectives, categorizes modules into forget-specific, retain-specific, and task-shared groups, and applies function-specific update rules to avoid gradient conflicts during unlearning. Experiments on real-world datasets are reported to demonstrate more effective unlearning than baselines.

Significance. If the module categorization proves stable and the selective updates reliably reduce conflicts, the approach could advance transparent unlearning methods for LLMRec by moving beyond uniform parameter updates. The framework's emphasis on circuit extraction and functional disentanglement offers a potential path toward more interpretable privacy mechanisms, though the absence of robustness validation limits its assessed contribution at present.

major comments (2)

[§3.2] §3.2 (Module Categorization): The central claim that categorizing modules into forget-specific, retain-specific, and task-shared groups enables conflict-free updates rests on unvalidated contribution analysis. No sensitivity checks, stability metrics across runs, or ablations under gradient perturbations or data shifts are reported, which directly undermines the reliability of the function-specific rules and reduces the method to standard multi-objective optimization.
[§4] §4 (Experiments): The reported superiority over baselines lacks accompanying quantitative metrics, standard deviations from multiple runs, or ablation studies isolating the effect of the categorization step. Without these, the experimental support for the claim of 'more effective unlearning' cannot be assessed as load-bearing evidence.

minor comments (2)

[Abstract] Abstract: The description of circuit extraction and contribution metrics is high-level; adding one sentence on the precise analysis technique (e.g., gradient-based or activation-based) would improve reproducibility.
[§3] Notation: The terms 'forget-specific', 'retain-specific', and 'task-shared' are introduced without a formal definition or pseudocode for the assignment procedure, which could be clarified in §3.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to strengthen the validation of our module categorization and the robustness of the experimental evidence.

read point-by-point responses

Referee: [§3.2] §3.2 (Module Categorization): The central claim that categorizing modules into forget-specific, retain-specific, and task-shared groups enables conflict-free updates rests on unvalidated contribution analysis. No sensitivity checks, stability metrics across runs, or ablations under gradient perturbations or data shifts are reported, which directly undermines the reliability of the function-specific rules and reduces the method to standard multi-objective optimization.

Authors: We agree that additional validation would strengthen the reliability of the categorization. In §3.2 the modules are categorized by quantifying their individual contributions to the forget and retain objectives within the extracted recommendation circuits; the function-specific update rules are then derived directly from these contribution patterns to avoid gradient conflicts. While the current experiments demonstrate that selective updates yield more effective unlearning than uniform baselines, we acknowledge the absence of sensitivity checks, stability metrics, and perturbation ablations. In the revised manuscript we will add (i) sensitivity analyses varying the contribution thresholds, (ii) stability metrics (e.g., Jaccard overlap of categorized modules) across five independent runs, and (iii) ablations under gradient noise and data shifts. These additions will show that the categorization is stable and that the tailored rules produce conflict reduction beyond what standard multi-objective weighting achieves. revision: yes
Referee: [§4] §4 (Experiments): The reported superiority over baselines lacks accompanying quantitative metrics, standard deviations from multiple runs, or ablation studies isolating the effect of the categorization step. Without these, the experimental support for the claim of 'more effective unlearning' cannot be assessed as load-bearing evidence.

Authors: We thank the referee for highlighting this gap. Section 4 reports that CURE outperforms baselines on real-world datasets in unlearning effectiveness while preserving utility. To make this evidence load-bearing we will revise §4 to include (i) mean and standard deviation results over five independent runs for all metrics, and (ii) an explicit ablation that disables the categorization step (replacing it with uniform updates) while keeping all other components fixed. These additions will isolate the contribution of the circuit-based disentanglement and allow direct assessment of whether the selective rules are responsible for the observed gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity: derivation relies on empirical circuit extraction and module analysis without self-referential reductions

full rationale

The paper defines a circuit-aware unlearning framework that extracts core circuits for item recommendation, performs contribution analysis on modules to categorize them into forget-specific, retain-specific, and task-shared groups, and applies function-specific update rules. This chain is driven by internal model analysis and selective updates rather than any equation or parameter that is fitted to data and then renamed as a prediction. No self-definitional loops appear (e.g., no objective defined in terms of its own output), no fitted inputs are presented as independent predictions, and no load-bearing uniqueness theorems or ansatzes are imported solely via self-citation. The central claim of reduced gradient conflicts and improved unlearning is evaluated via experiments on real-world datasets, keeping the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that LLMs contain extractable circuits causally responsible for recommendation behaviors and that module contributions to forget versus retain objectives can be measured.

axioms (1)

domain assumption LLM-based recommenders contain identifiable computational circuits responsible for task-specific behaviors
Invoked to extract core circuits underlying item recommendation and analyze module contributions.

invented entities (1)

forget-specific, retain-specific, and task-shared module groups no independent evidence
purpose: To enable selective, function-specific parameter updates that avoid gradient conflicts
Derived from contribution analysis of modules within extracted circuits; no independent falsifiable evidence provided in abstract.

pith-pipeline@v0.9.0 · 5570 in / 1093 out tokens · 109953 ms · 2026-05-13T16:43:07.895222+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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unclear
Relation between the paper passage and the cited Recognition theorem.

we extract the core circuits underlying item recommendation and analyze how individual modules within these circuits contribute to the forget and retain objectives. Based on this analysis, these modules are categorized into forget-specific, retain-specific, and task-shared groups

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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Reference graph

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