arxiv: 2605.03759 · v1 · submitted 2026-05-05 · 💻 cs.CV · cs.AI

Recognition: unknown

Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks

JuneHyoung Kwon , MiHyeon Kim , Eunju Lee , JungMin Yun , Byeonggeuk Lim , YoungBin Kim

Authors on Pith no claims yet

Pith reviewed 2026-05-07 17:52 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords unlearninginformationlearningremembenchmarksdiagnosingfoundationallvlms

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The pith

LVLM unlearning benchmarks fail due to initial memorization failures on fictitious data; ReMem benchmark with multi-hop and multi-image scaling plus Exposure metric enables reliable learning and unlearning diagnosis.

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

Large vision-language models combine image understanding with language processing and can store sensitive personal details from training data, creating privacy risks. Researchers test whether these models can unlearn such information using benchmarks built around made-up identities. The paper identifies a core flaw: the models frequently do not learn these identities well enough in the first place, so any later forgetting tests become meaningless. Two main issues are diagnosed: insufficient memorization overall and difficulty handling questions that require connecting facts across multiple steps or images. To address this, the authors created ReMem. It scales up the amount of training data, designs questions that demand reasoning, and uses multiple visual contexts to strengthen initial learning. They also propose the Exposure metric, which examines the model's internal probability distributions to quantify how thoroughly specific information has been removed after unlearning attempts. Experiments indicate that this approach yields more trustworthy evaluations of both learning and unlearning performance.

Core claim

Current unlearning benchmarks attempt to mitigate this using fictitious identities but overlook a critical stage 1 failure: models fail to effectively memorize target information initially, rendering subsequent unlearning evaluations unreliable.

Load-bearing premise

That under-memorization and the multi-hop curse are the root causes of unreliable unlearning evaluations, and that ReMem's data scaling, reasoning-aware QA pairs, and diverse visual contexts will produce robust foundational learning without introducing new evaluation artifacts.

Figures

Figures reproduced from arXiv: 2605.03759 by Byeonggeuk Lim, Eunju Lee, JuneHyoung Kwon, JungMin Yun, MiHyeon Kim, YoungBin Kim.

**Figure 1.** Figure 1: Stage 1 performance comparison across FI view at source ↗

**Figure 2.** Figure 2: Internal state analysis. Left: Scatter plot of Min-k% probability versus Inverse Perplexity (1/PPL) comparing the Real Set with fictitious benchmarks. Middle & Right: Causal tracing heatmaps visualizing internal hidden state activations for a FIUBench sample (Middle) and a Real Set sample (Right). data (Zhou et al., 2025; Kurmanji et al., 2023; Chundawat et al., 2023; Kim et al., 2024). More recently, alig… view at source ↗

**Figure 3.** Figure 3: (a) Impact of QA sample quantity on memorization performance (EM, ROUGE). (b) Correlation between view at source ↗

**Figure 4.** Figure 4: Overview of the ReMem benchmark construction pipeline. view at source ↗

**Figure 5.** Figure 5: Causal tracing heatmaps comparing the in view at source ↗

**Figure 6.** Figure 6: Performance of unlearning methods under LLaVA-1.5-7B across different forget ratios. view at source ↗

**Figure 7.** Figure 7: Performance of unlearning methods under LLaVA-1.5-13B across different forget ratios. view at source ↗

read the original abstract

While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning benchmarks attempt to mitigate this using fictitious identities but overlook a critical stage 1 failure: models fail to effectively memorize target information initially, rendering subsequent unlearning evaluations unreliable. Diagnosing under-memorization and the multi-hop curse as root causes, we introduce ReMem, a Reliable Multi-hop and Multi-image Memorization Benchmark. ReMem ensures robust foundational learning through principled data scaling, reasoning-aware QA pairs, and diverse visual contexts. Additionally, we propose a novel Exposure metric to quantify the depth of information erasure from the model's internal probability distribution. Extensive experiments demonstrate that ReMem provides a rigorous and trustworthy framework for diagnosing both learning and unlearning behaviors in LVLMs.

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

ReMem flags a real under-memorization flaw in LVLM unlearning benchmarks and offers a concrete fix, but the new design needs checks for its own artifacts.

read the letter

The main point is that current unlearning benchmarks for large vision-language models rest on shaky ground because the models often fail to memorize the target fictitious identities in the first place, so later unlearning tests measure something unreliable. The paper calls this a stage-one failure tied to under-memorization and the multi-hop curse. ReMem addresses it with scaled data, reasoning-aware QA pairs, and multiple images per identity to build stronger initial learning, plus an Exposure metric that tracks erasure via internal probability shifts instead of just output accuracy. The abstract reports that experiments show this produces more trustworthy learning and unlearning diagnostics. What the work does well is naming the memorization gap plainly and tying it to why privacy evaluations can overstate success. That diagnosis matches known issues in how fictitious-identity setups are used and gives a practical lever for improvement. The soft spots sit in the benchmark construction itself. Adding reasoning structure and visual diversity could let models succeed by pattern-matching the new templates or contexts rather than truly internalizing the personal facts, which would leave the unlearning evaluation still fragile. The stress-test concern about illusory memorization is worth testing directly against the results. The paper claims the changes produce robust foundational learning, but the abstract alone does not show the controls or ablation numbers needed to confirm that. This is for researchers who build or audit unlearning methods and benchmarks in multimodal models. Anyone designing evaluation protocols will find the specific proposals useful to examine. It deserves a serious referee because the core flaw it identifies affects how privacy claims are assessed, even if the fix requires further validation. I would send it to review with requests for clearer evidence that ReMem forces genuine memorization and that unlearning metrics become more reliable as a result.

Referee Report

2 major / 2 minor

Summary. The paper claims that existing LVLM unlearning benchmarks are unreliable because models fail to initially memorize target fictitious identities (due to under-memorization and the multi-hop curse), and proposes ReMem—a new benchmark using data scaling, reasoning-aware QA pairs, and diverse visual contexts—to ensure robust foundational learning, along with a novel Exposure metric to quantify erasure depth from internal probabilities. Extensive experiments are said to validate that ReMem provides a rigorous framework for diagnosing learning and unlearning behaviors.

Significance. If the central claims hold, this would be a meaningful contribution to the field of LVLM safety and privacy, as reliable unlearning evaluation is a prerequisite for deploying models that handle personal data. The work is credited for introducing a new benchmark and the Exposure metric as independent tools, plus the focus on foundational memorization failures rather than only post-unlearning metrics.

major comments (2)

[§3] §3 (ReMem construction): the claim that reasoning-aware QA pairs and diverse visual contexts produce 'robust foundational learning' without new artifacts is load-bearing for the central thesis, yet no ablation isolates whether high performance stems from true memorization of specific identities versus exploitation of reasoning templates or contextual cues (as raised by the multi-hop structure).
[§4] §4 (Experiments): the validation that ReMem fixes under-memorization relies on the new Exposure metric and performance numbers, but without reported controls for template-pattern exploitation or comparisons to simpler scaling baselines, it is unclear if the improvements are due to the proposed design or other factors.

minor comments (2)

Clarify the exact definition and computation of the Exposure metric early in the paper (currently introduced in the abstract without a formal equation in the main text).
Add a limitations section discussing potential new evaluation artifacts introduced by the reasoning-aware QA format.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful review and for acknowledging the significance of addressing foundational memorization failures in LVLM unlearning evaluation. We address the major comments point by point below. Where the concerns identify gaps in experimental controls, we will revise the manuscript to incorporate the suggested analyses.

read point-by-point responses

Referee: [§3] §3 (ReMem construction): the claim that reasoning-aware QA pairs and diverse visual contexts produce 'robust foundational learning' without new artifacts is load-bearing for the central thesis, yet no ablation isolates whether high performance stems from true memorization of specific identities versus exploitation of reasoning templates or contextual cues (as raised by the multi-hop structure).

Authors: We agree that isolating true identity memorization from potential template or cue exploitation is necessary to support the claim of robust foundational learning without introducing new artifacts. The ReMem design uses principled scaling of multi-hop reasoning-aware pairs and diverse visual contexts specifically to overcome the multi-hop curse and under-memorization documented in existing benchmarks. However, the manuscript does not contain explicit ablations testing generalization to paraphrased templates or novel identity-template combinations. In the revision we will add these ablations, including performance on unseen reasoning structures and controls that hold template patterns constant while varying identities, to demonstrate that gains derive from identity-specific learning rather than surface exploitation. revision: yes
Referee: [§4] §4 (Experiments): the validation that ReMem fixes under-memorization relies on the new Exposure metric and performance numbers, but without reported controls for template-pattern exploitation or comparisons to simpler scaling baselines, it is unclear if the improvements are due to the proposed design or other factors.

Authors: We acknowledge that stronger attribution of improvements to the full ReMem design (rather than generic scaling or metric artifacts) would increase clarity. The current experiments already demonstrate that ReMem yields higher initial memorization and more reliable unlearning signals via the Exposure metric compared with prior benchmarks. To address the concern directly, the revised manuscript will include (i) comparisons against simpler scaling baselines that omit reasoning-aware QA pairs and multi-image diversity, and (ii) additional controls for template exploitation such as evaluation on paraphrased or cross-identity queries. These additions will clarify that the observed gains stem from the proposed combination of elements. revision: yes

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

Central claim rests on the diagnosis that existing benchmarks suffer from under-memorization and multi-hop issues, plus the assumption that ReMem's design principles correct them. Only abstract available, so ledger is minimal; Exposure metric counts as a new invented construct for measuring erasure.

axioms (2)

domain assumption Models fail to effectively memorize target information initially when using fictitious identities in current unlearning benchmarks.
Core diagnosis stated in abstract as root cause of unreliable evaluations.
domain assumption Principled data scaling, reasoning-aware QA pairs, and diverse visual contexts ensure robust foundational learning.
Assumed in the construction of ReMem to overcome the identified failures.

invented entities (1)

Exposure metric no independent evidence
purpose: Quantify the depth of information erasure from the model's internal probability distribution after unlearning.
Newly proposed metric introduced to provide a more precise measure than prior approaches.

pith-pipeline@v0.9.0 · 5459 in / 1438 out tokens · 104247 ms · 2026-05-07T17:52:43.410962+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

52 extracted references · 6 canonical work pages · 2 internal anchors

[1]

Visual Instruction Tuning , author=
[2]

mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration , author=
[3]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities , author=. arXiv preprint arXiv:2507.06261 , year=

work page internal anchor Pith review arXiv
[4]

Knowledge unlearning for mitigating privacy risks in language models , author=
[5]

Text summarization branches out , pages=

Rouge: A package for automatic evaluation of summaries , author=. Text summarization branches out , pages=
[6]

2021 , organization=

Machine unlearning , author=. 2021 , organization=

2021
[7]

2021 , organization=

Right to be forgotten in the age of machine learning , author=. 2021 , organization=

2021
[8]

Eldan, Ronen and Russinovich, Mark , journal=. Who's
[9]

2019 , publisher=

The European Union general data protection regulation: what it is and what it means , author=. 2019 , publisher=

2019
[10]

Maini, Pratyush and Feng, Zhili and Schwarzschild, Avi and Lipton, Zachary Chase and Kolter, J Zico , booktitle=COLM, year=
[11]

Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset , author=
[12]

Clear: Character unlearning in textual and visual modalities , author=
[13]

Protecting privacy in multimodal large language models with mllmu-bench , author=
[14]

Extracting training data from large language models , author=
[15]

The secret sharer: Evaluating and testing unintended memorization in neural networks , author=
[16]

Balesni, Mikita and Korbak, Tomek and Evans, Owain , journal=
[17]

arXiv preprint arXiv:2506.05198 , year=

Quantifying Cross-Modality Memorization in Vision-Language Models , author=. arXiv preprint arXiv:2506.05198 , year=

work page arXiv
[18]

2024 , publisher=

Exploring the landscape of machine unlearning: A comprehensive survey and taxonomy , author=. 2024 , publisher=

2024
[19]

Direct preference optimization: Your language model is secretly a reward model , author=
[20]

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning , author=
[21]

2022 , organization=

Unrolling sgd: Understanding factors influencing machine unlearning , author=. 2022 , organization=

2022
[22]

Decoupled distillation to erase: A general unlearning method for any class-centric tasks , author=
[23]

Towards unbounded machine unlearning , author=
[24]

Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher , author=
[25]

Layer attack unlearning: Fast and accurate machine unlearning via layer level attack and knowledge distillation , author=
[26]

Model sparsity can simplify machine unlearning , author=
[27]

Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation , author=
[28]

Learning to unlearn: Instance-wise unlearning for pre-trained classifiers , author=
[29]

2022 , organization=

Continual learning and private unlearning , author=. 2022 , organization=

2022
[30]

A comprehensive analysis of memorization in large language models , author=
[31]

arXiv preprint arXiv:2505.24832 , year =

How much do language models memorize? , author=. arXiv preprint arXiv:2505.24832 , year=

work page arXiv
[32]

arXiv preprint arXiv:2503.02695 , year=

Zero-Shot Complex Question-Answering on Long Scientific Documents , author=. arXiv preprint arXiv:2503.02695 , year=

work page arXiv
[33]

Modeling Multi-hop Question Answering as Single Sequence Prediction , author=
[34]

An Effective Method to Answer Multi-hop Questions by Single-hop QA System , author=
[35]

Evaluating Object Hallucination in Large Vision-Language Models , author=
[36]

Arcface: Additive angular margin loss for deep face recognition , author=
[37]

Locating and editing factual associations in gpt , author=
[38]

Understanding information storage and transfer in multi-modal large language models , author=
[39]

Vlkeb: A large vision-language model knowledge editing benchmark , author=
[40]

GitHub repository , howpublished =

Romain Beaumont , title =. GitHub repository , howpublished =. 2022 , publisher =

2022
[41]

2021 , organization=

Learning transferable visual models from natural language supervision , author=. 2021 , organization=

2021
[42]

Detecting pretraining data from large language models , author=
[43]

Knowledge editing for multi-hop question answering using semantic analysis , author=
[44]

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable , author=
[45]

MuSiQue: Multihop Questions via Single-hop Question Composition , author=
[46]

Gemini: A Family of Highly Capable Multimodal Models

Gemini: a family of highly capable multimodal models , author=. arXiv preprint arXiv:2312.11805 , year=

work page internal anchor Pith review arXiv
[47]

Judging llm-as-a-judge with mt-bench and chatbot arena , author=
[48]

Memorization without overfitting: Analyzing the training dynamics of large language models , author=
[49]

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs , author=
[50]

Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning , author=
[51]

2024 , organization=

Mmbench: Is your multi-modal model an all-around player? , author=. 2024 , organization=

2024
[52]

arXiv preprint arXiv:2510.19422 , year=

LLM Unlearning with LLM Beliefs , author=. arXiv preprint arXiv:2510.19422 , year=

work page arXiv