VLM-UnBench demonstrates that prompt-based training-free unlearning in VLMs leaves forget accuracy near the no-instruction baseline except under oracle conditions that reveal the target concept.
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Smith, and Chiyuan Zhang
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TimeROME-DLM enables training-free knowledge editing in masked diffusion language models via temporal causal tracing and low-rank residual edit memory applied at inference time.
REMEDI is a new benchmark for evaluating machine unlearning in multi-label clinical disease inference on MIMIC-III data that reveals trade-offs in existing methods.
DareU unlearns LLMs by PPO-optimizing attribution rewards so outputs are no longer attributable to forget owners, outperforming loss-based methods on forget-utility trade-offs.
New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.
TC-UMIA is a population-level attack using pre- and post-unlearning predictions to infer membership across forget, retain, and unseen sets, revealing added privacy leakage to retained data.
Machine unlearning conflates reversing the influence of specific training examples (untraining) with removing the full underlying distribution or behavior (unlearning).
Heuresis evaluates six search strategies for autonomous ML research agents and finds that novel ideas are rare, none rated original, and only one reaches top-10 quality while strategies steer axes but do not expand the quality-novelty frontier.
RepSelect isolates forget-set-specific representations via gradient PCA collapse to achieve 4-50x better post-relearning robustness than baselines across multiple models and forget categories.
Maps common low-compute research strategies for foundation models onto statistical, internal, external, and construct validity threats via a causal-inference lens.
MASC achieves competitive forget-retain trade-offs in language model unlearning at lower computational cost via margin self-correction and an online stopping criterion on TOFU, MUSE News, and MUSE Books.
Targeting minor components in LLM representations during unlearning yields substantially better resistance to relearning attacks than prior methods.
Existing continual unlearning methods fail to maintain forgetting, retained knowledge, and utility over 100 sequential privacy-deletion tasks in multimodal document models.
Geometric Unlearning distills a low-rank safe subspace from reference prompts and applies projection-based alignment on synthetic anchors to suppress target content while preserving non-target utility.
WIN-U delivers a retain-free unlearning update that approximates the gold-standard retrained model via a Woodbury-informed Newton step using only forget-set curvature information.
Downgrading optimizers to lower-information variants during LLM unlearning yields more robust forgetting on MUSE and WMDP benchmarks by converging to harder-to-perturb loss basins.
Introduces 5WBENCH balanced benchmark across 5W categories and MAAT three-phase adapter unlearning method that targets causal Why-type knowledge.
Unlearned language models retain low calibration error but show increased shortcut reliance on the TOFU benchmark, extending the reliability paradox to machine unlearning.
Standard unlearning metrics disagree in multimodal settings, but a correlation-weighted Unified Quality Score delivers consistent method rankings across benchmarks.
MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.
Collateral damage from LLM unlearning decays with semantic distance but persists across domains and can be predicted pre-unlearning from forget-evaluation set interaction features.
citing papers explorer
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Can VLMs Truly Forget? Benchmarking Training-Free Visual Concept Unlearning
VLM-UnBench demonstrates that prompt-based training-free unlearning in VLMs leaves forget accuracy near the no-instruction baseline except under oracle conditions that reveal the target concept.
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TimeROME-DLM: Temporal Causal Tracing and Low-Rank Inference-Time Knowledge Editing for Masked Diffusion Language Models
TimeROME-DLM enables training-free knowledge editing in masked diffusion language models via temporal causal tracing and low-rank residual edit memory applied at inference time.
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REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
REMEDI is a new benchmark for evaluating machine unlearning in multi-label clinical disease inference on MIMIC-III data that reveals trade-offs in existing methods.
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De-attribute to Forget for LLM Unlearning
DareU unlearns LLMs by PPO-optimizing attribution rewards so outputs are no longer attributable to forget owners, outperforming loss-based methods on forget-utility trade-offs.
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Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.
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Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
TC-UMIA is a population-level attack using pre- and post-unlearning predictions to infer membership across forget, retain, and unseen sets, revealing added privacy leakage to retained data.
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Is your algorithm unlearning or untraining?
Machine unlearning conflates reversing the influence of specific training examples (untraining) with removing the full underlying distribution or behavior (unlearning).
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Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty
Heuresis evaluates six search strategies for autonomous ML research agents and finds that novel ideas are rare, none rated original, and only one reaches top-10 quality while strategies steer axes but do not expand the quality-novelty frontier.
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RepSelect: Robust LLM Unlearning via Representation Selectivity
RepSelect isolates forget-set-specific representations via gradient PCA collapse to achieve 4-50x better post-relearning robustness than baselines across multiple models and forget categories.
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Validity Threats for Foundation Model Research
Maps common low-compute research strategies for foundation models onto statistical, internal, external, and construct validity threats via a causal-inference lens.
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Fast Unlearning at Scale via Margin Self-Correction
MASC achieves competitive forget-retain trade-offs in language model unlearning at lower computational cost via margin self-correction and an online stopping criterion on TOFU, MUSE News, and MUSE Books.
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Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
Targeting minor components in LLM representations during unlearning yields substantially better resistance to relearning attacks than prior methods.
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ICU-Bench:Benchmarking Continual Unlearning in Multimodal Large Language Models
Existing continual unlearning methods fail to maintain forgetting, retained knowledge, and utility over 100 sequential privacy-deletion tasks in multimodal document models.
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Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure
Geometric Unlearning distills a low-rank safe subspace from reference prompts and applies projection-based alignment on synthetic anchors to suppress target content while preserving non-target utility.
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WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework
WIN-U delivers a retain-free unlearning update that approximates the gold-standard retrained model via a Woodbury-informed Newton step using only forget-set curvature information.
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Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning
Downgrading optimizers to lower-information variants during LLM unlearning yields more robust forgetting on MUSE and WMDP benchmarks by converging to harder-to-perturb loss basins.
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MAAT: Multi-phase Adapter-Aware Targeted Unlearning
Introduces 5WBENCH balanced benchmark across 5W categories and MAAT three-phase adapter unlearning method that targets causal Why-type knowledge.
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Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models
Unlearned language models retain low calibration error but show increased shortcut reliance on the TOFU benchmark, extending the reliability paradox to machine unlearning.
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Metric Unreliability in Multimodal Machine Unlearning: A Systematic Analysis and Principled Unified Score
Standard unlearning metrics disagree in multimodal settings, but a correlation-weighted Unified Quality Score delivers consistent method rankings across benchmarks.
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Revisiting the Past: Data Unlearning with Model State History
MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.
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PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
Collateral damage from LLM unlearning decays with semantic distance but persists across domains and can be predicted pre-unlearning from forget-evaluation set interaction features.