@inproceedings{cohen-etal-2026-remind,
title = "{REMIND}: Memorization and Unlearning in {LLM}s Through the Lens of Input Loss Landscapes",
author = "Cohen, Liran and
Nemcovsky, Yaniv and
Mendelson, Avi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2215/",
doi = "10.18653/v1/2026.acl-long.2215",
pages = "47955--47993",
ISBN = "979-8-89176-390-6",
abstract = "Understanding how large language models (LLMs) store, retain, and remove knowledge is critical for interpretability, reliability, and privacy compliance. We reveal a key phenomenon: machine unlearning imprints distinct geometric signatures in the model{'}s input loss landscape (ILL), with unlearned examples forming flat, low-curvature plateaus that contrast sharply with the high-curvature basins of retained or unseen examples. Remarkably, these patterns emerge even when pointwise losses overlap, exposing residual memorization through input-output behavior alone. Building on this insight, we introduce **REMIND (Residual Memorization in Neighborhood Dynamics)**, a framework that diagnoses memorization states (retained, forgotten, holdout) by probing local ILL curvature over semantically coherent neighborhoods. REMIND operates using only loss queries and a novel embedding-proximity perturbation method to generate controlled, interpretable variants. In evaluations, REMIND achieves 82{\%} multi-class ROC-AUC, outperforming baselines like ROUGE-L and MIN-K{\%}++, with roughly 2{\texttimes} higher AUC at 1{\%} FPR, and remains robust on paraphrased inputs. This neighborhood-level geometric analysis provides a practical, interpretable lens on LLM knowledge retention and unlearning, detecting subtle residual signals missed by pointwise or aggregated metrics."
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<abstract>Understanding how large language models (LLMs) store, retain, and remove knowledge is critical for interpretability, reliability, and privacy compliance. We reveal a key phenomenon: machine unlearning imprints distinct geometric signatures in the model’s input loss landscape (ILL), with unlearned examples forming flat, low-curvature plateaus that contrast sharply with the high-curvature basins of retained or unseen examples. Remarkably, these patterns emerge even when pointwise losses overlap, exposing residual memorization through input-output behavior alone. Building on this insight, we introduce **REMIND (Residual Memorization in Neighborhood Dynamics)**, a framework that diagnoses memorization states (retained, forgotten, holdout) by probing local ILL curvature over semantically coherent neighborhoods. REMIND operates using only loss queries and a novel embedding-proximity perturbation method to generate controlled, interpretable variants. In evaluations, REMIND achieves 82% multi-class ROC-AUC, outperforming baselines like ROUGE-L and MIN-K%++, with roughly 2× higher AUC at 1% FPR, and remains robust on paraphrased inputs. This neighborhood-level geometric analysis provides a practical, interpretable lens on LLM knowledge retention and unlearning, detecting subtle residual signals missed by pointwise or aggregated metrics.</abstract>
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%0 Conference Proceedings
%T REMIND: Memorization and Unlearning in LLMs Through the Lens of Input Loss Landscapes
%A Cohen, Liran
%A Nemcovsky, Yaniv
%A Mendelson, Avi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cohen-etal-2026-remind
%X Understanding how large language models (LLMs) store, retain, and remove knowledge is critical for interpretability, reliability, and privacy compliance. We reveal a key phenomenon: machine unlearning imprints distinct geometric signatures in the model’s input loss landscape (ILL), with unlearned examples forming flat, low-curvature plateaus that contrast sharply with the high-curvature basins of retained or unseen examples. Remarkably, these patterns emerge even when pointwise losses overlap, exposing residual memorization through input-output behavior alone. Building on this insight, we introduce **REMIND (Residual Memorization in Neighborhood Dynamics)**, a framework that diagnoses memorization states (retained, forgotten, holdout) by probing local ILL curvature over semantically coherent neighborhoods. REMIND operates using only loss queries and a novel embedding-proximity perturbation method to generate controlled, interpretable variants. In evaluations, REMIND achieves 82% multi-class ROC-AUC, outperforming baselines like ROUGE-L and MIN-K%++, with roughly 2× higher AUC at 1% FPR, and remains robust on paraphrased inputs. This neighborhood-level geometric analysis provides a practical, interpretable lens on LLM knowledge retention and unlearning, detecting subtle residual signals missed by pointwise or aggregated metrics.
%R 10.18653/v1/2026.acl-long.2215
%U https://aclanthology.org/2026.acl-long.2215/
%U https://doi.org/10.18653/v1/2026.acl-long.2215
%P 47955-47993
Markdown (Informal)
[REMIND: Memorization and Unlearning in LLMs Through the Lens of Input Loss Landscapes](https://aclanthology.org/2026.acl-long.2215/) (Cohen et al., ACL 2026)
ACL