Memorization in Generative Models

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8 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
Oct 6, 2026Hicham Randrianarivo, Logan Renaud, Alexia AllalGenerative Retrieval Memorization in Generative Models
Artefact Research Center France
Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning
Oct 1, 2026Hyungjun Joo, Sehwan Kim, Hyeonggeun Han +2Diffusion Model Alignment Attention Control in Diffusion Models
Seoul National University · Konkuk University
From Modes to Memories: Characterizing the Scale-Space Dynamics of Diffusion Models
Sep 30, 2026Cristina López Amado, Marco Fumero, Francesco LocatelloMemorization in Generative Models Diffusion Models
Institute of Science and Technology Austria (ISTA)
PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis
Sep 30, 2026Pengfei Li, QinYi Liu, Mohammad KhalilSynthetic Data Generation Synthetic Tabular Data Generation
Ghost in the Encoder: Decodable Artist Identity Representations in Lyrics-to-Song Generation
Sep 30, 2026Arhan Vohra, Choenden Kyirong, Laura Ibáñez-Martínez +1Text-to-Music Generation Audio Diffusion Models
Music Technology Group, Universitat Pompeu Fabra
First Learn, Then Memorize: The Spectral Bias of Diffusion Models
Sep 28, 2026Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1Neural Network Generalization Memorization in Generative Models
Laboratoire de Physique de l’École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, F-75005 Paris, France · Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France · Department of Computing Sciences, Bocconi University, Milano, Italy
Double Descent and Malign Overfitting in Diffusion Models
Sep 22, 2026Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1Double Descent Benign Overfitting
Laboratoire de Physique de l’École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, F-75005 Paris, France · Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France · Department of Computing Sciences, Bocconi University, Milano, Italy
Mitigating Sequential Reappearance in Diffusion Data-Point Unlearning
Sep 21, 2026Donghyun Kim, Taehyuk Lee, Jinyeong Kim +4Memorization in Generative Models Diffusion Model Unlearning
Department of Computer Science and Engineering, Konkuk University Trustworthy Machine Learning Lab Seoul, Republic of Korea
Copying Versus Randomization in Lempel-Ziv Music Synthesis
Sep 11, 2026Nadav Mishan, Ram ZamirControllable Music Generation Symbolic Music Generation
Department of Electrical Engineering Tel Aviv University Tel Aviv, Israel
Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++
Sep 3, 2026Antonio Scardace, Francesco Guarnera, Sebastiano Battiato +1Privacy Auditing Memorization in Generative Models
University of Catania, Department of Mathematics and Computer Science, Catania, Italy
Zero Gap Is Not Restoration: Stratified Per-Question Probability Evaluation and Step-wise Mitigation of Benchmark Contamination
Aug 7, 2026Ruijie Hou, Yueyang Jiao, Zhao Wang +1LLM Evaluation Memorization in Generative Models
Zhejiang University
The Geometry of Memorization: Finite-Time Spectral Sensitivity as a Diagnostic for Flow Matching Models
Jul 14, 2026Shuchan WangFlow Matching Neural Network Memorization
An exact information theory of generalization phase transitions in Bayesian diffusion models
Jul 9, 2026Henry Hunt, Mason Kamb, Surya GanguliMemorization in Generative Models Diffusion Models
Department of Applied Physics Stanford University Stanford, CA 94305
Anchored or Drifting: What Recursive Self-Generation Reveals About Training Data
Jun 30, 2026Wojciech Łapacz, Stanisław PawlakMembership Inference Attacks Memorization in Generative Models
Warsaw University of Technology
Explaining Machine Learning and Memorization with Statistical Mechanics
Jun 30, 2026Robin TheriaultStatistical Physics of Learning Neural Network Memorization
Faculty of Science
Cyclic Denoising Reveals Ultrastable Memories in Diffusion Models
Jun 22, 2026Rishabh Sharma, Stefano MartinianiDiffusion Model Sampling Memorization in Generative Models
Simons Center for Computational Physical Chemistry, New York University · Center for Soft Matter Research, Department of Physics, New York University · Center for Neural Science, New York University +1
MGI: Member vs Generated Inference
Jun 22, 2026Bihe Zhao, Michel Meintz, Juangui Xu +2Image Generation Memorization in Generative Models
CISPA Helmholtz Center for Information Security
Substitution-Based Analysis of Structural Novelty for Generative Models of Materials
Jun 22, 2026Masahiro Negishi, Aron WalshMemorization in Generative Models Crystal Structure Generation
Department of Materials, Imperial College London, Exhibition Road, South Kensington, SW7 2AZ, London, United Kingdom.
NAMESAKES: Probing Identity Memorization in Text-to-Image Models
Jun 18, 2026Morris Alper, Vasudha Varadarajan, Moran Yanuka +2Memorization in Generative Models T2I Generation Evaluation
Carnegie Mellon University · Tel Aviv University · Cornell University
Learning What to Remember: A Cognitively Grounded Multi-Factor Value Model for Agentic Memory
Jun 11, 2026Zhibao Chen, Qian ChengLLM Agent Memory Persistent Memory for Language Models
Huatai Securities, Nanjing, China
Detecting Functional Memorization in Code Language Models
Jun 11, 2026Matthieu Meeus, Anil Ramakrishna, Matthew Grange +2Memorization in Language Models LLM Auditing
Meta · Imperial College London
Substrate Asymmetry in User-Side Memory: A Diagnostic Framework
Jun 10, 2026Youwang DengRetrieval-Augmented Generation LLM Personalization
EpistemicaLab — Independent Research
Deployment-Time Memorization in Foundation-Model Agents
Jun 8, 2026Lei, Chen, Guilin Zhang +8Persistent Memory for Language Models Privacy Leakage in Language Models
Rachel
Echo-Memory: A Controlled Study of Memory in Action World Models
Jun 8, 2026Wayne King, Zeyue Xue, Yuxuan Bian +13Action-Conditioned Video Generation Video Diffusion Models
The University of Hong Kong · Joy Future Academy, JD · The Chinese University of Hong Kong +5
Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
Jun 8, 2026Xiao Li, Yixuan Jia, Zekai Zhang +6Self-Supervised Learning Memorization in Generative Models
University of Michigan · 1Ohio State University
A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models
Jun 7, 2026Yunchen Li, Shaohui Lin, Zhou YuStochastic Interpolants Memorization in Generative Models
East China Normal University
Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
Jun 5, 2026Thomas Sesmat, Gabriel Meseguer-Brocal, Geoffroy PeetersData Leakage Rectified Flow
LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France · Deezer Research, Paris, France
LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs
Jun 4, 2026Gianluca Barmina, Peter Schneider-Kamp, Lukas Galke PoechData Leakage Memorization in Language Models
University of Southern Denmark
SaliMory: Orchestrating Cognitive Memory for Conversational Agents
Jun 2, 2026Kai Zhang, Xinyuan Zhang, Hongda Jiang +11Persistent Memory for Language Models Memorization in Generative Models
Meta Reality Labs
DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory
May 29, 2026Zhenhao Yang, Xiaoshi Wu, Zhengyao Lv +5Long-Horizon Video Generation Memory-Augmented Video Generation
1The University of Hong Kong · *Work done during an internship at Kling Team, Kuaishou Tech. · 2Kling Team, Kuaishou Technology

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Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval

Hicham Randrianarivo, Logan Renaud, Alexia Allal

Artefact Research Center France

Generative Retrieval Memorization in Generative Models Diffusion Language Model Decoding

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Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with diffusion, but changes identifiers, training recipe and decoding at once, so differences cannot be credited to the paradigm. On NQ320K and MS300K, we train autoregressive, masked-diffusion and block-diffusion models with residual-quantised, product-quantised and random identifiers. With identifier length and training budget fixed, we decode each model in several ways. Decoding alone moves a diffusion model's Hit@1 by 6.6 to 13.7 points. Our reference diffusion decoding, generate-and-match, generates an identifier, then retrieves the closest corpus identifiers. The generated identifier is right for 14-21% of NQ320K queries. We test one-pass scoring to decode diffusion retrievers: the model reads a fully masked identifier once, and each document is scored by its codes' probabilities. It matches or beats generate-and-match in 11 of 12 settings. Autoregressive models still lead in Hit@1; on NQ320K, the lead comes from the model, not beam search. Starting from one sampled identifier, one-pass scoring removes 46-83% of masked diffusion's deficit to beam search; from generate-and-match, at most a quarter. On NQ320K, every paradigm largely memorises which identifier answers which query: random identifiers keep 83-90% of the Hit@1 of residual-quantised ones. There, product-quantised identifiers lead residual-quantised ones by 3.4 points in the autoregressive model and by -0.7 to +3.6 in diffusion models; across decodings, AR's gap exceeds diffusion's by 1.5-2.3 points, around our 2-point threshold. Paradigm comparisons must report each paradigm at its own recipe and best decoding.

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