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Period ending 2026-09-21

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130 papers

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A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models
Jun 7, 2026Yunchen Li, Shaohui Lin, Zhou YuOverfitting Interpolation
Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
Jun 5, 2026Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +3Large Language Model Adaptation Membership Inference
Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2
Jun 5, 2026Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez +3Membership Inference Algorithm Auditing
DragOn: A Benchmark and Dataset for Drag-Based GUI Interactions
Jun 4, 2026Nathan Bout, Maxime Langevin, Ronan RiochetBenchmark Datasets Real-World Manipulation Tasks
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 PoechMemorization Language Model Evasion Attacks
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Jun 3, 2026Rishit Dagli, Abir Harrasse, Luke Zhang +4Training Data Attribution Training Data
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
Jun 2, 2026Joanna Zou, Fraser Birks, Dallas Foster +1Interatomic Potentials Molecular Dynamics
KletterMix: Climbing Toward High-Quality German Pretraining Data
Jun 2, 2026Maurice Kraus, Ruben Härle, Sebastian Sztwiertnia +4Large Language Model Pretraining Text Corpora
A Primer in Post-Training Reasoning Data: What We Know About How It Works
Jun 1, 2026Yaoming Li, Guangxiang Zhao, Qilong Shi +3Table Reasoning Datasets LLM Post-Training Methods
Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers
May 30, 2026Yeqi Huang, Yue Chen, Yanwei Ye +2Biomedical Text Qwen3-Embedding-8B
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
May 30, 2026Amir Hossein Shahdadian, Ahmed M. Abdelmoniem, Mahdi Taheri +2Heterogeneous Federated Learning Federated Learning
On the Difficulty of Learning a Meta-network for Training Data Selection
May 30, 2026Zilin Du, Junqi Zhao, Boyang Albert LiMeta-Learning Training Data
idSCD: Identifying Training Datasets through Semantic Correlation Descriptors
May 28, 2026Andrada Gobeaja, Ionut Hodoroaga, Elena Burceanu +1Membership Inference Cross-Dataset Benchmark
Data filtering methods for training language models
May 28, 2026Egor Shevchenko, Elena BruchesNatural Language Datasets Noisy Labels
Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
May 28, 2026Mengdi Chu, Yang Liu, Ayan Biswas +1Distribution Shifts Spatio-Temporal Forecasting
Toward Ethical Facial Age Estimation: A Generalized Zero-Shot Benchmark Without Training on Children's Data
May 28, 2026Caio Petrucci, Leo Sampaio Ferraz Ribeiro, Sandra AvilaAge Children
Auditing Training Data in Generative Music Models via Black-Box Membership Inference
May 28, 2026Yi Chen Liu, Jiawei Yu, Kexin Cao +3Music Generation Membership Inference
DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents
May 27, 2026Jiamian Wang, Ruiyi Zhang, Tong Yu +5Multimodal Documents Search Agents
No Safe Dose: How Training Data Drives Unsafe Image Generation
May 27, 2026Felix Friedrich, Lukas Helff, Niharika Hegde +2Text-To-Image Large Language Model Safety
RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data
May 26, 2026Hsiu-Yuan Huang, Weijie Liu, Chenming Tang +5Reinforcement Learning With Verifiable Reward Training Data Attribution
When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection
May 26, 2026Zhengyu Hu, Zheyuan Xiao, Linxin Song +10Teacher Training Data
From Latent Space to Training Data: Explainable Specialization in Minimal MLPs
May 25, 2026Enrique Alba, Ezequiel Lopez-RubioMultilayer Perceptrons Latent Space
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
May 24, 2026Hongkai Li, Shifeng Xie, Lefei Shen +7Time Series Foundation Models Time Series
Spiking the training data to correct for test set contamination
May 24, 2026Johnny Tian-Zheng Wei, Jerry Li, Ameya Godbole +1Contamination Training Data
Extracting Training Data from Diffusion Language Models via Infilling
May 22, 2026Yihan Wang, N. AsokanDiffusion Language Models Memorization
Instrumentation for Imitation Learning: Enhancing Training Datasets for Clothes Hanger Insertion
May 22, 2026Remko Proesmans, Thomas Lips, Francis wyffelsImitation Learning Teleoperation
How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks
May 22, 2026Dong-Won LimInverse Kinematics Robotic Arms
What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA
May 21, 2026Xinjie He, Zhiyuan Lin, Su Liu +4Deep Reinforcement Learning Curriculum
CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models
May 19, 2026Yike Sun, Mingkun Xu, Mu You +5Model Interpretability Efficient Influence Function
Generative Pseudo-Force Fields for Molecular Generation
May 18, 2026Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4Molecular Generation Conformational Ensembles
Interference-Aware Multi-Task Unlearning
May 18, 2026Ying-Hua Huang, Rui Fang, Hsi-Wen Chen +1Multi--Task Learning Robust Multimodal Learning
Temporal Task Diversity: Inductive Biases Under Non-Stationarity in Synthetic Sequence Modelling
May 18, 2026Afiq Abdillah Effiezal Aswadi, Oliver Britton, Ross Baker +1Inductive Bias Sequence Modeling
The MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting
May 18, 2026Corentin Dumery, Niki Amini-Naieni, Shervin Naini +1Counting Vision Datasets
Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew
May 18, 2026Joan Serrà, Dipam Goswami, Fabio Morreale +2Discrete Diffusion Model Training Data Attribution
ARIA: A Diagnostic Framework for Music Training Data Attribution
May 15, 2026Changheon Han, Ashkan Panahi, Kıvanç TatarTraining Data Rights
MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
May 14, 2026Weisen Jiang, Shuhao Chen, Sinno Jialin PanMixture-of-Experts Models Training Data
WARDEN: Endangered Indigenous Language Transcription and Translation with 6 Hours of Training Data
May 13, 2026Ziheng Zhang, Yunzhong Hou, Naijing Liu +1Low-Resource Languages Transcription
Does language matter for spoken word classification? A multilingual generative meta-learning approach
May 13, 2026Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der MerweMultilingual Language Models English
SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning
May 13, 2026Run Zou, Jianhang Ding, Yifan Ding +3Multimodal Instruction Tuning Training Data
Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training
May 12, 2026Yuanda Xu, Hejian Sang, Zhengze Zhou +3Token-Level Supervision On-Policy Distillation
Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
May 12, 2026Brian P. Powell, Jorge Martinez-Palomera, Amy Tuson +3Phase-Folded Light Curves Deep Learning
Trajectory Supervision for Continual Tool-Use Learning in LLMs
May 10, 2026Vishnu Vardhan Reddy, Sagnik Chatterjee, Soumik BhattaContinual Learning Methods Training Data
Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
May 8, 2026Pingbang Hu, Xueshen Liu, Z. Morley Mao +1Large Language Model Post-Training Training Data
DataDignity: Training Data Attribution for Large Language Models
May 7, 2026Xiaomin Li, Andrzej Banburski-Fahey, Jaron LanierLarge Language Model Evaluation Training Data Attribution
Leveraging Image Generators to Address Training Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
May 7, 2026Gabriel Jeanson, David-Alexandre Duclos, William Larrivée-Hardy +5Forest Inventory Image Generation
OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents
May 6, 2026Shuang Chen, Kaituo Feng, Hangting Chen +7Multimodal Search Agents Multi-Turn Tool-Use Agent
Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
May 6, 2026Kaifeng He, Xiaojun Zhang, Peiliang Cai +7Code Generation Code Quality
Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment
May 6, 2026Seunghan LeeTabular Foundation Models Tabpfn
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
May 3, 2026Kumar Shubham, Pavan Karjol, Kiran M K +1Noisy Labels High-Dimensional Data
Learning from a single labeled face and a stream of unlabeled data
Apr 30, 2026Branislav Kveton, Michal ValkoFacial Recognition Unlabeled Data
ClawGym: A Scalable Framework for Building Effective Claw Agents
Apr 29, 2026Fei Bai, Huatong Song, Shuang Sun +11Claws Sandbox
Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost
Apr 28, 2026Venkata Pushpak Teja MentaZero-Shot Text-To-Speech Librispeech
What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective
Apr 28, 2026Guangzeng Han, Xiaolei HuangInstruction Tuning Multimodal Instruction Tuning
Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora
Apr 27, 2026Chenkai Pan, Xinglong Xu, Yuhang Xu +6Training Data Knowledge Gaps
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Apr 24, 2026Zixuan Wang, Xingyu Dang, Jason D. Lee +1Power-Law Distribution Distributional Learning
CRAFT: Clustered Regression for Adaptive Filtering of Training data
Apr 24, 2026Parthasarathi Panda, Asheswari Swain, Subhrakanta PandaSequence Modeling Training Data
FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Apr 24, 2026Marco Obermeier, Marco Pruckner, Florian Haselbeck +1Electricity Price Forecasting Time Series Foundation Models
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Apr 22, 2026Alexander Weers, Daniel Rueckert, Martin J. MentenMedical Vision-Language Models Medical Report Generation
Fairness Audits of Institutional Risk Models in Deployed ML Pipelines
Apr 21, 2026Kelly McConvey, Dipto Das, Maya Ghai +3Fairness Audits Institutional Composition
Fine-Tuning Small Reasoning Models for Quantum Field Theory
Apr 21, 2026Nathaniel S. Woodward, Zhiqi Gao, Yurii Kvasiuk +3Large Reasoning Models Effective Field Theory

Jun 7, 2026 · cs.LGJ/K move · Enter open · S save

A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models

Yunchen Li, Shaohui Lin, Zhou Yu

East China Normal University

This paper provides a theoretical account of memorization in stochastic interpolation models. By leveraging closed-form expressions for the optimal velocity field and the associated score function, we show that, in the continuous-time oracle setting, both deterministic and stochastic generation processes recover training samples. Under Euler discretization, generated samples remain centered around training samples, with deviations controlled by the step size. We further analyze generation in the presence of estimation errors and show that accumulated estimation errors control the endpoint deviation from the training set. These results imply that the generated sample admits a representation as a training sample perturbed by three controlled terms: a discretization-induced bound, an estimation-error-induced bound, and stochastic Gaussian noise. Based on this characterization, we provide theoretical definitions of overfitting and underfitting in generative models. Synthetic simulations support our theoretical findings.

Overfitting Interpolation Generative Models Memorization Classical Numerical Solver Training Data Theoretical Foundations

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