:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Mehta, Sushant
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2511.17602
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

When Are Learning Biases Equivalent? A Unifying Framework for Fairness, Robustness, and Distribution Shift
by: Mehta, Sushant
Published: (2025)

Scaling Laws and In-Context Learning: A Unified Theoretical Framework
by: Mehta, Sushant, et al.
Published: (2025)

Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data
by: Lee, Jungi, et al.
Published: (2025)

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
by: Li, Hongkai, et al.
Published: (2026)

EnterpriseBench Corecraft: Training Generalizable Agents on High-Fidelity RL Environments
by: Mehta, Sushant, et al.
Published: (2026)

LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training
by: Gwak, Minju, et al.
Published: (2026)

Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
by: Cheng, Yinan, et al.
Published: (2024)

Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models
by: Golchin, Shahriar, et al.
Published: (2023)

Synthetic Data Aided Federated Learning Using Foundation Models
by: Abacha, Fatima, et al.
Published: (2024)

TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classification
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)

RADAR: Mechanistic Pathways for Detecting Data Contamination in LLM Evaluation
by: Kattamuri, Ashish, et al.
Published: (2025)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
by: Lan, Tian, et al.
Published: (2025)

Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
by: Han, Seungju, et al.
Published: (2026)

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
by: Wang, Kevin, et al.
Published: (2026)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)

Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation
by: Mahdavi, Sadegh, et al.
Published: (2025)

Search-Time Data Contamination
by: Han, Ziwen, et al.
Published: (2025)

Mixtera: A Data Plane for Foundation Model Training
by: Böther, Maximilian, et al.
Published: (2025)

Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
by: Zhu, Mengran, et al.
Published: (2024)

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
by: Kothapalli, Vignesh, et al.
Published: (2026)

BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models
by: Srivastava, Gaurav, et al.
Published: (2025)

Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems
by: Mehta, Sushant
Published: (2025)

Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
by: Erturk, Eray, et al.
Published: (2025)

Trace-Level Analysis of Information Contamination in Multi-Agent Systems
by: Mazhar, Anna, et al.
Published: (2026)

The Impact of Post-training on Data Contamination
by: Kocyigit, Muhammed Yusuf, et al.
Published: (2026)

Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models
by: Hu, Zizhao, et al.
Published: (2025)

Evading Data Contamination Detection for Language Models is (too) Easy
by: Dekoninck, Jasper, et al.
Published: (2024)

On The Fragility of Benchmark Contamination Detection in Reasoning Models
by: Wang, Han, et al.
Published: (2025)

A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data
by: Ulmer, Markus, et al.
Published: (2023)

IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
by: Chikoti, Shreenaga, et al.
Published: (2024)

Does Training on Synthetic Data Make Models Less Robust?
by: Zhang, Lingze, et al.
Published: (2025)

Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
by: Tao, Yongding, et al.
Published: (2025)

RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
by: Mou, Xudong, et al.
Published: (2025)

Beyond Next Token Prediction: Patch-Level Training for Large Language Models
by: Shao, Chenze, et al.
Published: (2024)

CacheFormer: High Attention-Based Segment Caching
by: Singh, Sushant, et al.
Published: (2025)

Online Detection of Water Contamination Under Concept Drift
by: Li, Jin, et al.
Published: (2025)

Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)

Beyond Similarity: Temporal Operator Attention for Time Series Analysis
by: Twitty, Jevon, et al.
Published: (2026)