arXiv Machine Learning July 3, 2026 By Tzu-Heng Huang, Aditya Goyal, John Cooper, Frederic Sala

WARP: Weight-Space Analysis for Recovering Training Data Portfolios

Read the original on arXiv Machine Learning →

arXiv:2607. 01686v1 Announce Type: new Abstract: Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how different sources are sampled -- are rarely disclosed.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 4

STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

arXiv:2606. 05165v1 Announce Type: new Abstract: Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data.

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arXiv Machine Learning

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Learning the Pareto Frontier of Predictive Models under Distribution Shift

arXiv:2608. 00632v1 Announce Type: new Abstract: Modern machine learning pipelines increasingly rely on reusing pretrained and foundation models across downstream tasks.

By Yiming Dong, Jiwei Zhao, Yang Young Lu

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arXiv Machine Learning

Jun 17

Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework

arXiv:2502. 18049v5 Announce Type: replace-cross Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation.

By Hengzhi He, Shirong Xu, Guang Cheng

arXiv Machine Learning

Jun 26

Dataset Usage Inference without Shadow Models or Held-out Data

arXiv:2606. 26257v1 Announce Type: new Abstract: How much of my data was used to train a machine learning model?

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arXiv AI

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DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning

arXiv:2607. 22769v1 Announce Type: cross Abstract: The training efficacy of large language models (LLMs) is fundamentally constrained by the quality and composition of training data.

By He Zhang

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arXiv Machine Learning

Jul 15

Inference-Time Machine Unlearning via Gated Activation Redirection

arXiv:2605. 12765v3 Announce Type: replace Abstract: Large Language Models memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety.

By Vin\'icius Conte Turani, Ot\'avio Parraga, Jo\~ao Vitor Boer Abitante, Kristen K. Arguello, Joana Pasquali, Ramiro N. Barros, Flavio du Pin Calmon, Christian Mattjie, Rodrigo C. Barros, Lucas S. Kupssinsk\"u

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STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

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Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework

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Inference-Time Machine Unlearning via Gated Activation Redirection