arXiv AI August 10, 2026 By Jinqi Wu, Sishuo Chen, Zhangming Chan, Yong Bai, Lei Zhang, Sheng Chen, Chenghuan Hou, Xiang-Rong Sheng, Han Zhu, Jian Xu, Bo Zheng, Chaoyou Fu

MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms

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arXiv:2603. 02184v2 Announce Type: replace-cross Abstract: Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learning paradigm for conversion rate (CVR) prediction.

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

benchmarks

arXiv Machine Learning

Jun 2

SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models

arXiv:2601. 22276v2 Announce Type: replace Abstract: As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributors who provide a collection of data is essential for fair compensation and sustainable data marketplaces.

By Mingyu Lu, Soham Gadgil, Chris Lin, Chanwoo Kim, Su-In Lee

diffusion safety

arXiv AI

Aug 10

Long-Horizon Agent Trajectory Attribution: A Unified Benchmark and Fine-Grained Annotation Framework

arXiv:2608. 06909v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, and memory.

By Jing Chen, Yang Sun, Li Zhang, Lin Xu, Jie Shi

llms agents benchmarks safety

arXiv AI

Jul 28

How Context Attribution Handles What the Model Already Knows

arXiv:2607. 23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response.

By Quoc-Huy Trinh, Lin Zhu, Sebastian Szyller

llms benchmarks

arXiv Machine Learning

Jun 3

Attribution via Distributional Paths for Information Revelation

arXiv:2606. 03885v1 Announce Type: new Abstract: Feature attribution methods explain predictions by assigning importance scores to input features.

By Kieran A. Murphy, Shameen Shrestha

arXiv AI

Jul 3

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

arXiv:2607. 01420v1 Announce Type: cross Abstract: As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and model safety.

By Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi, Sai Soorya Rao Veeravalli, Trang Nguyen, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Koustava Goswami, Samyadeep Basu

llms nlp efficiency multimodal benchmarks safety

arXiv Machine Learning

Jun 11

Bergson: An Open Source Library for Data Attribution

arXiv:2606. 11660v1 Announce Type: new Abstract: Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applications including debugging undesirable model behavior and training dataset curation.

By Lucia Quirke, Louis Jaburi, David Johnston, William Z. Li, Gon\c{c}alo Paulo, Guillaume Martres, Girish Gupta, Stella Biderman, Nora Belrose

llms safety

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SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models

Long-Horizon Agent Trajectory Attribution: A Unified Benchmark and Fine-Grained Annotation Framework

How Context Attribution Handles What the Model Already Knows

Attribution via Distributional Paths for Information Revelation

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

Bergson: An Open Source Library for Data Attribution