arXiv AI July 28, 2026 By Mohammed Yousif, Prabhjot Singh, Arjun Pankajakshan, Madhu Reddiboina

DocHRL: A Hierarchical Reinforcement Learning Framework for Cost-Optimised Document Classification

arXiv:2607. 22644v1 Announce Type: new Abstract: Real-world document classification pipelines typically apply the same sequence of models to every incoming document, regardless of its complexity or type.

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

arXiv AI

Aug 11

DocAtlas: Long-Document Understanding as Mutable-State Interaction

arXiv:2608. 07527v1 Announce Type: cross Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts.

By Hongchen Wei, Yuanzhe Wang, Bei Liu, Yifan Yang, Qi Dai, Kai Qiu, Yunsheng Li, Dongdong Chen, Chong Luo, Zhenzhong Chen, Baining Guo

llms rag agents reinforcement-learning multimodal

arXiv Machine Learning

Jul 22

A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

arXiv:2607. 18358v1 Announce Type: cross Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise.

By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu

llms safety

arXiv Machine Learning

Jun 29

Learning to Evict from Key-Value Cache

arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.

By Luca Moschella, Laura Manduchi, Ozan Sener

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Jul 24

From Errors to Rules: Iterative Prompt Optimization for Text Classification

arXiv:2607. 20497v1 Announce Type: new Abstract: Prompt optimization for text classification spans diverse approaches, from demonstration selection to exploration-based search to error-driven diagnosis, each with known but incompletely characterized strengths and limitations.

By Yueying Cui, Renhao Xue, Yi Zhang, Mukul Prasad

nlp benchmarks

arXiv AI

Jul 16

OvisOCR2 Technical Report

arXiv:2607. 13639v1 Announce Type: cross Abstract: We introduce OvisOCR2, a 0.

By Shiyin Lu, Yinglun Li, Yu Xia, Yuhui Chen, An-Yang Ji, Jun-Peng Jiang, Qing-Guo Chen, Jianshan Zhao, En Lin, Haijun Li, Cheng Qin, Zhao Xu, Weihua Luo

reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 14

Learning to Fine-tune Foundation Models under Resource Limitations

arXiv:2607. 10694v1 Announce Type: cross Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device.

By Thomas Tsouparopoulos, Iordanis Koutsopoulos

nlp reinforcement-learning fine-tuning

Related stories

DocAtlas: Long-Document Understanding as Mutable-State Interaction

A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

Learning to Evict from Key-Value Cache

From Errors to Rules: Iterative Prompt Optimization for Text Classification

OvisOCR2 Technical Report

Learning to Fine-tune Foundation Models under Resource Limitations