arXiv AI August 21, 2026 By Andrei Seoev, Leonid Gremyachikh, Anastasiia Smirnova, Yash Madhwal, Alisa Kalacheva, Dmitry Belousov, Ilia Zubov, Aleksei Smirnov, Denis Fedyanin, Vladimir Gorgadze, Yury Yanovich

The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain

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arXiv:2510. 14642v2 Announce Type: replace-cross Abstract: In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV).

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arXiv Machine Learning

Jun 30

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

arXiv:2606. 28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge.

By Junhan Li, Yuxin Zhang, Haoran Wang, Minghao Chen

reinforcement-learning benchmarks safety

arXiv AI

Jul 29

HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising

arXiv:2607. 24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.

By Ji Wu, Yunshan Peng, Wentao Bai, Yunke Bai, Wenzheng Shu, Jinan Pang, Yanxiang Zeng, Xialong Liu

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 5

DNQ: Deep Nash Q-Network for Partially Observable n-Player Games

arXiv:2606. 06480v1 Announce Type: cross Abstract: Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions, resource allocation, and security competition.

By Qintong Xie, Edward Koh, Xavier Cadet, Peter Chin

agents

arXiv Machine Learning

Aug 4

LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations

arXiv:2608. 00123v1 Announce Type: cross Abstract: LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation.

By Yan Fang, Jialin Chen, Chun Gan, Hang Yu, Mingjun Nie, Yeyu Zhang, Fengxiang He, Ching Law

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arXiv Machine Learning

Aug 18

Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions

arXiv:2605. 09448v2 Announce Type: replace Abstract: We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints.

By Zihao Hu, Yuxiao Wen, Yuan Yao, Jiheng Zhang, Zhengyuan Zhou

arXiv AI

Aug 18

Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies

arXiv:2601. 02754v3 Announce Type: replace-cross Abstract: With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments.

By Mingming Zhang, Na Li, Zhuang Feiqing, Hongyang Zheng, Jiangbing Zhou, Wang Wuyin, Sheng-jie Sun, XiaoWei Chen, Junxiong Zhu, Lixin Zou, Chenliang Li

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Related stories

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising

DNQ: Deep Nash Q-Network for Partially Observable n-Player Games

LLM-OSDA: An Optimal-Stopping Dynamic Auction for Native Advertising in Multi-Turn LLM Conversations

Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions

Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies