arXiv Machine Learning July 24, 2026 By Salavat Ishbulatov

Attribution Markets: A Fisher-Market Formulation for Fractional Credit Assignment Between Planned Tasks and Performed Actions

Read the original on arXiv Machine Learning →

arXiv:2607. 20694v1 Announce Type: new Abstract: Personal and organizational planning systems maintain two records that drift apart: what was planned (a task's effort budget) and what was done (a logged action's duration and description).

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

arXiv Machine Learning

Jun 16

Repeated Bilateral Trade: The Quest for Fairness

arXiv:2606. 15369v1 Announce Type: new Abstract: We study repeated bilateral trade from a fairness perspective.

By Fran\c{c}ois Bachoc, Roberto Colomboni, Emilie Kaufmann

agents safety

arXiv Machine Learning

Jun 3

Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

arXiv:2606. 02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night).

By Oleg Miroshnichenko

agents reinforcement-learning

arXiv Machine Learning

1d ago

PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding

arXiv:2602. 08261v2 Announce Type: replace Abstract: Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Action (CPA).

By Binglin Wu, Yingyi Zhang, Xianneng Li, Ruyue Deng, Chuan Yue, Weiru Zhang, Xiaoyi Zeng

llms benchmarks

arXiv Machine Learning

Aug 11

Regret, equilibrium, and learning in games: A guided tour

arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.

By Panayotis Mertikopoulos

agents reinforcement-learning safety

arXiv AI

Aug 11

Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training

arXiv:2608. 09217v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute without regard to differences in how tasks respond to optimization.

By Ting Zhou, Zhenqing Ling, Daoyuan Chen, Qianli Shen, Yilun Huang, Ying Shen, Yaliang Li

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 30

Learning to Bid in Discriminatory Auctions with Budget Constraints

arXiv:2606. 29252v1 Announce Type: new Abstract: We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus $\alpha$ times payment, where $\alpha\in[0,1]$ is a cost-of-capital parameter.

By Negin Golrezaei, Sourav Sahoo

reinforcement-learning

Related stories

Repeated Bilateral Trade: The Quest for Fairness

Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding

Regret, equilibrium, and learning in games: A guided tour

Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training

Learning to Bid in Discriminatory Auctions with Budget Constraints