arXiv AI By Xinyu Wang, Ziyu Zhao, Yixuan He, Xiaowen Chang Alex Smola

When Is Deletion Ordering Tractable? From Update Dynamics to Permutation Structure

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arXiv AI
Sep 16

Execution Flexibility in Automated Planning: A Comparative Evaluation of Deordering and Reordering Strategies

The paper evaluates strategies for increasing plan‑execution flexibility by converting sequential plans into partial‑order plans through deordering and reordering. It compares block deordering methods, which restructure causal dependencies, with MaxSAT‑based approaches that optimize within existing causal structures. The study finds that block deordering consistently outperforms MaxSAT in both effectiveness and efficiency, offering anytime solutions and higher flexibility gains per computation time.

By Md. Monjurul Islam, Sabah Binte Noor, Fazlul Hasan Siddiqui, Gahangir Hossain
arXiv Machine Learning
Sep 24

From Reasoning Strings to Partial Orders: Verifier-Certified Rule Transport through Quotient Policy Optimization

The paper introduces Verifier-Certified Rule Transport (VCRT), a method that uses native verifiers to replay adjacent operation pairs and identify commutation certificates or anti-diamonds, thereby distinguishing true logical dependencies from mere serialization choices in reinforcement learning with verifiable rewards. VCRT assigns policy credit based on the total probability mass of each certified orbit and imposes constraints on post-swap consistency, source retention, and policy drift. In leave-one-environment-out transfer experiments across ProofWriter, CLRS, and Lean, VCRT achieves a 77.60% macro pass rate, outperforming the strongest baseline by 13.06 points, with the largest gains observed in Lean.

By Bang Xie, Hao Liu, Zhiyuan Peng, Xin Yin, Chenhao Ying, Yuan Luo, Senjian Zhang, Wei Chen