arXiv:2606. 05009v1 Announce Type: cross Abstract: Deontic reasoning is the task of answering questions by applying explicit rules and policies to case-specific facts, for example computing tax liability under a statute or determining the outcome of an immigration appeal.
By Guangyao Dou, William Jurayj, Nils Holzenberger, Benjamin Van Durme
arXiv:2609.38409v1 Announce Type: new
Abstract: Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable r...
By \.Ibrahim Ethem Deveci, Funda Tan \c{C}al{\i}k, Bar{\i}\c{s} Deniz Sa\u{g}lam, Duygu Ataman
arXiv:2609.21492v1 Announce Type: new
Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely r...
By Jingyu Hu, Shu Yang, Weiru Liu, Di Wang
ARBOR is a framework that tracks reasoning states as a tree throughout a model’s chain of thought, enabling attribution of failures such as overthinking, underthinking, and unsupported deductions to specific state-step relations. By maintaining a global view of the state trajectory, ARBOR distinguishes failure steps from at‑risk steps where the failure did not occur, refining failure modes into distinct subtypes. The method can be integrated with existing inference-time mitigations and training data, and its code, data, and interactive examples are publicly available.
By Huihan Li, Siyuan Wang, Naga Vamsi Ramana Dinavahi, Xiaohui Liu, Xiang Ren
SCOPE compiles natural‑language policies into neurosymbolic predicate programs that combine small neural models, deterministic extractors, and logical composition. The system is jointly optimized with adversarially generated counterexamples, achieving higher accuracy than prompt‑optimization, task‑decomposition, and workflow‑generation baselines on real‑world policy benchmarks. Programs trained with the weakest executor transfer to other models, enabling deployment on inexpensive models with stable, interpretable predictions.
By Arnon Mazza, Elad Levi
RuleWeaver is a benchmark construction framework designed to evaluate large language models’ ability to reason over complex, rule‑centered scenarios. It begins with corpus‑derived IF‑THEN meta rules, expands them into more intricate rules, and composes these into scenario‑based QA instances. The benchmark assesses not only final answer correctness but also process‑level metrics such as rubric‑based answer quality, rule recall, and rule precision, revealing that current LLMs achieve only about 50% of the maximum rubric score on these tasks.
By Bohan Yu, Shi-Yang Li, Pengfei Cao, Jun Zhao, Kang Liu