arXiv:2604.27251v3 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicite...
By Xingwei Tan, Marco Valentino, Mahmud Elahi Akhter, Yuxiang Zhou, Maria Liakata, Nikolaos Aletras
The paper introduces Many-Tier Instruction Hierarchy (ManyIH), a new framework for resolving conflicts among instructions with arbitrarily many privilege levels in large language model agents. It presents ManyIH-Bench, a benchmark featuring 853 agentic tasks that require navigating up to 12 levels of conflicting instructions across 46 real-world agents. Experiments show current models achieve only about 40% accuracy when instruction conflict scales, highlighting a gap in fine-grained, scalable conflict resolution.
By Jingyu Zhang, Tianjian Li, William Jurayj, Hongyuan Zhan, Benjamin Van Durme, Daniel Khashabi
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.
By Shashwat Sourav, Aishwarya Balwani
arXiv:2607. 04562v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent outputs that can be wrong.
By MY Pitsane, Hope Mogale
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.
By Tejas Singh Anand, Yuet Ying Christina Wang, Wanting Jiang, Steve Masson, Tian Zheng, Bingjie Zhou
The paper introduces CONFLICTGUI, a benchmark that tests GUI agents on instruction-internal and instruction‑GUI context conflicts, revealing that many agents over‑comply with infeasible instructions. To address this, the authors propose CONFLICTGUARD, an inference‑time framework that couples a feasibility verification protocol with a conditional action modulation mechanism, enabling agents to assess instruction logic and GUI evidence before acting. Experiments on five agents show that CONFLICTGUARD significantly improves conflict‑task success while maintaining normal task performance.
By Zhaoyuan Huang, Tianjie Ju, Pengzhou Cheng, Zheng Wu, Yansi Li, Chuanbiao Song, Jun Lan, Huijia Zhu, Weiqiang Wang, Zhuosheng Zhang