Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model.
arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.
By Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge, Ashmita Kapoor, Tanya Dixit
arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).
By Marco Aruta, Francesco Improta, Vadim Malvone, Aniello Murano, Vladana Perlic
arXiv:2607. 23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B".
By Paul Simpson, John Kozak, Lisa Doake
arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.
By Jiajing Zhang, Jiamei Jiang, Chenyang Zhang, Feifei Mo, Linjing Li, Daniel Zeng
arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.
By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu
arXiv:2609.20973v1 Announce Type: cross
Abstract: Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-ste...
By Jiazhang Cai, Tao Wang, Ruidong Zhang, Siyuan Li, Terry Ma, Luyang Fang, Haoran Lu, Huimin Cheng, Yingchuan Zhang, Shushan Wu, Rui Xie, Lin Tang, Chao Huang, Rongjie Liu, Ziyu Liu, Meizhi Yu, Yongkai Chen, Yifan Zhou, Zeliang Sun, Chang Liu, Zhen Xiang, Wei Xiao, Zixin Rao, Xinyi Liu, Yutong Hu, Mengrui Zhang, Jing Zhang, Weidi Luo, Jincheng Yu, Zhengliang Liu, Weihang You, Hanqi Jiang, Yi Pan, Junhao Chen, Xinliang Li, Tianming Liu, Wenxuan Zhong, Ping Ma
arXiv:2608. 19009v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof assistants) that claim to detect the model's errors.
By Yajie Yin
arXiv:2607. 03847v1 Announce Type: new Abstract: Understanding why discovered scenarios become critical in scenario-based testing is essential for effectively leveraging them in decision-making systems.
By Qitong Chu, Xunjie He, Chen Deng, Huaxin Pei, Yufeng Yue
arXiv:2607. 03656v1 Announce Type: cross Abstract: Large Language Models are increasingly used to turn natural-language requirements into code.
By Adarsh Vatsa, Sachi Shome, Yingming Zhou, William Eiers
arXiv:2608. 08822v1 Announce Type: new Abstract: Cognitive decision-making research depends on diverse scenarios with carefully controlled complexity, yet manual production is slow, inconsistent, and biased.
By Abdalla Doleh, Toni Somers, Ratna Babu Chinnam
The article examines how Large Language Models can aid in creating Entity-Relationship diagrams from natural language requirements. It tests three LLMs with three prompting strategies—Zero-Shot, Chain of Thought, and Chain of Thought + Verifier—on scenarios of increasing complexity. Findings show that while LLMs perform adequately on simpler tasks, their reliability drops with more complex requirements, leading to inconsistencies, ambiguities, and constraint representation failures.
By Arthur F. Siqueira, Carlos D. S. Nogueira, Eduarda Farias, Claudio E. C. Campelo, J\'ulia Menezes