Automation services for complex business processes usually require a high level of information technology literacy. There is a strong demand for a smartly assisted process automation (IPA: intelligent process automation) service that enables even general users to easily use advanced automation. A natural language interface for such automation is expected as an elemental technology for the IPA realization. The workflow targeted by IPA is generally composed of a combination of multiple tasks. However, semantic parsing, one of the natural language processing methods, for such complex workflows has not yet been fully studied. The reasons are that (1) the formal expression and grammar of the workflow required for semantic analysis have not been sufficiently examined and (2) the dataset of the workflow formal expression with its corresponding natural language description required for learning workflow semantics did not exist. This paper defines a new grammar for complex workflows with chaining machine-executable meaning representations for semantic parsing. The representations are at a high abstraction level. Additionally, an approach to creating datasets is proposed based on this grammar.
Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection
Merging Triggers, Breaking Backdoors: Defensive Poisoning for Instruction-Tuned Language Models
Graduate School of Artificial Intelligence, POSTECH, Republic of Korea（POSTECH人工智能研究生院，韩国）；Department of Computer Science and Engineering, POSTECH, Republic of Korea（POSTECH计算机科学与工程系，韩国）
SwitchCIT: Switching for Continual Instruction Tuning
Coordinated Science Laboratory（协调科学实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mathematics（数学系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing
College of Intelligence and Computing（智能与计算学院）；School of New Media and Communication（新媒体与传播学院）；Shanghai Key Laboratory of Data Science（上海数据科学 key laboratory）
When Emotion Becomes Trigger: Emotion-style dynamic Backdoor Attack Parasitising Large Language Models
School of Cyber Science and Engineering, Sichuan University（四川大学计算机科学与工程学院）；School of Computer Science, China West Normal University（西南大学计算机科学学院）；School of Electronic and Information Engineering, Lanzhou Jiaotong University（兰州交通大学电子信息工程学院）
OGAM: Connecting Systematic Testing to Runtime Assurance through Object-Grounded Attention Monitoring for VLA Policies
Carl von Ossietzky University of Oldenburg（卡尔·冯·奥西茨基大学奥尔登堡分校）；Huadian Electric Power Research Institute（华电电力科学研究院）；Chalmers University of Technology（查尔姆斯理工大学）
DropVLA: An Action-Level Backdoor Attack on Vision-Language-Action Models
Institute of Trustworthy Embodied AI, Fudan University（可信具身AI研究院，复旦大学）；Shanghai Key Laboratory of Multimodal Embodied AI（上海多模态具身AI重点实验室）；City University of Hong Kong（香港城市大学）
