A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs directly as target process models so as to establish connections to the field of program synthesis and facilitate the translation from abstract process models to executable processes, e.g., for robotic process automation. Furthermore, we develop a novel bottom-up agglomerative approach to the discovery of such structured program process models. In comparison with the popular top-down recursive inductive miner, our proposed agglomerative miner enjoys the similar theoretical guarantee to produce sound process models (without deadlocks and other anomalies) while exhibiting some advantages like avoiding silent activities and accommodating duplicate activities. The proposed algorithm works by iteratively applying a few graph rewriting rules to the directly-follows-graph of activities. For real-world (sparse) directly-follows-graphs, the algorithm has quadratic computational complexity with respect to the number of distinct activities. To our knowledge, this is the first process discovery algorithm that is made for the purpose of program synthesis. Experiments on the BPI-Challenge 2020 dataset and the Karel programming dataset have demonstrated that our proposed algorithm can outperform the inductive miner not only according to the traditional process discovery metrics but also in terms of the effectiveness in finding out the true underlying structured program from a small number of its execution traces.
Bespoke Nanoparticle Synthesis and Chemical Knowledge Discovery Via Autonomous Experimentations
Computational Science Research Center, Korea Institute of Science and Technology（韩国科学技术研究院计算科学研究中心）；Korea University（高丽大学）；Materials Architecturing Research Center, Korea Institute of Science and Technology（韩国科学技术研究院材料结构研究中心）；University of Science and Technology (UST)（科学技术大学）
Structured Program Synthesis using LLMs: Results and Insights from the IPARC Challenge
Thoughtworks Technologies, Pune, India Dept. of CSIS \& APPCAIR, BITS Pilani, Goa Campus, India School of CSE, University of New South Wales, Sydney, Australia
Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization
Université de Montréal（蒙特利尔大学）；University of Oxford（牛津大学）；University College London（伦敦大学学院）；Mila - Québec AI Institute（魁北克人工智能研究所）；CIFAR AI Chair（CIFAR人工智能 chair）；University of Ottawa（渥太华大学）；National Research Council Canada（加拿大国家研究理事会）
Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships
Data-driven materials design group, Center for Basic Research on Materials (CBRM)（数据驱动材料设计组，基本材料研究中心（CBRM））；National Institute for Materials Science (NIMS)（国家材料科学研究所（NIMS））
An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery
Idiap Research Institute（伊迪亚普研究所）；National Biomarker Centre（国家生物标志物中心）；CRUK-MI（英国癌症研究中心曼彻斯特研究所）；Univ. of Manchester（曼彻斯特大学）；Department of Computer Science, Univ. of Manchester（曼彻斯特大学计算机科学系）
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Wizard Intelligence Learning Lab, Stanford University（智能巫师学习实验室，斯坦福大学）；Peking University（北京大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Ant Group（蚂蚁集团）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理重点实验室）
Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data. Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size increases. In this paper we propose ToolWriter to generate query specific programs and detect when to apply them to transform tables and align them with the TQA model's capabilities. Focusing ToolWriter to generate row-filtering tools improv…
Usable Agent Discovery for Decentralized AI Systems
Department of Computer Science（计算机科学系）；University of Pisa（比萨大学）；Institute of Information Science and Technologies（信息科学与技术研究所）；National Research Council of Italy（意大利国家研究理事会）；Institute of Informatics and Telematics（信息学与电信研究所）
Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution
Max Planck Institute for the Science of Light（光科学Max Planck研究所）；Department of Physics, Friedrich-Alexander University Erlangen-Nürnberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学物理系）
Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
Department of Industrial Systems Engineering and Management, National University of Singapore（工业系统工程与管理系，新加坡国立大学）；Department of Civil, Environmental and Geomatic Engineering, ETH Zürich（土木、环境与测绘工程系，苏黎世联邦理工学院）；Future Resilient Systems, Singapore-ETH Centre（未来韧性系统，新加坡-苏黎世中心）
Eindhoven University of Technology（埃因霍温理工大学）；Clermont-Auvergne University（克莱蒙奥弗涅大学）
Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers for questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery, based on utility functions and constraints. We describe four scopes on which these utility functions and constrains can be defined, and show that utility fun…
Divergent-Convergent Reasoning: Scaling Test-Time Compute through Structured Solution Synthesis
School of Computing, Queen’s University（女王大学计算学院）；IBM T.J. Watson Research Center（IBM T.J.沃森研究中心）；University of Chicago（芝加哥大学）；Tensormesh Inc.（Tensormesh公司）
AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search
School of Data Science, CUHK-Shenzhen（CUHK-Shenzhen数据科学学院）；Shanghai AI Laboratory（上海人工智能实验室）；Jilin University（吉林大学）；Mila - Quebec AI Institute（魁北克AI研究所）；HEC Montréal（蒙特利尔HEC）
This thesis focuses on process mining on event data where such a normative specification is absent and, as a result, the event data is less structured. The thesis puts special emphasis on one application domain that fits this description: the analysis of smart home data where sequences of daily activities are recorded. In this thesis we propose a set of techniques to analyze such data, which can be grouped into two categories of techniques. The first category of methods focuses on preprocessing event logs in order to enable process discovery techniques to extract insights into unstructured eve…
MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning
Department of Industrial \& Systems Engineering, KAIST, Daejeon, Korea；Graduate School of Data Science, KAIST, Daejeon, Republic of Korea；Korea Research Institute of Chemical Technology, KRICT, Daejeon, Korea
Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model
State Key Laboratory of Advanced Materials for Intelligent Sensing, Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University（智能传感先进材料国家重点实验室、有机集成电路重点实验室、教育部、天津分子光电子科学重点实验室、化学系、天津大学）；Language Intelligence Technology Co., Ltd.（语言智能技术有限公司）；College of Intelligence and Computing, Tianjin University（智能与计算学院、天津大学）；School of Materials and Chemical Engineering, Ningbo University of Technology（材料与化学工程学院、宁波工业大学）
