Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded actions and atoms as task size increases. Recent advances in partial grounding have addressed this challenge by incrementally grounding only the most promising operators, guided by predictive models. However, these approaches primarily rely on relational features or learned embeddings and do not leverage the textual and structural cues present in PDDL descriptions. We propose SPG-LLM, which uses LLMs to analyze the domain and problem files to heuristically identify potentially irrelevant objects, actions, and predicates prior to grounding, significantly reducing the size of the grounded task. Across seven hard-to-ground benchmarks, SPG-LLM achieves faster grounding-often by orders of magnitude-while delivering comparable or better plan costs in some domains.
The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans
Department of Informatics（信息学院）；Athens University of Economics and Business（雅典经济与商业大学）；Archimedes, Athena Research Center（阿基米德·雅典研究中心）；Instituto de Telecomunicações（电信研究所）；Department of Computer Science（计算机科学系）；Columbia University（哥伦比亚大学）
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning
Intelligent Software Research Center, Institute of Software, CAS（中国科学院软件研究所智能软件研究中心）；SKL of Processors, Institute of Computing Technology, CAS（中国科学院计算技术研究所处理器国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Cambricon Technologies（寒武纪科技）；University of Science and Technology of China（中国科学技术大学）
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
Nanyang Technological University（南洋理工大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Guangdong University of Technology（广东工业大学）；School of Automation（自动化学院）；Singapore University of Technology and Design（新加坡科技设计大学）；Auburn University（奥本大学）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）；Sungkyunkwan University（成均馆大学）；Hong Kong University of Science and Technology（香港科技大学）
Coherent Without Grounding, Grounded Without Success: Observability and Epistemic Failure
Institut Català de Nanociència i Nanotecnologia (ICN2)（加泰罗尼亚纳米科学与纳米技术研究所（ICN2））；Artificial Intelligence Research Institute (IIIA-CSIC)（人工智能研究所（IIIA-CSIC））
Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval
Department of Intelligent Systems, Delft University of Technology（智能系统系，代尔夫特理工大学）；Department of Chemical Engineering, Delft University of Technology（化学工程系，代尔夫特理工大学）
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Weiyang College, Tsinghua University（清华大学未央学院）；Shanghai Qi Zhi Institute（上海期智研究院）
Graph Alignment Topology as an Inductive Bias for Grounding Detection
Department of Pediatrics, University of Illinois College of Medicine Peoria（伊利诺伊大学皮奥里亚医学院儿科部）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机与数据科学学院）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡莱医学院）
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
University of California, Davis（加州大学戴维斯分校）；Virginia Tech（弗吉尼亚理工大学）；The Chinese University of Hong Kong（香港中文大学）；NVIDIA；Adobe Research（Adobe研究）；Fudan University（复旦大学）；Meta AI
IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）
Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation
The Hong Kong Polytechnic University（香港理工大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；Great Bay University（大湾区大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs
University of Science and Technology of China（中国科学技术大学）；Xiaomi Corporation（小米公司）；State Key Laboratory of Communication Content Cognition, People’s Daily Online（人民日报社传播内容认知国家重点实验室）
