LLM Grounding

LLM: Large Language Model

LLM grounding studies whether a model’s answers, explanations, and actions are supported by the context it receives, structured knowledge, or observations of the world. Common approaches include retrieval-augmented generation, knowledge graphs and ontologies, tool use, evidence-linked training, and verification methods that check claims or support abstention. Work also examines grounding in images, audio, video, spatial data, and interactive environments, with evaluations measuring evidence use rather than answer accuracy alone. A central challenge is that supplying evidence does not ensure models use it: parametric knowledge, misleading context, and weak evidence selection can still lead to unsupported answers, while stronger verification can add cost and latency.

Latest papers

320 papers33 papers in the last four weeks, down 13% on the four weeks before. 0.2% of all new papers.

Category-Anchored Large Language Models for Next POI Recommendation
Oct 29, 2026Lexi Bai, Xiaopeng Zeng, Xun Zhou +2Sequential Recommendation Large Language Model-Based Recommendation
Harbin Institute of Technology, Shenzhen, China · City University of Hong Kong, Hong Kong SAR, China.
PRISM-BENCH: Diagnosing Grounded and Faithful Multimodal Reasoning Beyond Final-Answer Accuracy
Oct 27, 2026Vansh Bhatia, Mehdi Rezagholizadeh, Parsa Ashrafi Fashi +3MLLM Evaluation Multimodal Model Evaluation
Advanced Micro Devices, Inc. · University of Toronto
OA-RAG: Ontology-based Agentic RAG for Faithful LLM Reasoning
Oct 26, 2026Hui Yang, Jiaoyan Chen, Yongsheng GaoAgentic RAG Retrieval-Augmented Generation
The University of Manchester, Manchester, United Kingdom · SNOMED International, London, United Kingdom
Scoped Semantic Invariance as a Foundation for LLM Reliability
Oct 23, 2026Rosario Uceda-Sosa, Maria Chang, Karthikeyan Natesan Ramamurthy +1Reasoning Consistency in Language Models LLM Reliability
IBM T. J. Watson Research Center, New York, USA
From Deliberately Constructed Systemic Representations of the World of Activity to Dialogue: A Five-Level Communication Architecture for Strong Language-Based ASI
Oct 23, 2026Maxim OsovskyLLM Grounding Knowledge Representation
Adam University, Bishkek, Kyrgyzstan
LeafSkill: Efficient Leaf-Level Action Grounding for Recursive LLM Agents
Oct 23, 2026Shen Liu, Ze Zhou, Zhenguo Xu +3LLM Agent Skill Retrieval LLM Grounding
North China Institute of Computer System Engineering, Beijing, China · Beijing University of Aeronautics and Astronautics, Beijing, China · University of Science and Technology of China, Hefei, China +1
When More Metadata Misleads: Probing Field-Level Grounding in LLM-Generated Museum Descriptions
Oct 22, 2026Clemencia Siro, Laura HollinkGrounded Text Generation LLM Grounding
Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands
Better Reasoning, Not Just Bigger Models: A Structure-Grounded Framework for Efficient and Reliable Inference
Oct 22, 2026Amirhossein Aghaei, Liangyan Li, Shiyi Yang +2LLM Reasoning with Graphs LLM Grounding
Huawei Research Canada, Markham, Canada · Huawei Research Canada, Waterloo, Canada · Huawei Technologies Co., Ltd., Beijing, China +1
Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories
Oct 21, 2026Sujoy Roychowdhury, Joseph H. R. Isaac, Jiangning Gao +2Cross-Modal Retrieval Semantic Search
Ericsson R&D
AI Mindmeld: A Latent Interconnect Protocol
Oct 21, 2026Siavash Golkar, Jake Kovalic, Shirley HoLarge Language Model-Guided Scientific Discovery Cross-Modal Representation Learning
Polymathic AI · Center for Data Science, New York University · Department of Applied Physics, Yale University +3
When Copying Is Hard: Copy-Constrained Decoding for Exact Span Reproduction
Oct 20, 2026Jinghui Zhang, Lang Gao, Zongfang Liu +5Language Model Decoding Constrained Decoding
MBZUAI · Zhejiang University · Westlake University +4
Recognizing Atari and Assessing Sente: Controlled Evaluation Tasks for Language-Based Go Agents
Oct 16, 2026Taiyi Chen, Iskar Deng, Amandalynne PaulladaGame-Playing Agents AI Agent Evaluation
University of Washington
Training Reasoning Models to Generate Diverse Scientific Ideas
Oct 15, 2026Manya Wadhwa, Pranav Narayanan Venkit, Hiroaki Hayashi +3Diverse Text Generation Scientific Hypothesis Generation
New York University · Salesforce AI Research · Stony Brook University
CLUE: Uncovering Social Context for Pragmatic Understanding
Oct 15, 2026Chengfei Wu, Zhaoqing Wu, Dan GoldwasserPragmatic Reasoning in Language Models Social Reasoning
Purdue University
Which Knowledge Should LLMs Trust? Uncovering a Generalizable Steering Direction Across Knowledge Conflicts
Oct 14, 2026Yibin Lou, Yi Hu, Jianhui Chen +3Knowledge Conflicts in Language Models LLM Grounding
State Key Laboratory of Multimedia Information Processing, Peking University · School of Computer Science, Peking University · Southern University of Science and Technology +2
Recognition Is Not Grounding: Benchmarking Multilingual Financial Evidence Extraction
Oct 14, 2026Lingfei Qian, Yan Wang, Xueqing Peng +6Financial Language Model Evaluation Document Information Extraction
The Fin AI, USA · University of Manchester, UK · Georgia Institute of Technology, USA +2
Can LLMs Access their World Knowledge for Event Prediction?
Oct 13, 2026Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi +2Event Time Prediction LLM Evaluation
RBC Borealis · University of Alberta
Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports
Oct 13, 2026Sachin KumarLanguage Model Generation Evaluation LLM Grounding
LexisNexis, USA
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit
Oct 13, 2026Hoang Pham, Duc Anh Vu, Duy Tung Doan +2RL for RAG RAG Citation Faithfulness
Nanyang Technological University · Viettel AI, Viettel Group · VinUniversity
Communication, Vocalisation, and Language
Oct 12, 2026Peter WallisLLM Grounding Cultural Knowledge in Language Models
Phantom Merge: When Your Large Language Model Agents Pick One but Tell You About Another
Oct 12, 2026Wangshu Zhu, Qianwen Guo, Yushun DongLLM Grounding LLM Agent Reliability
Department of Computer Science, Florida State University · Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering
Relational Semantic Grounding for Test-Time Discovery
Oct 10, 2026Tianle Liu, Gongbo Cheng, Chenggong Ni +4LLM Grounding
School of Electronics and Information Engineering, Suzhou University of Science and Technology · School of Intelligence Science and Technology, Nanjing University · School of Computer Science Engineering, North Minzu University +1
GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks
Oct 8, 2026Amirhossein Vahdat, Thierry Badard, Luc LamontagneLLM Evaluation Geospatial Reasoning Benchmarks
Département des Sciences Géomatiques, Université Laval, QC, Canada · Département d’informatique et de génie logiciel, Université Laval, QC, Canada
Automated Assembly Instruction Generation from CAD Models Using Grounded Large Language Models: A Human-in-the-Loop Framework
Oct 8, 2026Aaron Dsouza, Mohammed Azeez Khan, Ashutosh Mishra +3Grounded Text Generation LLM Grounding
Department of Electronics and Communication Engineering, National Institute of Technology Warangal, India · Department of Computer Science and Engineering, National Institute of Technology Warangal, India · Department of Computer Science and Engineering, Nawab Shah Alam Khan College of Engineering and Technology, Hyderabad, India +2
When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection
Oct 7, 2026M. Mikail Demir, M. Abdullah CanbazHallucination in RAG Legal Citation Verification
University at Albany, SUNY Albany, New York, USA
AI4Fire: Evaluating Large Language Models on Wildfire Tasks
Oct 7, 2026Yue Zhao, Zuobin Xiong, Xiyang Hu +3LLM Evaluation LLM Grounding
University of Southern California · University of Nevada, Las Vegas · Arizona State University +1
Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing
Oct 6, 2026Remo Grillo, Lukas Klic, Giovanni ColavizzaLLM Grounding Knowledge Graphs
University of Bologna, Via Zamboni 33, 40126 Bologna, Italy · I Tatti — The Harvard University Center for Italian Renaissance Studies, Via di Vincigliata 26, 50135 Florence, Italy · Department of Communication, University of Copenhagen, Karen Blixens Plads 8, 2300 Copenhagen S, Denmark
LOGIC: An LLM Benchmark for Intent-Grounded Change Impact in Aerospace Electrical Systems
Oct 6, 2026Muhammad Faraz Shoaib, Muhammad Qasim, Raisulhaq Mohammed Rizwan +3LLM Grounding
School of Computing Wichita State University 1845 Fairmount St. Wichita, KS 67260 · School of Computing Department of Biomedical Engineering Wichita State University 1845 Fairmount St. Wichita, KS 67260 · School of Computing Department of Industrial Engineering Wichita State University 1845 Fairmount St. Wichita, KS 67260
Grounding Probes: Generator-Independent Hallucination Detection from Observer Model Hidden States
Oct 3, 2026Michael Rathmayr, Ádám Kovács, Gábor RecskiHallucination in RAG LLM Grounding
TU Wien, Austria · KR Labs
TCMClinicalReason-Bench: Can Language Models Reason from Pathogenesis to Prescription over Real-World Clinical Cases?
Oct 3, 2026Jirui Dai, Chenkai Zhang, Yan Jia +4Traditional Chinese Medicine Clinical Language Model Evaluation
the Department of Computer Science, Johns Hopkins University, Baltimore, USA · the School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China · the School of Information Engineering, Huzhou University, Huzhou, China +3

Oct 29, 2026J/K move · Enter open · S save

Category-Anchored Large Language Models for Next POI Recommendation

Lexi Bai, Xiaopeng Zeng, Xun Zhou, Yuanshao Zhu+1

Harbin Institute of Technology, Shenzhen, China · City University of Hong Kong, Hong Kong SAR, China.

Sequential Recommendation Large Language Model-Based Recommendation LLM Grounding

Open paper AskPDF arXiv

Next point-of-interest (POI) recommendation aims to predict a user's next destination from spatio-temporal check-in data, and serves as the foundation for many location-based services. Recent advances incorporate large language models (LLMs) into this task by textualizing user trajectories to exploit their semantic reasoning ability. However, directly generating POI identifiers with such models is unreliable: POIs are sparse, localized, and long-tailed entities with weak natural-language semantics, which creates a semantic-to-entity gap between high-level mobility intent and concrete POI prediction. To bridge this gap, we propose ANCHOR, a category-anchored framework for semantic-to-entity grounding in next POI recommendation. Specifically, ANCHOR first estimates category-level mobility intent in a compact and stable semantic space, rather than predicting concrete POI entities directly. The inferred intent is then grounded into a candidate set by integrating three complementary sources of mobility evidence: category anchors, historical visit memory, and user preferences. Finally, a history-aware reranker selects the target POI by matching each candidate with the user's historical mobility patterns and current context. Experiments on three real-world check-in datasets show that ANCHOR consistently outperforms representative traditional, structure-enhanced, and LLM-based baselines on the reported metrics. Further analyses validate the effectiveness of semantic-to-entity grounding and show favorable trade-offs among candidate coverage, ranking quality, and inference efficiency.