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.

LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards
Mar 2, 2026Guanzheng Chen, Michael Qizhe Shieh, Lidong BingRL for Language Models Reinforcement Learning with Verifiable Rewards
National University of Singapore · MiroMind AI · absolute AI
Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
Mar 2, 2026Emmanuel Aboah Boateng, Kyle MacDonald, Akshad Viswanathan +1E-Commerce Search Intent Classification
DoorDash, Inc. San Francisco, California, USA
Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Exam
Feb 27, 2026Michael Majurski, Cynthia MatuszekQuery Reformulation LLM Grounding
National Institute of Standards and Technology · University of Maryland Baltimore County
TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining
Feb 27, 2026Zitong Xu, Yuqing Wu, Yue ZhaoNamed Entity Recognition LLM Grounding
Sun Yat sen University, Guangzhou, China · Uber Technologies, Inc., Seattle, USA · Monroe University, New Rochelle, NY, USA
Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem
Feb 25, 2026Heejin JoStructured Reasoning in Language Models Prompt Engineering
Independent Researcher
Enhancing Legal LLMs through Metadata-Enriched RAG Pipelines and Direct Preference Optimization
Feb 25, 2026Suyash Maniyar, Deepali Singh, Rohith ReddyLLM Grounding Language Modeling
Grounding LLMs in Scientific Discovery via Embodied Actions
Feb 24, 2026Bo Zhang, Jinfeng Zhou, Yuxuan Chen +3AI Agents for Scientific Discovery LLM Grounding
Tsinghua University, Beijing, China.
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
Feb 19, 2026Marcelo LabreRetrieval-Augmented Generation LLM Grounding
Advanced Institute for Artificial Intelligence (AI2)
Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
Feb 19, 2026Dun Yuan, Hao Zhou, Xue Liu +4Knowledge Augmentation for Language Models Graph Retrieval-Augmented Generation
Charlie · School of Computer Science, McGill University, Montreal, QC H3A 0E9, Canada. · Standards and Mobility Innovation Lab, Samsung Research America, Plano, Texas, TX 75023, USA.
ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution
Feb 17, 2026Yahia Alqurnawi, Preetom Biswas, Anmol Rao +3Multimodal Large Language Models LLM Grounding
School of Computing and Augmented Intelligence Arizona State University Tempe, AZ 85281, USA
Discovering High Level Patterns from Simulation Traces
Feb 10, 2026Sean Memery, Kartic SubrLLM Grounding Program Synthesis
University of Edinburgh, United Kingdom
MedBeads: An AI-Native Clinical Context Graph Built from Immutable Beads and Reconstructable Clinical Links
Feb 1, 2026Takahito NakajimaKG Construction LLM Grounding
Diagnostic Imaging and Interventional Radiology, Institute of Medicine, University of Tsukuba · Center for Cyber Medicine Research, University of Tsukuba
KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering
Dec 10, 2025Xin Sun, Zhongqi Chen, Xing Zheng +6LLM Grounding RL for Language Model Reasoning
University of Science and Technology of China, Hefei, China · Independent · Institute of Automation, Chinese Academy of Sciences, Beijing, China.
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Nov 6, 2025Yuanning Cui, Zequn Sun, Wei Hu +1Multi-Hop QA LLM Grounding
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing 210093, China +2
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Nov 5, 2025Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius +3LLM Grounding Automated Fact-Checking
Technical University Munich
Multi-Step Knowledge Interaction Analysis via Rank-2 Subspace Disentanglement
Nov 3, 2025Sekh Mainul Islam, Pepa Atanasova, Isabelle AugensteinDisentangled Representation Learning LLM Grounding
University of Copenhagen
BuildArena: A Physics-Aligned Interactive Benchmark of LLMs for Engineering Construction
Oct 18, 2025Tian Xia, Tianrun Gao, Wenhao Deng +4LLM Evaluation LLM Grounding
Westlake University, China · Uniforce AI, China
Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs
May 24, 2025Chen Yang, Ruping Xu, Ruizhe Li +2LLM Grounding LLM Prompting
Zhejiang University of Technology, China1 · Zhejiang Key Laboratory of Visual Information Intelligent Processing, China 2 · University of Aberdeen, UK +1
Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family
Apr 25, 2025Pierre-Carl Langlais, Pavel Chizhov, Mattia Nee +6Retrieval-Augmented Generation LLM Grounding
PleIAs, Paris, France
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
Mar 3, 2025Tin Nguyen, Logan Bolton, Mohammad Reza Taesiri +2LLM Grounding LLM Answer Verification
Auburn University · University of Alberta

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LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards

Guanzheng Chen, Michael Qizhe Shieh, Lidong Bing

National University of Singapore · MiroMind AI · absolute AI

RL for Language Models Reinforcement Learning with Verifiable Rewards LLM Grounding Reward Shaping Long-Context Language Model Evaluation

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Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs) by optimizing them against factual outcomes. However, this paradigm falters in long-context scenarios, as its reliance on internal parametric knowledge is ill-suited for tasks requiring contextual grounding--the ability to find and reason over externally provided information. We identify a key reason for this failure: a reward based solely on the final answer is too sparse to effectively guide the model for identifying relevant evidence. We formally prove that the outcome-only reward leads to significant vanishing gradients for the context grounding process, rendering learning intractable. To overcome this bottleneck, we introduce LongRLVR to augment the sparse answer reward with a dense and verifiable context reward. This auxiliary signal directly incentivizes the model for selecting the correct grounding information, providing a robust learning gradient that solves the underlying optimization challenge. We validate our method on challenging long-context benchmarks using Qwen and LLaMA models. LongRLVR consistently and significantly outperforms the standard RLVR across all models and benchmarks, e.g., boosting a 14B model's scores on RULER-QA from 73.17 to 88.90 and on LongBench v2 from 39.8 to 46.5. Our work demonstrates that explicitly rewarding the grounding process is a critical and effective strategy for unlocking the full reasoning potential of LLMs in long-context applications. Our code is available at https://github.com/real-absolute-AI/LongRLVR.