LLM Hallucination Mitigation

LLM: Large Language Model

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9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

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Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding
Jul 7, 2026Daniel Maninger, Leon Chemnitz, Jannis Brugger +3Retrieval-Augmented Generation Constrained Decoding
Technische Universität Darmstadt, Darmstadt, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · Pariton AI, Berlin, Germany +1
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
Jul 7, 2026Kaishen Wang, Tong Zheng, Xuehao Cui +3LLM Hallucination Mitigation RL for Language Model Reasoning
Department of Computer Science, University of Maryland, College Park
Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation
Jul 6, 2026Mohammed Saim Ahmed Quadri, Yunzhe Xue, Justin W. Ady +1Retrieval-Augmented Generation LLM Hallucination Mitigation
Department of Computer Science, New Jersey Institute of Technology Newark, NJ, USA · Department of Data Science, New Jersey Institute of Technology Newark, NJ, USA · Vascular and Endovascular Surgery, Robert Wood Johnson Hospital New Brunswick, NJ, USA
Grounded Optimization: A Layered Engineering Framework for Reducing LLM Hallucination in Automated Personal Document Rewriting
Jul 1, 2026Shashank Indukuri, Adarsh AgrawalLLM Grounding LLM Hallucination Mitigation
Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination
Jun 30, 2026Vijay Vankadaru, Asha Matthews, Tanya Roosta +1LLM Hallucination Mitigation LLM Interpretability
School of Information University of California, Berkeley California, USA
When the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented Dialogue
Jun 30, 2026Mohammad Alijanpour Shalmani, Alale Rezvani Boroujeni, Jiann Shiun YuanAI Agent Reliability LLM Hallucination Mitigation
College of Engineering and Computer Science, University of Central Florida, Orlando, Florida · Department of Marketing, University of Central Florida, Orlando, Florida
Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement
Jun 25, 2026Igor ItkinAI Agent Reliability Multi-Agent Coordination
Independent Researcher, Tel Aviv, Israel
TTFT-Aware Graph Chain-of-Thought:Distance-Indexed Neural A* for Low-Hallucination Multi-Hop Medical Reasoning
Jun 22, 2026Bechir Dardouri, Kaïs Zhioua, Yassine MsaddakInference-Time Search LLM Hallucination Mitigation
Tanit Healthcare Technologies, Tunisia
Hallucination as Context Drift: Synchronization Protocols for Multi-Agent LLM Systems
Jun 19, 2026Carson RodriguesMulti-Agent LLM Systems LLM Hallucination Mitigation
Celabe
Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
Jun 16, 2026Divyansh Srivastava, Shreya Ghosh, Anshul Verma +1Multi-Agent LLM Systems LLM Hallucination Mitigation
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Department of Computer Science and Engineering, School of Electrical and Computer Sciences (SECS), Indian Institute of Technology Bhubaneswar, India · Department of Computer Science2026 Banaras Hindu University, Varanasi, India
LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI
Jun 16, 2026Lalit Yadav, Akshaj GurugubelliLLM Hallucination Mitigation Multi-Agent Debate
Independent Researcher, Sunnyvale, CA, USA · Independent Researcher, San Diego, CA, USA
BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation
Jun 14, 2026Ning Li, Zixuan Guo, Yan Xu +7RL for Language Models LLM Hallucination Mitigation
1Shanghai Jiao Tong University · 2Tencent · 3The
Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops
Jun 12, 2026Muhammad Osama, Maheera Amjad, Zartasha Mustansar +2Multi-Agent LLM Systems LLM Hallucination Mitigation
Data Science and Machine Learning Lab, SINES, NUST · CEME, NUST
NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning
Jun 11, 2026Feng Lyu, Huiqin Yan, Sijing Duan +5LLM Hallucination Mitigation Text Summarization
Central South University · Tsinghua University · Nanjing University +2
SafeLLM: Extraction as a Hallucination-Resistant Alternative to Rewriting in Safety-Critical Settings
Jun 11, 2026Julia Ive, Felix Jozsa, Evridiki Georgaki +6Retrieval-Augmented Generation LLM Grounding
Institute of Health Informatics, University College London, London, UK · National Hospital for Neurology and Neurosurgery, Queen Square, London, UK · Somerset NHS Foundation Trust, UK +2
Goal-Autopilot: A Verifiable Anti-Fabrication Firewall for Unattended Long-Horizon Agents
Jun 10, 2026Youwang DengAI Agent Reliability LLM Hallucination Mitigation
EpistemicaLab — Independent Research
Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
Jun 4, 2026Christopher J. Wedge, Joshua Stutter, Danny Dixon +1Multi-Hop QA Tool-Augmented Language Model Agents
National Innovation Centre for Data, Newcastle University
Hybrid Adversarial Defence for Natural Language Understanding Tasks
Jun 3, 2026Manar Abouzaid, Yang Wang, Chenghua Lin +1LLM Hallucination Mitigation LLM Security
School of Electronics and Computer Science, University of Southampton, UK · Department of Computer Science, University of Manchester, UK
Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
Jun 3, 2026Saroj MishraLLM Hallucination Mitigation Hallucination in Language Models
University of North Dakota
Building Reliable Long-Form Generation via Hallucination Rejection Sampling
Jun 2, 2026Lin Li, Georgia Channing, Suhaas M Bhat +2Long-Form Document Generation LLM Hallucination Mitigation
Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation
Jun 2, 2026Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg +4LLM Hallucination Mitigation Hard Negative Mining
University of Illinois Chicago · Microsoft
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization
Jun 2, 2026Mingkuan Zhao, Wentao Hu, Tianchen Huang +6LLM Hallucination Mitigation Hallucination in Language Models
Xi’an Jiaotong University · Xingchen AGI Lab, China Telecom AI Technology (Beijing) Co., Ltd. · University of Science and Technology of China +2
Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
May 31, 2026Yiming Liao, Zeno Franco, Jose Eduardo Lizarraga Mazaba +1LLM Self-Correction LLM Hallucination Mitigation
University of Maryland, Baltimore County Baltimore, Maryland, USA · Concordia University Wisconsin · Medical College of Wisconsin Milwaukee, Wisconsin USA
Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
May 30, 2026S M Tahmid Siddiqui, Akib Jawad Ononto, Anoop Singhal +1LLM Hallucination Mitigation Question Answering
The University of Texas at Dallas, Richardson, TX 75080, USA · National Institute of Standards and Technology, Gaithersburg, USA
Citation Grounding: Detecting and Reducing LLM Citation Hallucinations via Legal Citation Graphs
May 30, 2026Volodymyr OvcharovLegal Citation Verification LLM Hallucination Mitigation
LEX AI LLC, Kyiv, Ukraine
Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
May 30, 2026Hanze Li, Jinhao You, Yichen Guo +3Language Model Decoding LLM Hallucination Mitigation
De Artificial Intelligence Lab
Score $\times$ Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
May 30, 2026Yun-Chen Cheng, Che-Yu Lin, Cheng-Lin YangLanguage Model Decoding LLM Hallucination Mitigation
CyCraft AI Lab, Taiwan
Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching
May 27, 2026Diego Gosmar, Deborah A. DahlAI Agent Reliability Multi-Agent LLM Systems
Head of AI, Tesisquare · Member, Open Voice Interoperability Initiative · Linux Foundation AI & Data +4
Hallucination Detection-Guided Preference Optimization for Clinical Summarization
May 27, 2026Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi +6LLM Self-Refinement LLM Hallucination Mitigation
1Manning College of Information and Computer Sciences · 2Ensemble HP · 3Columbia University +1
MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
May 27, 2026Hyeonjeong Ha, Jeonghwan Kim, Cheng Qian +7Memory-Augmented Language Models LLM Hallucination Mitigation
University of Illinois Urbana-Champaign · Capital One · Columbia University

Jul 7, 2026, cs.SEJ/K move · Enter open · S save

Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding

Daniel Maninger, Leon Chemnitz, Jannis Brugger, Tushar Lamba+2

Technische Universität Darmstadt, Darmstadt, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Darmstadt, Germany · Pariton AI, Berlin, Germany+1

Retrieval-Augmented Generation Constrained Decoding LLM Hallucination Mitigation Code Generation

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Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex and evolving API specifications. Although LLMs are increasingly used for code generation, previous work has empirically shown that their ability to generate correct web API integrations is limited. At the same time, mitigation techniques and their effectiveness for this setting remain insufficiently understood. In this paper, we propose and systematically evaluate retrieval-augmented generation (RAG) and constrained decoding (CD) as two complementary approaches to improving LLM-generated web API invocation code. For RAG, we design a retriever that processes OpenAPI specifications and retrieves compact endpoint representations to inject into model prompts. For CD, we introduce an automatic translation from OpenAPI specifications to regex-based constraints enforced during generation. We evaluate both approaches on WAPIIBench's existing synthetic dataset and on a new real-world dataset derived from GitHub repositories. Our results show that RAG reduces hallucinations and improves correctness when generating full API invocations but reduces it when the endpoint is already provided as it encourages the generation of unnecessary parameters. In contrast, CD reliably prevents illegal URLs, HTTP methods, and arguments and substantially improves overall correctness for both starter codes.

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