arXiv AI August 13, 2026 By Diego Gosmar, Deborah A. Dahl

Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching

arXiv:2605. 29055v2 Announce Type: replace Abstract: This paper describes an approach to hallucination detection and mitigation using a HOPE-inspired Nested Learning architecture with Continuum Memory Systems (CMS) and semantic similarity caching, tested on a hybrid benchmark of 310 prompts (217 epistemic-uncertainty prompts, 93 fabrication-induction stress tests).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 11

From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

arXiv:2606. 07537v1 Announce Type: cross Abstract: Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales.

By Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin, Golam Mostofa Naeem

llms safety

arXiv AI

Jul 7

Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)

arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.

By Mohamed Aly Bouke

rag nlp benchmarks safety

arXiv AI

Jul 21

Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI

arXiv:2607. 17883v1 Announce Type: cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true.

By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu

llms agents safety

arXiv AI

Aug 11

REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment

arXiv:2608. 07931v1 Announce Type: new Abstract: Large reasoning models (LRMs) are prone to hallucination, which undermines their reliability and poses challenges for safe deployment.

By Zhengze Huang, Luyang Yu, Di Hong, Xinzhe Huang, Wanyu Lin, Zhixuan Chu, Zhan Qin, Tianhang Zheng

benchmarks safety

arXiv AI

Aug 5

UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space

arXiv:2608. 03817v1 Announce Type: cross Abstract: Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence.

By Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia, Mohamad Amin Yousefi, Asal Mohammadjafari Mamaqani, Behrad Samimi, Mohammad Hossein Rohban

llms multimodal benchmarks safety

arXiv AI

Aug 12

Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique

arXiv:2608. 10430v1 Announce Type: cross Abstract: Large Language Models (LLMs) deployed as AI agents frequently exhibit user specification-grounding failures, executing hallucinated, undesired actions to force a resolution rather than expressing uncertainty.

By Sanidhya Vijayvargiya, Rahul Lokesh

llms agents diffusion robotics fine-tuning efficiency safety

Related stories

From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

Detecting Hallucinations in Retrieval-Augmented Generation through Grounding-Aware Sensitivity by Perturbation (GASP)

Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI

REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment

UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space

Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique