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
The paper surveys hallucinations in large language models (LLMs) through a lifecycle lens, covering causes, detection, mitigation, and prevention. It categorizes hallucinations into data‑related, training‑related, and inference‑related stages, aligning each with specific interventions. The authors also review benchmark datasets and propose a standardized framework to diagnose and address hallucinations for safer, more reliable LLMs.
By Naveen Lamba, Sanju Tiwari, Manas Gaur
Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model hallucinations, and heterogeneous mechanisms mak...
arXiv:2601.22984v3 Announce Type: replace
Abstract: Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evalu...
By Yuhao Zhan, Tianyu Fan, Linxuan Huang, Zirui Guo, Chao Huang
arXiv:2609.09895v1 Announce Type: new
Abstract: Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model...
By Xinyu Chen, Adnan Mahmood, Mark Dras
arXiv:2606. 17449v1 Announce Type: cross Abstract: While Multimodal Retrieval-Augmented Generation (M-RAG) enhances Large Vision-Language Models, it remains highly susceptible to cross-modal hallucinations, causal fabrications, and sycophancy.
By Zehang Wei, Jiaxin Dai, Jiamin Yan, Xiang Xiang