arXiv AI July 3, 2026 By Shashank Indukuri, Adarsh Agrawal

Grounded Optimization: A Layered Engineering Framework for Reducing LLM Hallucination in Automated Personal Document Rewriting

arXiv:2607. 01457v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to resume optimization for applicant tracking systems, introducing hallucination failures distinct from general text generation: anachronistic technology injection, cross-domain terminology contamination, structural mutation, and content fabrication.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 24

Grad Detect: Gradient-Based Hallucination Detection in LLMs

arXiv:2606. 24790v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations.

By Anand Kamat, Daniel Blake, Brent M. Werness

llms benchmarks safety

Hugging Face Trending Papers

Jun 23

Grad Detect: Gradient-Based Hallucination Detection in LLMs

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these hallucinations is critical for deploying LLMs reliably in high-stakes applications.

llms benchmarks safety

arXiv AI

Jun 3

Building Reliable Long-Form Generation via Hallucination Rejection Sampling

arXiv:2606. 03628v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsupported content, which undermines their reliability.

By Lin Li, Georgia Channing, Suhaas M Bhat, Gabriel Davis Jones, Yarin Gal

llms 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

arXiv AI

Aug 11

Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States

arXiv:2608. 08024v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations.

By Zakhar Mrykhin, Valentin Malykh

llms rag safety

arXiv AI

3d ago

Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory

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

agents benchmarks safety

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Grad Detect: Gradient-Based Hallucination Detection in LLMs

Grad Detect: Gradient-Based Hallucination Detection in LLMs

Building Reliable Long-Form Generation via Hallucination Rejection Sampling

Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique

Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States

Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory