arXiv AI September 1, 2026 By Nora Girda, Adrian Groza

Review Before Trust: Source-Grounded Integrity Gates for AI-Assisted Personal Health Records

The paper introduces a source‑grounded integrity gate for AI‑assisted personal health records, ensuring that data generated by large language models remains provisional until a deterministic monitor verifies it against the source document. The monitor only accepts candidates that contain a unique supporting quotation, appear within the same laboratory row, and preserve provenance, preventing the model from approving its own output. In Medical DataCloud, the system passed all 22 conformance and mutation tests and, in a replay of nine historical lab reports, admitted 72 of 97 numeric candidates while retaining 25 for human review.

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 9

Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture

arXiv:2606. 09500v1 Announce Type: new Abstract: Objective.

By Yoojin Nam, Jinhoon Jeong, Namkug Kim

llms benchmarks

arXiv AI

Aug 11

CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR

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By Veronica Chatrath, Bryan Zhu, George Pu, Jingxuan Fan, Apaar Shanker, Varun Ursekar, Anahita Sharma, Jason Qin, Keqi Han, Soham Dinesh Tiwari, Soham Dan, Vijay Kalmath, Yuan Li, Daniel Yue Zhang, Chenguang Wang, Zainab Doctor, Zhijun Yin, Nigam H. Shah, Yuan Xue

llms agents benchmarks

arXiv Machine Learning

Jun 15

Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops

arXiv:2606. 14149v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions are involved.

By Muhammad Osama, Maheera Amjad, Zartasha Mustansar, Arslan Shaukat, Muhammad U. S. Khan

llms agents safety

arXiv AI

Jul 17

Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control

arXiv:2607. 14890v1 Announce Type: new Abstract: Autonomous coding agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence.

By Jek Huang, Jeffery Hsia, Jiayi Sun, Freddie Shi, Wei Huang, Ian H. White

agents benchmarks

arXiv AI

Jul 13

Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

arXiv:2607. 09349v1 Announce Type: cross Abstract: Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents.

By Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim

rag fine-tuning benchmarks safety

arXiv AI

Aug 11

From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.

By Zijie Zhuang, Changxin Lao, Pengbo Xu, Hanwen Xu, Ruochen Yang, Yingzhi He, Peng Zhang, Jiangxia Cao, Yusheng Huang, Guohong Mu, Jian Liang, Ruiming Tang, Shuang Yang, Zhaojie Liu, Wenwu Ou, Kun Gai

llms agents

One email a morning, machine-written

Related stories

Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture

CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR

Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops

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From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents