arXiv AI June 30, 2026 By Mohammed Bousmah

LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators

arXiv:2606. 29437v1 Announce Type: cross Abstract: The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them?

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

arXiv AI

Jul 3

A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content

arXiv:2607. 01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation.

By Yang Zhao, Yingshuo Li, Zeyu Zhang

llms agents

arXiv AI

Jun 10

More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production

arXiv:2601. 11072v1 Announce Type: cross Abstract: Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article.

By Amber Kusters, Pooja Prajod, Pablo Cesar, Abdallah El Ali

arXiv AI

Jul 9

Participatory provenance as representational auditing for AI-mediated public consultation

arXiv:2604. 20711v2 Announce Type: replace Abstract: Artificial intelligence is increasingly deployed to synthesize large-scale public input in policy consultations and participatory processes.

By Sachit Mahajan

nlp safety

arXiv AI

Aug 7

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

arXiv:2607. 28617v2 Announce Type: replace Abstract: System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications.

By Xiangning Lin, Shenzhe Zhu, Shu Yang, Zhenyu Zhang, Haoqian Zhang, Yipeng Zhao, Chengxuan Qian, Tianwei Wang, Ziheng Zhang, Zhenlong Yuan, Dingcheng Wang, Juncheng Wu, Yuan Si, Jiaxin Liu, Baolong Bi, Robert Mahari, Tobin South, Dazza Greenwood, Zexue He, Rishi Bommasani, Sophia Kazinnik, Andreas Haupt, Samuele Marro, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei

llms

arXiv AI

Jul 7

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions

arXiv:2607. 03233v1 Announce Type: cross Abstract: The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intelligence, cybersecurity, and cyber investigation.

By Eduardo Almeida Palmieri, Mohamed Chahine Ghanem, Dipo Dunsin, Zubair Baig, Ed de Quincey, Kim-Kwang Raymond Choo

llms rag agents nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 16

From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents

arXiv:2606. 04990v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.

By Yiqi Wang, Jiaqi Zhang, Taotao Cai, Zirui Liu, Qingqiang Sun, Zequn Sun, Zhangkai Wu, Manqing Dong, Mingkai Zhang, Xuefei Yin, Yanming Zhu

llms agents benchmarks safety

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A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content

More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production

Participatory provenance as representational auditing for AI-mediated public consultation

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions

From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents