arXiv:2607. 27859v1 Announce Type: cross Abstract: Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations.
By Girish G N, Ashutosh Sahoo, Ajay Bhat, Akshay SP, Gurukiran S, Parag Paul, Dhanashekar Kandaswamy
arXiv:2605. 25815v4 Announce Type: replace Abstract: Agent-to-Agent (A2A) networks enable autonomous AI agents to collaborate by sharing reusable problem-solving instructions.
By Qiming Ye, Peixian Zhang, Yupeng He, Zifan Peng, Gareth Tyson
arXiv:2606. 26028v2 Announce Type: replace-cross Abstract: As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy?
By Xihan Xiong, Zelin Li, Wei Wei, Qin Wang, William Knottenbelt, Zhipeng Wang
arXiv:2512. 16167v3 Announce Type: replace-cross Abstract: Decentralized LLM-based multi-agent service economies face three vulnerabilities that undermine traditional trust mechanisms: reduced cost of fraud, difficulty in evaluating service quality, and instability of service content.
By Jiye Wang, Shiduo Yang, Ting Qiao, Jiayu Qin, Jianbin Li, Yu Wang, Yuanhe Zhao
arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.
By Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos, Maryline Laurent, Jackson Walters
The paper introduces the Agentic Commerce Bench (ACB), a benchmark for measuring fraud in AI agents that autonomously spend money. It presents a taxonomy of agentic commerce fraud, a dataset of twenty fraud classes derived from real production data, and an open‑source detector stack called gordonguard for auditing and replaying hostile counterparties. The study shows that current reasoning layers and security scanners perform poorly on many classes, highlighting the need for better detection mechanisms.
By Ankit Srivastava, Debjyoti Paul