The article titled "Where Does the Money Go Across Long-Running Coding Agents?" explores the financial aspects of maintaining and operating coding agents over extended periods. It highlights the costs associated with controlling these agents and examines how resources are allocated during their lifecycle.
By Daniel Miller
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
The article "Coding Agents Can Solve the Task — But How Much Work Did They Waste?" examines the performance of coding agents using real repositories and seeded agent decisions. It reports on 50,400 runs, highlighting metrics such as wasted turns, patch churn, and the effectiveness of different harness rules. The study provides insights into how these agents manage code modifications and the efficiency of their decision-making processes.
By Emmimal P Alexander
SR‑Fraud is a framework that uses a frozen, stateless LLM agent to score transactions in real time while an offline reflection agent proposes boundary hypotheses based on matured errors. The system then verifies these hypotheses deterministically before updating its knowledge state. On a production payment‑fraud benchmark, SR‑Fraud outperforms both static and periodically retrained CatBoost models and successfully detects an emerging fraud burst.
By Xuwei Tan, Yao Ma, Xueru Zhang
arXiv:2610.16449v1 Announce Type: cross
Abstract: Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency fund flows. Existing Graph Neu...
By Runang He, Tongya Zheng, Jing Cao, Yucheng Ying, Shunyu Liu, Bingde Hu, Yang Gao, Zeyuan Hu, Jianrong Zhang, Mingli Song
The article discusses OpenAI’s focus on Recursive Self‑Improvement (RSI), which the author suggests may represent a new form of AGI. It highlights how OpenAI’s research team is employing coding agents and notes a significant rise in AI spending per researcher in late July, likely linked to internal access to a model later released as GPT‑6 Astra. The piece references related essays and includes a chart illustrating the growth of agentic engineering at OpenAI.