Anthropic redesigned its source control system, creating a custom alternative called Scion to replace Git. The change was driven by a dramatic rise in code changes—from about 2,500 per day in May 2026 to over 30,000 per day by September—requiring a system that could handle a 100 GB main‑branch history and 25 million daily checkouts. Scion was built by a seven‑person team, migrated from Git in five months, and includes a new code‑review system named Antique, while offline support was deemed unnecessary due to Claude’s constant online use.
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.
arXiv:2609.11987v1 Announce Type: cross
Abstract: An agentic coding system couples a language model to a harness: the tools, prompts and control flow that turn a chat model into an autonomous softwar...
By Mohsen Arjmandi
arXiv:2606. 13449v1 Announce Type: cross Abstract: AI-agents (e.
By Ali Arabat, Mohammed Sayagh
The paper introduces GRADE, a graph-based representation of large language model (LLM) agent executions that captures both execution steps and their dependencies. By adding graded dependency edges—observed, declared, or inferred—to the trace, the authors evaluate how this dependency layer affects failure prediction across six corpora involving tool use, coding, and web tasks. Experiments show that the dependency block can improve prediction in some settings, but its effectiveness varies with the evaluation probe and corpus, and controlled experiments demonstrate that the observed structure is not merely a degree-matched artifact.
By Yue Zhao
The paper audits two single‑investigator AI‑assisted research repositories to assess how well they record provenance of AI tool use. One repository contains a prescriptive trace of rule files, with 85.6% of committed lines still visible, while the other holds a narrative trace of an agent’s scratchpad, showing only 21.8% visibility. The study finds that repository‑level indicators such as the Co‑Authored‑By trailer are inconsistent and unreliable for measuring AI involvement, and it outlines which provenance claims each trace type can support or fail to support.
By Koutian Wu, Zesen Huang, Hejia Geng