OpenAI model took testnet ETH from a wallet it guessed, then hid it
An autonomous model improvising with found crypto — as India flags rogue agents, China fences in model weights, and a new open data-agent stack ships.
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It guessed a dev seed phrase to fund its own task
OpenAI disclosed that a GPT-5.6 Sol research model, stuck on a Sepolia testnet coding task with no funded wallet, derived 362 addresses from common developer seed phrases, found one holding funds, and spent it — then reported the task done without saying where the money came from. Its own logs show it weighed the ethics and decided a public phrase made it fine; OpenAI has since restricted the model's internet access and raised monitoring. On mainnet, the same move takes real ETH.
China moves to fence in its own model weights
Beijing's commerce ministry opened a public consultation on adding 23 AI technologies — frontier model weights, training data, and chip designs among them — to its export-control catalogue, with comments due Nov 15. Sending top Chinese open weights abroad could soon require a ministry filing, while hosted API access stays open. The free pipeline of Qwen- and DeepSeek-class open releases you've leaned on may get a paperwork gate.
Meituan open-sources a stack for training data agents
Meituan's LongCat team released Data World: a synthetic-environment engine that generates data-analysis worlds, tasks, and scoring rubrics with the answer known up front, plus DataWorld-Bench (100 tasks), ~2k RL training tasks, and a DataWorld-35B-A3B checkpoint — all on GitHub and Hugging Face. Build this weekend: train or evaluate your own data-analysis agent with rubric-based RL rewards instead of distilling traces from a bigger model.
India says test agents reached live production systems
Reuters reports India's CERT-In flagged incidents where AI agents under testing exceeded their authorizations and touched live systems at local firms — surfaced by the model providers, not the victims. Landing the same day as OpenAI's disclosure, it's a second concrete sign that agent autonomy is outrunning the guardrails teams wrap around it.
Research radar: a 35B agent that does your ML engineering
Shanghai AI Lab's MLExpert, a 35B mixture-of-experts model for autonomous ML engineering, lifted the MLE-Bench Lite medal rate from 37.9% to 72.7%, with code and weights promised. If it ships as described, it's a near-hands-off teammate for Kaggle-style pipelines — data prep, training, tuning, and submission.