john bracke

Building a self-evolving intelligent agent model - MiniMax M2.7 "M2.7 is our first model which deeply participated in its own evolution" - We believe that future AI self-evolution will gradually transition towards full autonomy, coordinating data construction, model training, inference architecture, evaluation, and other stages without human involvement - To this end, we conducted preliminary exploratory tests in low-resource scenarios. We had M2.7 participate in 22 machine learning competitions at the MLE Bench Lite level open-sourced by OpenAI - These competitions can be run on a single A30 GPU, yet they cover virtually all stages of machine learning research. - We designed and implemented a simple harness to guide the agent in autonomous optimization. The core modules include three components: short-term memory, self-feedback, and self-optimization - Specifically, after each iteration round, the agent generates a short-term memory markdown file and simultaneously performs self-criticism on the current round's results, thereby providing potential optimization directions for the next round - The next round then conducts further self-optimization based on the memory and self-feedback chain from all previous rounds - We ran a total of three trials, each with 24 hours for iterative evolution. From the figure below, one can see that the ML models trained by M2.7 continuously achieved higher performance over time - In the end, the best run achieved 9 gold medals, 5 silver medals, and 1 bronze medal. The average medal rate across the three runs was 66.6%, a result second only to Opus-4.6 (75.7%) and GPT-5.4 (71.2%), tying with Gemini-3.1 (66.6%)

Sir, another George Hotz blackpill has hit LinkedIn

Best of luck to Kyle Tucker on the ring-chasing. No regrets where we spent our money over here.

The second year in a row we had Goldman’s top strategists on to talk about the year ahead outlook. Fun tradition. Hatzius and Snider are both awesome