Frontier LLMs can directly inspect and edit raw neural network weights in Python without gradients or training loops:

Asked to improve Qwen3.5-0.8B at math with no gradient descent or training, GPT-6 Astra uses a Python kernel over the model's 0.8 billion parameters and a budget of 50 forward passes to trace why 85 of the base model's first 96 practice answers run into repetitive thinking loops at the 32,768-token limit. Astra isolates the looping behavior to a single direction written by the MLPs of layers 15 and 19 and subtracts that direction from 64 output columns in each—natively editing 0.016% of the weights (131,072 parameters). That edit cuts token-limit looping on MATH-500 from 93.0% to 33.1%, raising MATH-500 accuracy from 7.9% to 48.1%, GSM8K from 13.8% to 67.1%, and out-of-distribution instruction following (IFEval) from 21.0% to 62.3%.