A blog post titled “Sparks of Weight-Space Programming,” published by MIT CSAIL researchers, has drawn widespread attention. Experiments show that GPT-6 Astra can improve the performance of small language models on tasks such as mathematics by modifying a small number of weights, and can identify reinforcement learning network functions solely from weights and generate runnable game network parameters from scratch—a result that many have linked to the concept of recursive self-improvement (RSI). However, after reviewing the public experiment logs, it becomes clear that the dramatic score increase of Qwen3.5-0.8B actually stemmed from fixing a bug that caused it to easily fall into thinking loops under specific settings and fail to output complete answers, rather than from adding new mathematical capabilities. On Gemma 3 270M, which rarely exhibits looping problems, similar modifications brought only very limited improvements. In the experiments, all objectives, rules, and tool frameworks were set by humans, and Astra merely automated the specified fixed-point weight debugging operations. It did not truly understand the internal logic of other models, nor did it achieve recursive self-improvement. It is essentially an automated upgrade of the model training process and remains far from the RSI closed loop capable of autonomous understanding and autonomous evolution. Nevertheless, the research also demonstrates a new capability: large models can directly write constructed algorithms into the weights of specified neural networks—and this has already been reproduced on multiple different large models.