Researchers Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab have released a modified Qwen3.5-0.8B checkpoint on Hugging Face. According to the team, OpenAI’s GPT-6 Astra selected the edits by inspecting the model’s parameters, running diagnostics, and evaluating candidate patches. The checkpoint accompanies their project post, Sparks of Weight-Space Programming.

The authors call the method weight-space programming. A weight tensor is a multidimensional array containing model parameters; the workflow gives an LLM access to those arrays, describes a goal in plain English, and provides tools for testing possible changes. Gradient-based training played no role in generating the reported patch.

The score jump tracks fewer reasoning loops

The strongest reported experiment targeted Qwen3.5-0.8B’s math performance. Astra’s intervention changed about 0.016% of the model’s weights and produced the following results:

Before the edit, the base model frequently repeated intermediate reasoning until it exhausted its context window. The authors traced that behavior to a numerical direction associated with repetition and subtracted the pattern from selected weight matrices. Under their diagnosis, the benchmark gains reflect abilities that looping had prevented the model from expressing, including producing a final answer and following output instructions.