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TLDRI attempted to replicate “Sparks of Weight-Space Programming” by Takehi et al (2026) with stratification on two reasoning models, GPT 6 Astra and Deepseek-V4.1-Flash. The task for the LLMs is to identify the game a ML model is trained for giving the weight of the model (using exact same prompt and weight defined by Takehi).
Although both of these models do well on the surface (4/4 correct for Astra and 3/4 correct for Flash), the stratification approach exposes the difference in mechanism between these two models in approaching the problem. The experiment result shows that Flash depends a lot on channel label for the answer (while Astra's performance does not degrade when channel label does not present). Additionally, Astra is able to identify the game with good accuracy using only the first layer of weight or as few as 1000 weight values selected at random out of 17253 weight values (Flash's performance degrade substantially on these).
So Astra survives this first round of stratification tests just for now. Comparing the results between Astra and Flash, it is obvious that Flash is relying heavily on prompt metadata instead of reading the weights.
BackgroundOn November 2 an article “Sparks of Weight-Space Programming” was published by Takehi et al (2026). The article is cleanly written and one of the claims is that by looking at model weights, GPT 6 Astra can tell what the model is trained to do. The article reports that Astra is able to name the game a ML model is trained for in four different cases (Breakout, Pong, Pac-man, Freeway) directly through the weight of the model without use of external tools.
If LLMs do have capability to read model weights and identify what it is trained to do, this would have huge implications in the fields of interpretability and weight auditing without evaluation as suggested by the article by Takehi et al (2026).
I find the reported results to be very surprising, so I attempted to replicate their experiment using exactly the