The editing agent explored changes in a Python environment and evaluated candidate weight modifications without running a gradient-based training loop. The downloadable artifact records the resulting parameters and can be compared with the original Qwen3.5 checkpoint.
Researchers can use the release to examine how direct parameter edits affect a compact model. Its value is in reproducing a controlled comparison and checking for regressions beyond the targeted math task.
