IWIBench lets large models rewrite a small model's weights: structured output scores zero across all ten editors, while alias edits hit the ceiling
IWIBench turns gradient-free behavioural weight editing into a scored, controlled task: an editor model gets only four tools for reading and writing weights, running the target and inspecting it, and must make ten kinds of semantic change to Gemma 3 4B IT, after which fresh processes compare target success and control retention before and after the patch. Across ten configurations on the same thirty episodes, 6.1-sol at medium reasoning leads with a macro reward of 0.4601 and Astra follows at 0.4356; no editor scores on structured-response defaults, and only 5.6-terra earns positive reward on answer-length control.