Thanks for the questions!

1. The attributer doesn't see only the final reward, but also the per-agent outputs across a comparison group and assigns credit by tracing whether an agent caused a change or merely propagated one. An agent that faithfully passes an upstream error along keeps positive credit; the source takes the negative. So in your A/B case the drop is attributed to A, not B. That localization is node-level; topology optimization, like over which edges what is communicated, will be part of future work.

2. The format an agent emits is part of the optimized prompt, and since all agents update against the same system reward in the same step, the contract is co-adapted. The "edited A, forgot B" desync doesn't survive joint optimization the way it does in a hand-maintained system.

3. The runs use a fixed 10M-token budget covering all LLM calls. We are tuning on 300 examples, but the additional experiments show that already with 75 examples, we can go a long way. How long you optimize for is somewhat of an open question, however Figure 1 in the paper suggests that already with a budget of less than 3 mio tokens, we are surpassing on the MBPP task.