We sincerely thank all reviewers for the insightful comments and feedback on our work of learning from failure (LfF)

We sincerely thank all reviewers for the insightful comments and feedback on our work of learning from failure (LfF). We do not interpret this as a "true" trade-off, as debiasing does not degrade the model's Instead, we view the apparent underperformance as a result of "not utilizing a (delusional) spurious correlation." Following R1's suggestion, we additionally test ReBias [2] (SOT A among This is also consistent with our claim that LfF is not "domain-specific" However, this consistency may not hold depending on the definition of "domain." Hence, we deeply resonate with R2's concern, and we will further clarify the type of knowledge used by LfF and For example, we will modify L2-5 in the abstract by "In this work, we propose a new algorithm utilizing a However, we only use the LfF's yes/no type of knowledge for choosing one of the attributes as an undesired Following R2's suggestion, we further verify Our LfF combination rule achieves 74.01% We will add more discussions and experiments in the final draft.