While model merging offers a way to combine different skills, it is often impractical due to mismatched neural architectures and the closed-source nature of top-performing models.
大多数人认为模型合并是整合不同AI模型能力的可行方法，但作者明确指出这种方法在实践中存在根本性限制，挑战了行业对模型合并解决方案的普遍信任。
This problem is compounded for proprietary reasoning APIs that expose neither logits nor intermediate token probabilities, leaving practitioners with no reliable uncertainty signal at inference time.
令人惊讶的是：当前许多专有的推理API既不提供logits也不提供中间token概率，这使得实践者在推理时无法获得可靠的不确定性信号。这一被忽视的挑战限制了大型语言模型在实际应用中的可靠性评估，而SELFDOUBT正是为了解决这一特定问题而设计的。