这篇论文提出了学习系统评估的新视角，关注学习如何改变获取证据的能力，而非仅从已有证据中推断。
该研究提出了一种新的学习范式，称为Wide Learning，关注学习者状态如何影响其获取证据的能力。研究通过一个受控实验证明，在两个具有相同公共观察规律的世界中，学习可以改变学习者的有效认知范围。在校准前，一个诊断尝试的实现概率仅为1/1024，低于0.95的阈值；校准后，实现概率达到1。
Wide Learning: Learning to Reach Evidence
Machine learning is usually evaluated after an evidence interface has been fixed. A dataset, sensor suite, query language, action set, or experimental protocol determines which observations can be obtained, and learning is judged by what it extracts from them. We study a complementary capability. A learner's state can determine which evidence-generating experiments it can reliably realise under bounded resources, even when primitive affordances remain fixed. We call this learner-relative experiment family its effective epistemic reach, and use Wide Learning for task-relevant learning-induced changes in that family.We formalise effective reach relative to learner state, deployment budget, reliability threshold, and evaluation distribution. In a controlled construction, two hidden worlds have exactly the same public observation law. An informative diagnostic exists in a fixed five-primitive substrate. Before calibration, one address attempt realises it with probability at most $2^{-10} = 1/1024$, below a pre-specified 0.95 threshold; after calibration, held-out realisation is 1. Public-channel total variation is 0, whereas the realised diagnostic has total variation 1, and sealed binary risk moves from approximately 1/2 to 0. The construction establishes that learning can change effective epistemic reach even when primitive affordances and deployment resources are held fixed. It opens a complementary evaluation question for learning systems: not only what they infer from available evidence, but what informative evidence experience teaches them to bring within reach.