Wide Learning: Learning to Reach Evidence
Quick summary
arXiv:2608.29608v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2608.29608v1 Announce Type: cross Abstract: 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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