The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
Traditional evaluations measure a learning algorithm's final performance on an i.i.d. test set, reducing learning to a single aggregate score. This approach obscures a fundamental question: to what extent does learning from a specific example generalize to others? Such per-sample generalization, akin to learning by analogy in human cognition, captures how far the knowledge extracted from one example can transfer, yet remains invisible to standard benchmarks. We introduce the Generalization Spectrum, an evaluation framework designed to expose this hidden dimension. For each training example, we construct a controlled suite of test variants arranged by increasing transfer distance, from exa...
| Authors | Jinghan Zhang, Zerui Cheng, Shiqi Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 25 June 2026 |
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