Knowledge Manifold Optimization for Scientific Discovery
The rapid expansion and specialization of scientific literature make it increasingly difficult to identify not only what is known, but where new and still supportable knowledge may emerge. We introduce Knowledge Manifold Optimization (KMO), a discovery-oriented extension of the Knowledge Manifold framework that treats scientific discovery as a multiobjective search over a continuous, literature-derived knowledge space. Candidate locations are evaluated using four complementary objectives: persistent knowledge-regime boundary strength, band-pass white-space novelty, semanticgradient magnitude, and evidential support. Rather than collapsing these criteria into a single weighted score, KMO i...
| Authors | Shigeru Obayashi, Yoshiaki Kawagoe, Kazuhiko Komatsu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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