AWhat changed
Evolves sparse network topologies while evaluating each with multiple values of one globally shared weight, reducing dependence on learned individual weight parameters.
Scientific scope
Evolves sparse network topologies while evaluating each with multiple values of one globally shared weight, reducing dependence on learned individual weight parameters. Not a weightless network or performance guarantee for arbitrary independently random weights: all connections share one sampled scalar. Architecture search, repeated rollouts and chosen activation primitives remain substantial optimization. Biological analogies are motivation only; this record concerns artificial simulated-control/image-learning methods. Gaier's star denotes work location, not co-first credit.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Not a weightless network or performance guarantee for arbitrary independently random weights: all connections share one sampled scalar. Architecture search, repeated rollouts and chosen activation primitives remain substantial optimization. Biological analogies are motivation only; this record concerns artificial simulated-control/image-learning methods. Gaier's star denotes work location, not co-first credit.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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