AWhat changed
Combines positional/structural encodings, local edge-aware message passing and global attention in a modular graph-learning architecture and implementation.
Scientific scope
Combines positional/structural encodings, local edge-aware message passing and global attention in a modular graph-learning architecture and implementation. Six source authors; star denotes correspondence, not equal contribution. Linear node/edge cost requires a linear-attention instantiation and excludes encoding precomputation such as Laplacian eigendecomposition; full attention remains quadratic. Universality argument needs sufficiently rich/full eigenvectors and suitable modules, not every practical instantiation. Non-biological graph methods only.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Six source authors; star denotes correspondence, not equal contribution. Linear node/edge cost requires a linear-attention instantiation and excludes encoding precomputation such as Laplacian eigendecomposition; full attention remains quadratic. Universality argument needs sufficiently rich/full eigenvectors and suitable modules, not every practical instantiation. Non-biological graph methods only.
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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