Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.15557.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T18:20:24.983021Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T11:09:47.156658Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c8170476-e30b-4853-80e1-045cdbb0962c · inbound
Momentum-Accelerated Richardson(m) and Their Multilevel Neural Solvers cites this paper.
Momentum-Accelerated Richardson(m) and Their Multilevel Neural Solvers Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:20:24.983021Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db3e288e-2f60-4ce7-ad4b-602a8c5121b7 · inbound
Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations cites this paper.
Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T15:37:10.926998Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03824bab-8795-489d-92ef-58c8186003b6 · inbound
Accurate and scalable deep Maxwell solvers using multilevel iterative methods cites this paper.
Accurate and scalable deep Maxwell solvers using multilevel iterative methods Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:53:33.648714Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d3ec5f1-99fe-4f0c-be51-fda60de31356 · inbound
RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections cites this paper.
RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Reference 2001
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:19:22.703097Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12b0c543-e7d7-44e5-a72f-f8288f741539 · inbound
Factored Sparse Approximate Inverse Preconditioning via Spectral Optimization cites this paper.
Factored Sparse Approximate Inverse Preconditioning via Spectral Optimization Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T11:09:47.158246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.