Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.05148.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T04:14:11.793614Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T17:05:50.959533Z
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 d07acfa5-cdb2-4adc-9ee4-6ffab2d6ee43 · inbound
Kernel Contracts: A Specification Language for ML Kernel Correctness Across Heterogeneous Silicon cites this paper.
Kernel Contracts: A Specification Language for ML Kernel Correctness Across Heterogeneous Silicon Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T22:34:06.945438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 460bdcaa-f0e0-4cff-b913-c2da3200a160 · inbound
Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models cites this paper.
Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T19:21:07.510558Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5205cc44-7559-4502-bcac-8e8228902eb6 · inbound
Self-Verifying Measurement Records: Hash-Linked Evidence Graphs for Hardware Benchmarking cites this paper.
Self-Verifying Measurement Records: Hash-Linked Evidence Graphs for Hardware Benchmarking Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T17:05:50.961017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.