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Paper Citation Record · LEDGER

SAIBench: A Structural Interpretation of AI for Science Through Benchmarks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.17869.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2311.17869 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T14:45:43.303013Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

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 ec84c1ec-1ade-41bd-9ab7-1406c4c9c6cf · inbound

VASP Agent: An Agentic Framework for Autonomous First-principles Calculations cites this paper.

VASP Agent: An Agentic Framework for Autonomous First-principles Calculations SAIBench: A Structural Interpretation of AI for Science Through Benchmarks

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-03T14:45:43.303013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:45:43.303013Z digest=sha256:da053d977fbd559c37d11ee4574b264a4c733b3c53b2c9db50208fe452a12782

Observation f318f451-5b9b-40e9-a98e-126407f04044 · inbound

AutoMatBench: An Automatic Optimization Toolkit for the Acceleration of Material Properties Prediction Benchmarking cites this paper.

AutoMatBench: An Automatic Optimization Toolkit for the Acceleration of Material Properties Prediction Benchmarking SAIBench: A Structural Interpretation of AI for Science Through Benchmarks

Reference 32

Resolution
unresolved
no resolver link, observed 2026-07-14T04:59:39.912425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T04:59:39.912425Z digest=sha256:8b23346995af154dfefc9e8ac24746d6342942a9f3fe187b9a2057b03199fc19