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Paper Citation Record · LEDGER

Do Two AI Scientists Agree?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2504.02822.

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

pith.paper-citation-record.v1
2504.02822 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:09.294897Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:36:09.692876Z

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 62a6d0d0-6623-4329-bae8-d0236b43d78d · inbound

BACON: A fully explainable AI model with graded logic for decision making problems cites this paper.

BACON: A fully explainable AI model with graded logic for decision making problems Do Two AI Scientists Agree?

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:36:09.705307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:36:09.294897Z digest=sha256:ea9da8a5ec578b9a2f40c898025543acd89613b78835c7c00b71067473e694e4

Observation e663dfb9-3e6a-4309-b41c-a36d839ca702 · inbound

Acquiring Human-Like Data-Efficient Mechanics Prediction from Deep Reinforcement Learning cites this paper.

Acquiring Human-Like Data-Efficient Mechanics Prediction from Deep Reinforcement Learning Do Two AI Scientists Agree?

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-03T06:49:06.071847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:49:06.071847Z digest=sha256:f634608e1877c9dff1b2946f1731e2d42205789ace204945357dd2b30ca827a5