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Paper Citation Record · LEDGER

Do Neural Language Representations Learn Physical Commonsense?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1908.02899.

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

pith.paper-citation-record.v1
1908.02899 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:15:38.871832Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T06:52:28.464580Z

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 1eff1a81-c6b9-4ac1-b8ee-b3f35d9d453c · inbound

IKIWISI: An Interactive Visual Pattern Generator for Evaluating the Reliability of Vision-Language Models Without Ground Truth cites this paper.

IKIWISI: An Interactive Visual Pattern Generator for Evaluating the Reliability of Vision-Language Models Without Ground Truth Do Neural Language Representations Learn Physical Commonsense?

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T13:15:38.871832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:15:38.871832Z digest=sha256:5c5914df690ca6565421b5a733084896fdb2b12105a09cc14c75c6d550c74e6d

Observation 063b9e39-0ea3-4628-baf5-5b29a3d22ea1 · inbound

Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models cites this paper.

Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models Do Neural Language Representations Learn Physical Commonsense?

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-16T06:52:28.467075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T06:50:49.096618Z digest=sha256:e6f3f3b338f06a043ca59a37343aaea9994d9deee8adf0ad22f5c7ff50b9899c