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Paper Citation Record · LEDGER

Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2202.02842.

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

pith.paper-citation-record.v1
2202.02842 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:21:13.781538Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T19:16:08.214495Z

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 a701cb68-5bcc-4eba-abef-9d2e1548eea9 · inbound

Evaluating Loss Landscapes from a Topology Perspective cites this paper.

Evaluating Loss Landscapes from a Topology Perspective Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T20:21:13.781538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:21:13.781538Z digest=sha256:9f7d5792b98a4c7a8f0d2a62946450a75393b4e93bde42572f5d47941592efed

Observation b4f11cbc-2315-42ae-bfd1-4c0b10c50148 · inbound

Visualizing Loss Functions as Topological Landscape Profiles cites this paper.

Visualizing Loss Functions as Topological Landscape Profiles Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T17:56:29.110415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:56:29.110415Z digest=sha256:e61aefa74bdec0d32682ed8b5e0e6c8126bc53f3096182578a3ad4ce184dad49

Observation c61174b1-5816-4df9-8c82-524a213a17d7 · inbound

SETOL: A Semi-Empirical Theory of (Deep) Learning cites this paper.

SETOL: A Semi-Empirical Theory of (Deep) Learning Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T14:45:19.972370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:45:19.972370Z digest=sha256:cc6a9a9356cfc1d295c2673adcdcb18d6262af686701e61d1a93d87eb140256b

Observation da764fcd-2a5f-4903-a329-061a61dbe15c · inbound

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy cites this paper.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:16:08.217087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-08T12:26:38.542442Z digest=sha256:96380a91a64d08ac7907c4f17d79b2ba8fa0e9d8f7b85d0f30a333573c02d125