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Paper Citation Record · LEDGER

Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.04614.

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

pith.paper-citation-record.v1
2412.04614 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:38:34.581649Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:56:59.336897Z

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 984829d6-5a12-4e9e-9eda-6e07cec21d3c · inbound

VLMs Can Aggregate Scattered Training Patches cites this paper.

VLMs Can Aggregate Scattered Training Patches Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T11:04:05.791113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:04:05.791113Z digest=sha256:615d5c91e139cce1e3c80e00f476a9703111b394bc965ade309ad425a805aefe

Observation 1725881f-bc21-4c5c-9850-e93582da06ef · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-21T20:40:36.368436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-21T20:38:18.005002Z digest=sha256:d6700a7d0aceff3404847415c813a7e56b79a66d8c98fcf672f7898d7ba12990

Observation f4bb0656-1f84-4dff-ba1f-1b4ded31045a · inbound

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning cites this paper.

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-02T13:56:59.338652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-07-02T13:46:59.407102Z digest=sha256:4d84e4b942ec163931a8a49dedacce7affcb6c21ca463336e0a232853d38dfb6

Observation fe1c7522-fd0b-4138-9403-a428dfbf95f6 · inbound

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning cites this paper.

DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T09:18:43.665696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:18:43.665696Z digest=sha256:9a869181f280ef5b405720b292cb96a3fc275336604c9ae53a315b8da50b3cdd

Observation bed8637e-3eee-4548-a1e6-d7ada862ea70 · inbound

Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models cites this paper.

Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-10T11:38:34.581649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T11:38:34.581649Z digest=sha256:afc91bcf00e2f722f3434225234c14ad6688731fa7978f4ac4a36355c2896ac5