Paper Citation Record · LEDGER

Injecting structural hints: Using language models to study inductive biases in language learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2304.13060.

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

pith.paper-citation-record.v1
2304.13060 v2

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 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:15:46.519309Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:54:37.899246Z

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 3e471e54-a973-4cee-b4ab-0ff437f9cd43 · inbound

Transfer of Structural Knowledge from Synthetic Languages cites this paper.

Transfer of Structural Knowledge from Synthetic Languages Injecting structural hints: Using language models to study inductive biases in language learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:46.519309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10a90ee-67a1-4874-896b-313c880b4aa6 · inbound

Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning cites this paper.

Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning Injecting structural hints: Using language models to study inductive biases in language learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:25.938487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d6192d-096c-4969-b7e7-f8594e0c1f01 · inbound

Rethinking Memorization Measures and their Implications in Large Language Models cites this paper.

Rethinking Memorization Measures and their Implications in Large Language Models Injecting structural hints: Using language models to study inductive biases in language learning

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:54:37.904123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0e082308-7adc-44b5-a944-a2c764ad5e08 · inbound

Procedural Pretraining: Warming Up Language Models with Abstract Data cites this paper.

Procedural Pretraining: Warming Up Language Models with Abstract Data Injecting structural hints: Using language models to study inductive biases in language learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T06:55:10.093365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.