Paper Citation Record · LEDGER

The Impact of Depth on Compositional Generalization in Transformer Language Models

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.19956.

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

pith.paper-citation-record.v1
2310.19956 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:33:34.671155Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T07:59:50.296785Z

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 18b57abd-bed7-4bec-a6b2-0a3d5ddee524 · inbound

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training cites this paper.

Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:34.671155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda07176-c96d-4846-9525-0d478dccbbda · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:30:55.112323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation be930955-5536-4955-8876-4bdcce09ee12 · inbound

Generalization in LLM Problem Solving: The Case of the Shortest Path cites this paper.

Generalization in LLM Problem Solving: The Case of the Shortest Path The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:39:38.045477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 56738cc4-0a4d-4533-ad79-d04a2d94d4f5 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:36:19.976050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5418ddd6-ec5c-4c0f-9a06-6f8d1b2c0ef7 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:32:30.207898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 79164dc0-e272-4b9d-ac9b-714348f9be84 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.298890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.