Paper Citation Record · LEDGER

The Impact of Depth on Compositional Generalization in Transformer Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:34:08.105371Z

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 de384c6d-2abf-4988-8da7-ea42cdb1a163 · inbound

MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections cites this paper.

MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T22:34:08.105371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13fbeee8-8df1-440b-a295-bc73d546e2c7 · inbound

Can Interpretation Predict Behavior on Unseen Data? cites this paper.

Can Interpretation Predict Behavior on Unseen Data? The Impact of Depth on Compositional Generalization in Transformer Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T19:10:29.782493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.