Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2311.12997.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:57:22.808829Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T16:23:39.009793Z
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 5569d08c-f9ac-48ce-88af-7ae4d248fd97 · inbound
Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer cites this paper.
Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:57:22.808829Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8c02d9d0-474e-44a2-8b3a-b3d1181f7ec6 · inbound
There Will Be a Scientific Theory of Deep Learning cites this paper.
There Will Be a Scientific Theory of Deep Learning Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Reference 289
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:21:09.215117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eee381e8-6e48-4de2-8990-5a2b02de889e · inbound
Learning to Theorize the World from Observation cites this paper.
Learning to Theorize the World from Observation Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Reference 286
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:21:28.300543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6ebf0876-a8a5-4663-813a-c9d0a2168dd7 · inbound
Shortcut Solutions Learned by Transformers Impair Continual Compositional Reasoning cites this paper.
Shortcut Solutions Learned by Transformers Impair Continual Compositional Reasoning Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:06:05.445599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 00330db2-d057-4c2d-9bf8-cadc11874971 · inbound
A Systematic Study of Behavioral Cloning for Scientific Data Annotation cites this paper.
A Systematic Study of Behavioral Cloning for Scientific Data Annotation Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks
Reference 216
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T16:23:39.012244Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.