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Paper Citation Record · LEDGER

Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.15720.

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

pith.paper-citation-record.v1
2407.15720 v2

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 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:28:15.771544Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e1567efb-18eb-43b9-914e-45dd97395275 · inbound

On the Limitations of Steering in Language Model Alignment cites this paper.

On the Limitations of Steering in Language Model Alignment Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-16T04:28:15.771544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:28:15.771544Z digest=sha256:83e279f398637f1da7b852fa48643b3fe4967c8f2d7dfadb1dd12966a568a088

Observation 12ae6f7c-46ad-47ad-ba03-f62dfd1dbf5a · inbound

Extrapolation by Association: Length Generalization Transfer in Transformers cites this paper.

Extrapolation by Association: Length Generalization Transfer in Transformers Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T04:59:23.877330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:59:23.877330Z digest=sha256:ff459b16c7148a0b672acb0491413ab74dd9467258bdfacd1b703267bea0e32c

Observation 61a4e2ed-7217-468d-bdcd-ec3c22309f3d · inbound

DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning cites this paper.

DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:20:34.331734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-18T01:18:44.523602Z digest=sha256:da328e37f520c6227daf9af66339f8355bc37b5e24ed4bbdc8c9505443025d0c

Observation 8f8712bc-90de-4aa8-b67d-33d331317be2 · inbound

DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning cites this paper.

DecompSR: A dataset for decomposed analyses of compositional multihop spatial reasoning Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-04T00:11:52.422398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:11:52.422398Z digest=sha256:505ab6172d3464e9f6a96729c2d584b7e47049bd6118582eb017c501f2c967f9

Observation e419be3c-e4c9-4b40-9704-d911b5f7a2cb · inbound

Multi-Hop Knowledge Composition is Bound by Pretraining Exposure cites this paper.

Multi-Hop Knowledge Composition is Bound by Pretraining Exposure Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-06-27T16:41:03.379605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-27T16:31:58.422166Z digest=sha256:7ec023086f8d820632f156b73b84b46cd985eda891e6ca023edc477bfd0ef4f2