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Paper Citation Record · LEDGER

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.15787.

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

pith.paper-citation-record.v1
2410.15787 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 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:23:57.809581Z

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

0
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 62b6c806-972d-42b7-bc7e-9af5a335e79c · inbound

Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers cites this paper.

Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

Reference 8856

Resolution
unresolved
no resolver link, observed 2026-08-09T12:23:57.809581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T12:23:57.809581Z digest=sha256:d803bbf900c7204611bdb0496ec06bd499ce6e9859efe29d9c2c46aaf7326a36

Observation ef27ffc5-517a-4a1c-b5c0-6c5bfab5f9e9 · inbound

On the Spatiotemporal Dynamics of Generalization in Neural Networks cites this paper.

On the Spatiotemporal Dynamics of Generalization in Neural Networks Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:00:46.830978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-16T08:59:44.016444Z digest=sha256:1f6bc6610427281a7820456da9ece0303d621ce503bd4db635aba53521c30c47

Observation 1ab43d13-8c1d-4d3c-859a-9dc800fc5dbb · inbound

On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication cites this paper.

On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:07:57.688758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-14T21:07:37.032208Z digest=sha256:98998af9c7e61325cff9e46b5df817f194202f3abeee30bd336d0937b34a19e4

Observation 610f50f2-6658-46d5-9875-b399a5c9854d · inbound

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction cites this paper.

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:45:57.366901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-11T02:18:59.088350Z digest=sha256:a9dc3e547dea30b1641aa0916ec38ccf23b3c2966fe12a2201f93228c419cb67

Observation 882870cc-46a9-4df2-aa28-99252171c7ad · inbound

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction cites this paper.

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-01T13:25:44.993805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-30T23:21:19.495375Z digest=sha256:33023d3a679392340ee807865af7efa1b544bd320c2590d9937691f82d0f11ff