Paper Citation Record · LEDGER

What Algorithms can Transformers Learn? A Study in Length Generalization

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2310.16028.

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

pith.paper-citation-record.v1
2310.16028 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 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 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:26:53.489472Z

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
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External citation measurements

7
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 eb08c33d-41a8-4903-af3d-095ee9979da4 · inbound

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 94

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T17:34:43.087593Z

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 00d1c289-b7d0-4645-944e-7ac3d9d84993 · inbound

Emergent Stack Representations in Modeling Counter Languages Using Transformers cites this paper.

Emergent Stack Representations in Modeling Counter Languages Using Transformers What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T15:26:53.489472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa4a1417-2469-47c8-962a-eabb70d3e43d · inbound

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges cites this paper.

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T14:54:29.309828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 276fd243-28ea-4c22-a5d5-b505bb565ecf · inbound

Code Simulation as a Proxy for High-order Tasks in Large Language Models cites this paper.

Code Simulation as a Proxy for High-order Tasks in Large Language Models What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-09T04:32:43.097070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8177049d-15cb-4f1e-9240-c587a0fedc80 · inbound

ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement cites this paper.

ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T22:29:37.732975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76c715e3-56a9-41b9-9470-18d9866511e1 · inbound

FoNE: Precise Single-Token Number Embeddings via Fourier Features cites this paper.

FoNE: Precise Single-Token Number Embeddings via Fourier Features What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:12:28.730236Z

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 1b4e5d1f-9277-4d54-a717-643538901f11 · inbound

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

Extrapolation by Association: Length Generalization Transfer in Transformers What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T04:59:24.336490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e642d740-59e9-406b-b362-41984e948cee · inbound

The Serial Scaling Hypothesis cites this paper.

The Serial Scaling Hypothesis What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:12:02.401992Z

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 67b93f2e-92fa-4641-81ee-4c39d0d58151 · inbound

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

On the Spatiotemporal Dynamics of Generalization in Neural Networks What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:00:46.836643Z

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 5701e019-3466-4348-95de-db0394411527 · inbound

LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning cites this paper.

LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:35:55.714967Z

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 b2425abd-d1c5-4029-b52c-0732cb79c3d1 · 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 What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:07:57.577421Z

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 af397df4-be69-411a-9e20-43cbdecd80e1 · 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 What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:39:38.085516Z

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 a75bf0e2-a315-4ede-8040-3197835301c0 · inbound

On the Emergence of Syntax by Means of Local Interaction cites this paper.

On the Emergence of Syntax by Means of Local Interaction What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-10T04:20:03.801289Z

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 714621d3-602d-4d81-8078-ef9fc8d331d0 · inbound

Training Transformers as a Universal Computer cites this paper.

Training Transformers as a Universal Computer What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:36:38.589963Z

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 43ee4f3f-1d48-407f-b1e8-2fa11c7325b9 · inbound

Agentic Transformers Provably Learn to Search via Reinforcement Learning cites this paper.

Agentic Transformers Provably Learn to Search via Reinforcement Learning What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T23:42:49.913561Z

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 775b5221-9025-4310-a56f-9c60ee1ab94a · inbound

A Verifiable Search Is Not a Learnable Chain-of-Thought cites this paper.

A Verifiable Search Is Not a Learnable Chain-of-Thought What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:49:39.727613Z

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 7e817d48-85e3-476a-9915-0887468e949d · inbound

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping cites this paper.

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.673836Z

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 26a39aa7-3826-4c2e-95b0-638661fee8ca · inbound

From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP cites this paper.

From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T03:23:31.727605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f90c2cbe-7c3d-468e-8328-59cc4cc74438 · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:50.953124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.