Paper Citation Record · LEDGER

Transformers Can Achieve Length Generalization But Not Robustly

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2402.09371.

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

pith.paper-citation-record.v1
2402.09371 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:54:29.313148Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T07:34:21.647564Z

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 ed0a5139-5a66-4877-b5c2-670c1858af3d · 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 Transformers Can Achieve Length Generalization But Not Robustly

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T14:54:29.313148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8032c346-8008-47bd-adf7-2eb85dbd9d64 · inbound

Solving Empirical Bayes via Transformers cites this paper.

Solving Empirical Bayes via Transformers Transformers Can Achieve Length Generalization But Not Robustly

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T20:21:58.420988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe812d76-337a-468b-bb5d-f301e3e82b77 · inbound

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

Extrapolation by Association: Length Generalization Transfer in Transformers Transformers Can Achieve Length Generalization But Not Robustly

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:59:24.440272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 579aac01-1df6-4128-9d10-723a59c6d0b5 · inbound

Modular Arithmetic: Language Models Solve Math Digit by Digit cites this paper.

Modular Arithmetic: Language Models Solve Math Digit by Digit Transformers Can Achieve Length Generalization But Not Robustly

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T05:02:54.113287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 108e7b8b-3fad-44cb-9056-c445807f3429 · inbound

RoboSSM: Scalable In-context Imitation Learning via State-Space Models cites this paper.

RoboSSM: Scalable In-context Imitation Learning via State-Space Models Transformers Can Achieve Length Generalization But Not Robustly

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T15:34:52.272478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3786f78-e500-493b-bace-9e37937f4f5f · inbound

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights cites this paper.

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights Transformers Can Achieve Length Generalization But Not Robustly

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:21:15.156327Z

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 bfb8451c-1e11-4550-b7c8-7a03984e3069 · inbound

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers cites this paper.

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers Transformers Can Achieve Length Generalization But Not Robustly

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-03T15:22:56.533047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4ef6ee2-10e3-43c1-949f-70ce9c48904c · inbound

Universal priors: solving empirical Bayes via Bayesian inference and pretraining cites this paper.

Universal priors: solving empirical Bayes via Bayesian inference and pretraining Transformers Can Achieve Length Generalization But Not Robustly

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T23:03:08.444115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6b0c5e7-c3f6-4f1b-bf6c-8863b058e245 · inbound

Training Transformers as a Universal Computer cites this paper.

Training Transformers as a Universal Computer Transformers Can Achieve Length Generalization But Not Robustly

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:36:38.611736Z

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 6e5c965c-3f12-45e8-b075-0657dcfcd3b4 · inbound

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

Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping Transformers Can Achieve Length Generalization But Not Robustly

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.649547Z

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 b6c385d0-34b3-45b9-b7c2-f386bbc47f0b · inbound

Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D cites this paper.

Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D Transformers Can Achieve Length Generalization But Not Robustly

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T21:30:59.895468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4b9378a-c2d9-474f-b263-f0e7834c0e1f · 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 Transformers Can Achieve Length Generalization But Not Robustly

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:51.052680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.