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Paper Citation Record · LEDGER

Reproducing Recurrent Transformers: The CoTFormer

As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.19405.

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

pith.paper-citation-record.v1
2607.19405 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:13:43.311785Z

measured 23 of 23 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87a1f96c-419e-432f-bc44-c9ce8a5457e3 · outbound

This paper cites The Thirteenth International Conference on Representation Learning (ICLR) , year=.

Reproducing Recurrent Transformers: The CoTFormer The Thirteenth International Conference on Representation Learning (ICLR) , year=

Reference 1

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no resolver link, observed 2026-08-02T08:13:40.306751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T08:13:40.306751Z digest=sha256:aa527e47e123f5aea8e81a1bef9f73af3de4cd64cb64a20c4b981c3144af17a6

Observation d81b704e-e531-438f-91a5-60ffad75c1b7 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

Reproducing Recurrent Transformers: The CoTFormer International Conference on Learning Representations (ICLR) , year=

Reference 2

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source=arxiv_source observed=2026-08-02T08:13:40.441326Z digest=sha256:261d24258cb034f9651a9b52cef052c223e1a75ccc9526892e0f947acbe9b01f

Observation 725e9475-cc04-47e0-a0c1-78bc0328d147 · outbound

This paper cites arXiv preprint arXiv:2507.02199 , year=.

Reproducing Recurrent Transformers: The CoTFormer arXiv preprint arXiv:2507.02199 , year=

Reference 3

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source=arxiv_source observed=2026-08-02T08:13:40.519176Z digest=sha256:2a2ae1f3d2ffe8705fe6087635184d35c71c586d0a3e93bcd14be68245002e02

Observation adf0cdbb-3b58-4a23-91eb-702c50150802 · outbound

This paper cites arXiv preprint arXiv:2601.10242 , year=.

Reproducing Recurrent Transformers: The CoTFormer arXiv preprint arXiv:2601.10242 , year=

Reference 4

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source=arxiv_source observed=2026-08-02T08:13:40.563499Z digest=sha256:524eca85e358e4db26af8afa3de75ab13f39fe88637900440309335355456d6f

Observation 64ae708c-8f4c-4367-af9e-4412a1b07d35 · outbound

This paper cites A Formal Comparison Between Chain of Thought and Latent Thought.

Reproducing Recurrent Transformers: The CoTFormer A Formal Comparison Between Chain of Thought and Latent Thought

Reference 5

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source=arxiv_source observed=2026-08-02T08:13:40.704011Z digest=sha256:68bcd2b70a91cb5a345b6fb0ce8da1bcb6803747ef42b14fe744dfd91a22832b

Observation 95cb53a2-3802-4a72-a70c-a6b925c92681 · outbound

This paper cites Does Transformer Interpretability Transfer to RNNs?.

Reproducing Recurrent Transformers: The CoTFormer Does Transformer Interpretability Transfer to RNNs?

Reference 6

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no resolver link, observed 2026-08-02T08:13:40.886207Z

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source=arxiv_source observed=2026-08-02T08:13:40.886207Z digest=sha256:04ac68c86a5fecf73c18e38c663801c468ea7ddce747d4a7d9f0ae405e88e64b

Observation 15129212-9d48-4541-8bcb-fc48d7ae1571 · outbound

This paper cites Reliability of CKA as a Similarity Measure in Deep Learning.

Reproducing Recurrent Transformers: The CoTFormer Reliability of CKA as a Similarity Measure in Deep Learning

Reference 7

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source=arxiv_source observed=2026-08-02T08:13:40.973271Z digest=sha256:260938bd606728947b39a90b2c3823fa1c7ac24a7127a992956eb6c6d73b3b61

Observation bc256ab2-bb20-4e86-b227-a46c329309e9 · outbound

This paper cites ICLR 2024 Re-Align Workshop , year=.

Reproducing Recurrent Transformers: The CoTFormer ICLR 2024 Re-Align Workshop , year=

Reference 8

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source=arxiv_source observed=2026-08-02T08:13:41.089509Z digest=sha256:59266fa1b4814266906a32ab9223a4931dbd82d9016e688c48f551d50d0556c3

Observation 3cb046c1-9101-491d-add6-11283d4a131a · outbound

This paper cites Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features.

Reproducing Recurrent Transformers: The CoTFormer Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features

Reference 9

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source=arxiv_source observed=2026-08-02T08:13:41.185655Z digest=sha256:f5a540bed560a5f55b45e86a219cfbd42141aa44b0ca84f7883e7d1b046f0287

Observation 06eaa4c2-2732-4332-acc0-0c8839654720 · outbound

This paper cites Frontiers in Earth Science , year=.

Reproducing Recurrent Transformers: The CoTFormer Frontiers in Earth Science , year=

Reference 10

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source=arxiv_source observed=2026-08-02T08:13:41.315475Z digest=sha256:47722fdeb5c4e2b75fa97f6b70228c72beda1e4e4433d92dcfa6d88f37ea7afb

Observation db665320-fad6-442a-948d-45c2f1f9cd7f · outbound

This paper cites Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year=.

Reproducing Recurrent Transformers: The CoTFormer Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year=

Reference 11

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source=arxiv_source observed=2026-08-02T08:13:41.490726Z digest=sha256:e32dfe0c7e80b287b1ff097443f4aac65b6ddc3db8695a6cb977c49c8257af59

Observation f0511a0c-0e91-438b-a6bc-59d9bfc043ad · outbound

This paper cites transformer-circuits.pub , year=.

Reproducing Recurrent Transformers: The CoTFormer transformer-circuits.pub , year=

Reference 12

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source=arxiv_source observed=2026-08-02T08:13:41.600960Z digest=sha256:198ab71f273ce9c2cedee9ab1903cfdd8020b169d7bd313ee5c8395f97dd3a4c

Observation cfaee31b-0405-4823-9453-65ff4626dafe · outbound

This paper cites arXiv preprint arXiv:2601.21582 , year=.

Reproducing Recurrent Transformers: The CoTFormer arXiv preprint arXiv:2601.21582 , year=

Reference 13

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no resolver link, observed 2026-08-02T08:13:41.781503Z

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source=arxiv_source observed=2026-08-02T08:13:41.781503Z digest=sha256:626701afa58c33f4f239e3528de34f7e6993e806c3bca731a19d8fbbd22606d7

Observation f1a32667-7fdb-46c8-a871-4934b61970a1 · outbound

This paper cites Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.

Reproducing Recurrent Transformers: The CoTFormer Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 14

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source=arxiv_source observed=2026-08-02T08:13:42.004926Z digest=sha256:b1e2029ee44799c979864e8a40481943688ea2465810bdc68edb0460210a6c99

Observation 55ffb5fc-6265-424d-8519-d3828d31ab3b · outbound

This paper cites Lessons on Parameter Sharing across Layers in Transformers.

Reproducing Recurrent Transformers: The CoTFormer Lessons on Parameter Sharing across Layers in Transformers

Reference 15

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source=arxiv_source observed=2026-08-02T08:13:42.156922Z digest=sha256:8f30b0c14df5a0112858a08cb0b3620f235dd176873c50c2b188ef54e58d90ed

Observation 1a974770-1c34-4754-b674-a8f626475410 · outbound

This paper cites Sparse Universal Transformer.

Reproducing Recurrent Transformers: The CoTFormer Sparse Universal Transformer

Reference 16

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source=arxiv_source observed=2026-08-02T08:13:42.340432Z digest=sha256:804626dab3556062c169dc3ed700f3479e8ab2645648de5fc1809f5c4976ca57

Observation daafd9df-dae4-454a-beff-c9cb2925a5e8 · outbound

This paper cites International Conference on Machine Learning (ICML) , year=.

Reproducing Recurrent Transformers: The CoTFormer International Conference on Machine Learning (ICML) , year=

Reference 17

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Observation 5739a06f-3a2c-4503-811c-dc45aded6c38 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year=.

Reproducing Recurrent Transformers: The CoTFormer Advances in Neural Information Processing Systems (NeurIPS) , year=

Reference 18

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Observation 6bfaea7c-bc4a-4839-b53f-4ab210c92db0 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

Reproducing Recurrent Transformers: The CoTFormer International Conference on Learning Representations (ICLR) , year=

Reference 19

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no resolver link, observed 2026-08-02T08:13:42.682550Z

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Observation 66f5aaa6-aa8b-4de2-a103-e221694ffa05 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year=.

Reproducing Recurrent Transformers: The CoTFormer Advances in Neural Information Processing Systems (NeurIPS) , year=

Reference 20

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Observation 9d840afd-1d21-4c88-ac5b-aa960e24f485 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

Reproducing Recurrent Transformers: The CoTFormer International Conference on Learning Representations (ICLR) , year=

Reference 21

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Observation efb0a363-133c-49d0-acc6-848db09ec46e · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

Reproducing Recurrent Transformers: The CoTFormer International Conference on Learning Representations (ICLR) , year=

Reference 22

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no resolver link, observed 2026-08-02T08:13:43.154602Z

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Observation d3c7f0ca-a707-4406-9324-585d3af3ed43 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

Reproducing Recurrent Transformers: The CoTFormer International Conference on Learning Representations (ICLR) , year=

Reference 23

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no resolver link, observed 2026-08-02T08:13:43.311785Z

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Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.