Paper Citation Record · LEDGER

DeepLoop: Depth Scaling for Looped Transformers

As of 7 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2607.13491.

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

pith.paper-citation-record.v1
2607.13491 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:05:25.943958Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:00:20.206489Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy0
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d8143d7-22d8-4bfa-ac71-09948ed90cd5 · outbound

This paper cites We test this prediction directly with a single-axis sweep over the exponentp at fixedR=3 on the GPT-2 small backbone.

DeepLoop: Depth Scaling for Looped Transformers We test this prediction directly with a single-axis sweep over the exponentp at fixedR=3 on the GPT-2 small backbone

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.943958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4688a474-d262-4da3-bbdc-759ffb373a48 · outbound

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

DeepLoop: Depth Scaling for Looped Transformers Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.255642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51fd1338-e877-4b76-9ef7-4fb9d4d55b48 · outbound

This paper cites Think before you speak: Training Language Models With Pause Tokens.

DeepLoop: Depth Scaling for Looped Transformers Think before you speak: Training Language Models With Pause Tokens

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.293025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a57bc42c-8a0a-4825-b8b2-1ffd28471b8c · outbound

This paper cites Adaptive Computation Time for Recurrent Neural Networks.

DeepLoop: Depth Scaling for Looped Transformers Adaptive Computation Time for Recurrent Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.336423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ea5ab9a-7864-44ca-9a21-4c0f9db82f1a · outbound

This paper cites Training Compute-Optimal Large Language Models.

DeepLoop: Depth Scaling for Looped Transformers Training Compute-Optimal Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.423184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a033c654-852b-4cea-956f-7be28130c703 · outbound

This paper cites Scaling Laws for Neural Language Models.

DeepLoop: Depth Scaling for Looped Transformers Scaling Laws for Neural Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.461381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1adb5a0-6984-4e3f-9708-c132d8c7a7de · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

DeepLoop: Depth Scaling for Looped Transformers ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.496749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24b1bc02-b38c-4dfd-b649-30ea0c125ae6 · outbound

This paper cites Subformer: Exploring weight sharing for parameter efficiency in generative transformers.

DeepLoop: Depth Scaling for Looped Transformers Subformer: Exploring weight sharing for parameter efficiency in generative transformers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.581738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e76d8fd9-76ac-4873-8ebc-75ad7cc8651a · outbound

This paper cites Reasoning with Latent Thoughts: On the Power of Looped Transformers.

DeepLoop: Depth Scaling for Looped Transformers Reasoning with Latent Thoughts: On the Power of Looped Transformers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.615490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43a20b59-8207-448e-8146-886ab29ac9f2 · outbound

This paper cites Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks.

DeepLoop: Depth Scaling for Looped Transformers Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.727834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffb217f5-4f37-4f21-9fc6-08be4b760cf4 · outbound

This paper cites Deep Delta Learning.

DeepLoop: Depth Scaling for Looped Transformers Deep Delta Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.797553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6529b4a7-67c0-43a4-ba10-6f763ce01537 · outbound

This paper cites 24); training uses the same FineWeb-Edu 50BT data and schedule but on4×H200 141GB GPUs.

DeepLoop: Depth Scaling for Looped Transformers 24); training uses the same FineWeb-Edu 50BT data and schedule but on4×H200 141GB GPUs

Reference 1024

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.885020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f5460ee-ff24-4378-8f35-f2cc1cef677a · outbound

This paper cites Training Large Language Models to Reason in a Continuous Latent Space.

DeepLoop: Depth Scaling for Looped Transformers Training Large Language Models to Reason in a Continuous Latent Space

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.388448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1a3d0d8-4bad-41f9-8938-720584511857 · outbound

This paper cites Hierarchical Reasoning Model.

DeepLoop: Depth Scaling for Looped Transformers Hierarchical Reasoning Model

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.655492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f915f51-9122-4c70-a914-d11e4763450c · outbound

This paper cites Understanding the difficulty of training transformers.

DeepLoop: Depth Scaling for Looped Transformers Understanding the difficulty of training transformers

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.536046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e78b15b-420d-4b80-b576-85133cf31047 · outbound

This paper cites Universal Transformers.

DeepLoop: Depth Scaling for Looped Transformers Universal Transformers

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.173822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a32a304-b21d-4a35-afbe-ab95480ae931 · outbound

This paper cites PonderNet: Learning to Ponder.

DeepLoop: Depth Scaling for Looped Transformers PonderNet: Learning to Ponder

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:24.981055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68bcd095-042b-47ed-a5ca-ab7bc458c5f6 · outbound

This paper cites Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit.

DeepLoop: Depth Scaling for Looped Transformers Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.062788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9899462-613a-42c4-9cdc-e16ce89aa748 · outbound

This paper cites On the Measure of Intelligence.

DeepLoop: Depth Scaling for Looped Transformers On the Measure of Intelligence

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.126875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f0cac54-33b1-4a22-ada1-bdf5a03f30e7 · outbound

This paper cites 15 Khashayar Gatmiry, Nikunj Saunshi, Sashank J Reddi, Stefanie Jegelka, and Sanjiv Kumar.

DeepLoop: Depth Scaling for Looped Transformers 15 Khashayar Gatmiry, Nikunj Saunshi, Sashank J Reddi, Stefanie Jegelka, and Sanjiv Kumar

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T05:05:25.210943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation baf61db0-02d6-4a67-88c3-7e01ccf72a50 · inbound

Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration cites this paper.

Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration DeepLoop: Depth Scaling for Looped Transformers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T09:00:20.206489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.