Paper Citation Record · LEDGER

Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.11268.

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

pith.paper-citation-record.v1
2410.11268 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:39.319942Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T23:23:51.654192Z

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 6064b54f-7ea6-4ec2-8904-08c8d1b2efad · inbound

Numerical Pruning for Efficient Autoregressive Models cites this paper.

Numerical Pruning for Efficient Autoregressive Models Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:26.888859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46b82d6b-aa8f-4693-9a91-6b646aeead10 · inbound

LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers cites this paper.

LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:39.319942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dfc83c9-49b8-42a4-bd67-5003348c49e6 · inbound

High-Order Matching for One-Step Shortcut Diffusion Models cites this paper.

High-Order Matching for One-Step Shortcut Diffusion Models Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T18:10:52.981593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 476ef222-2196-4878-8eec-db59a372e755 · inbound

Universal Approximation of Visual Autoregressive Transformers cites this paper.

Universal Approximation of Visual Autoregressive Transformers Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T16:39:14.118607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 362eea60-591f-4fab-8478-510d067a27f9 · inbound

A Survey on Latent Reasoning cites this paper.

A Survey on Latent Reasoning Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:22.857140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c58ff7cf-4744-4c63-9628-d0183ad75873 · inbound

Simply Stabilizing the Loop via Fully Looped Transformer cites this paper.

Simply Stabilizing the Loop via Fully Looped Transformer Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:23:51.678015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.