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Paper Citation Record · LEDGER

Efficient Long Sequence Modeling via State Space Augmented Transformer

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2212.08136.

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

pith.paper-citation-record.v1
2212.08136 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:34.834731Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T22:12:11.701219Z

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 ac8671b1-8181-42e3-932b-17d7c116681e · inbound

Mamba: Linear-Time Sequence Modeling with Selective State Spaces cites this paper.

Mamba: Linear-Time Sequence Modeling with Selective State Spaces Efficient Long Sequence Modeling via State Space Augmented Transformer

Reference 116

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:53:06.708476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T11:53:06.570968Z digest=sha256:55b5ee3c4410163c2f14c60dbbe2402f9d89d63d7653b09180a73c924a49b4f9

Observation 9373747f-f9f7-4541-a473-c7d32fed6ec3 · inbound

Jamba: A Hybrid Transformer-Mamba Language Model cites this paper.

Jamba: A Hybrid Transformer-Mamba Language Model Efficient Long Sequence Modeling via State Space Augmented Transformer

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-13T14:11:27.229169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T14:11:27.156350Z digest=sha256:994a8cf422ac3a3aadff6852914da64cab56aa4e8ea261583bbec98403e4c7fe

Observation 0176a3b0-e074-4fe7-9126-d7f8a91bb547 · inbound

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba cites this paper.

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba Efficient Long Sequence Modeling via State Space Augmented Transformer

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-22T22:12:11.704403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T22:07:12.198095Z digest=sha256:4257d8d6312e9855678fb64b684bc6656f2007707dcbfdff56b0a121303f518d

Observation 1609cb38-dc68-4e7d-be0c-b4819fa450ed · inbound

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding cites this paper.

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding Efficient Long Sequence Modeling via State Space Augmented Transformer

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T04:55:34.834731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:55:34.834731Z digest=sha256:6003c79cfed964cf0f250fc086e6035fe960a6b09e2f3cbefbb4c2b6885526d9

Observation 5efda260-6214-46c5-b6f0-09958d84ab61 · inbound

Towards Understanding Self-Pretraining for Sequence Classification cites this paper.

Towards Understanding Self-Pretraining for Sequence Classification Efficient Long Sequence Modeling via State Space Augmented Transformer

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:33:58.941590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-21T05:29:58.809024Z digest=sha256:451c3f25e8dd10169ff17f639e9946f5b0e11d660e735263e1476954b2cf9910