Paper Citation Record · LEDGER

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory

As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.21000.

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

pith.paper-citation-record.v1
2607.21000 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:50:28.931954Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

verified exact0
verified fuzzy0
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aacbfb5d-bb69-45f8-b287-84378d4616d2 · outbound

This paper cites Zoology: Measuring and Improving Recall in Efficient Language Models.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Zoology: Measuring and Improving Recall in Efficient Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:26.674694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee54b19b-59c5-4c84-a61d-3551dfa22dcf · outbound

This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Simple linear attention language models balance the recall-throughput tradeoff

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:26.748421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65140599-78aa-4fa9-8a41-6a79ec100849 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory xLSTM: Extended Long Short-Term Memory

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:26.873115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17b26bb8-e125-4569-a906-7f8756d4890b · outbound

This paper cites RecurrentGemma: Moving Past Transformers for Efficient Open Language Models.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.042281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36f0a150-2563-4d7b-befc-4cfc539083eb · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.204477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90c92607-4d00-440b-ac83-01026efb4e16 · outbound

This paper cites Language Modeling with Gated Convolutional Networks.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Language Modeling with Gated Convolutional Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.302479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0cfcff2-a546-453e-b2f3-78a39821840b · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.486817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aba9ba16-eb99-4bd7-9ceb-984b9c1613ae · outbound

This paper cites Fu, Tri Dao, Khaled K.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Fu, Tri Dao, Khaled K

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.645865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549e25b3-c02f-4092-a8e7-6d0b30219f8f · outbound

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

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.806978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c4c1b67-3a27-41a9-a349-950e80e8dfd5 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Efficiently Modeling Long Sequences with Structured State Spaces

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:27.884383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a268a6e7-7e09-4714-9cc9-1e922f607ae5 · outbound

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

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Jamba: A Hybrid Transformer-Mamba Language Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.048203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 643edcf8-3e44-44a7-946f-a011eac12d90 · outbound

This paper cites Transformers Learn Shortcuts to Automata.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Transformers Learn Shortcuts to Automata

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.180150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6927f64e-5c23-4bf6-bd33-efaedb289a3b · outbound

This paper cites Pointer sentinel mixture models.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Pointer sentinel mixture models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.255172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3ac7f8-d52f-4a12-b404-3b47bad14d87 · outbound

This paper cites Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.292044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81cfd8b6-6860-47cf-b7f3-d7674237d49c · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory RWKV: Reinventing RNNs for the Transformer Era

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.358991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8213667b-2f39-4066-861d-1a03936681bf · outbound

This paper cites Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.406256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00f96e30-70fc-4b25-a8eb-df042fe2bb3a · outbound

This paper cites Hierarchically Gated Recurrent Neural Network for Sequence Modeling.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Hierarchically Gated Recurrent Neural Network for Sequence Modeling

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.468423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a354378-6297-447e-a6e8-99bf9f1706a1 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8), 2019.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Language models are unsupervised multitask learners.OpenAI blog, 1(8), 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.552704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 117b2f98-a715-4479-bb50-17ef6b00f495 · outbound

This paper cites GLU Variants Improve Transformer.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory GLU Variants Improve Transformer

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.635822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f50134c8-aa43-4da7-939c-b3be454c28c9 · outbound

This paper cites RoFormer: Enhanced transformer with rotary position embedding.Neurocomputing, 568, 2024.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory RoFormer: Enhanced transformer with rotary position embedding.Neurocomputing, 568, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.726907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d99ec86-47ee-4802-8eb1-c3ed17ae6e89 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Retentive Network: A Successor to Transformer for Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.791457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cc85aaa-56af-4fe0-a587-88bc5020ac01 · outbound

This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Long Range Arena: A Benchmark for Efficient Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.861491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da024c7d-19c5-4621-9fe9-edaeeb674325 · outbound

This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T08:50:28.931954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.