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Paper Citation Record · LEDGER

Rethinking the long-range dependency in Mamba/SSM and transformer models

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 2 inbound Pith citation observations for arXiv:2509.04226.

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

pith.paper-citation-record.v1
2509.04226 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:22:53.238293Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T23:54:12.337711Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T07:05:59.428773Z

Reference resolution

41 of 41 outbound references displayed

  • verified exact2
  • verified fuzzy10
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation c53c4422-ab1e-402d-ae74-02b2e37b0428 · outbound

This paper cites Long short-term memory.

Rethinking the long-range dependency in Mamba/SSM and transformer models Long short-term memory

Reference 1

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source=pdf_text observed=2026-08-05T10:22:53.007069Z digest=sha256:5c35367cad5fc96175512f0e17f4a5ceb19120bf2b835a2e666faa5d1d468820

Observation ccae922a-f51f-461e-8bae-d1c5113bacb6 · outbound

This paper cites On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.

Rethinking the long-range dependency in Mamba/SSM and transformer models On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Reference 2

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source=pdf_text observed=2026-08-05T10:22:53.012810Z digest=sha256:0b469846efef96b8b1b8def402533a598b3d4a03e8359fa3ff4558a8546fc3e9

Observation 92c48215-b202-4608-9699-9f40e77f8af7 · outbound

This paper cites Attention is all you need.

Rethinking the long-range dependency in Mamba/SSM and transformer models Attention is all you need

Reference 3

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source=pdf_text observed=2026-08-05T10:22:53.019249Z digest=sha256:624011ae642897fb92ff16ab89eff113b9ed2b7122e8fd6d0ac6f2f010703e23

Observation 31de27d3-4853-4717-9291-9e5c26c2c8c8 · outbound

This paper cites A comprehensive survey on applications of transformers for deep learning tasks.Expert Systems with Applications, 241:122666, 2024.

Rethinking the long-range dependency in Mamba/SSM and transformer models A comprehensive survey on applications of transformers for deep learning tasks.Expert Systems with Applications, 241:122666, 2024

Reference 4

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.024284Z digest=sha256:35b1c2a3ba0b43d0f1ae85f5c966b16628a1baf110c63818da413c347437f6d0

Observation 1e6aa4a1-d163-495e-8f7a-2333758de6f6 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Rethinking the long-range dependency in Mamba/SSM and transformer models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 5

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source=pdf_text observed=2026-08-05T10:22:53.030220Z digest=sha256:16121f1de94f42389c22c1563afccfcce5c2351539080c0b095e9e68ea0e6ee2

Observation 954281e5-b7c4-47c9-97eb-d123bc9cabab · outbound

This paper cites Cross-lingual language model pretraining.Advances in neural information processing systems, 32, 2019.

Rethinking the long-range dependency in Mamba/SSM and transformer models Cross-lingual language model pretraining.Advances in neural information processing systems, 32, 2019

Reference 6

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source=pdf_text observed=2026-08-05T10:22:53.036362Z digest=sha256:5ab3e60647e8ee340115ce7e8b6984b475f10f8d3cc690af6df3cd474f5999d5

Observation d12a33ea-ae3f-40c9-840b-bbc17ea27089 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Rethinking the long-range dependency in Mamba/SSM and transformer models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 7

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source=pdf_text observed=2026-08-05T10:22:53.042611Z digest=sha256:f3910a70c6a451536970fab020733e1057a45fa907dcb954f3c5d48bb0e1eb6c

Observation 3bfd43a0-380d-431a-8bdb-53add4d18361 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Rethinking the long-range dependency in Mamba/SSM and transformer models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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source=pdf_text observed=2026-08-05T10:22:53.047431Z digest=sha256:7ec2707b55364042d7db330c11e367cda56ea71d6af440161ee17eba462c9c86

Observation f2376dd7-dae6-4fe1-84f6-a2c5e23b9909 · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

Rethinking the long-range dependency in Mamba/SSM and transformer models BEiT: BERT Pre-Training of Image Transformers

Reference 9

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source=pdf_text observed=2026-08-05T10:22:53.053668Z digest=sha256:1668b1c9ea03ae2c34fd9621af46c6e03329a6b6c8ce141c9cb4b118823f3bd0

Observation 4ebcf4eb-f070-4f7f-988e-7ffc07b3016c · outbound

This paper cites Medical transformer: Gated axial-attention for medical image segmentation.

Rethinking the long-range dependency in Mamba/SSM and transformer models Medical transformer: Gated axial-attention for medical image segmentation

Reference 10

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.062147Z digest=sha256:c50cab96f7ec0ee4e972430d44c5a485d4a9115b3d15288859762f913d2def2b

Observation 41c855f2-7fd0-4f7a-a7ea-63048c0255e8 · outbound

This paper cites Ds-transunet: Dual swin transformer u-net for medical image segmentation.

Rethinking the long-range dependency in Mamba/SSM and transformer models Ds-transunet: Dual swin transformer u-net for medical image segmentation

Reference 11

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source=pdf_text observed=2026-08-05T10:22:53.067671Z digest=sha256:d6b9182ed0c121f4f289104722ac7ccc1d6b67fa35255bc5ea977e0b331c11b7

Observation 828e9487-c997-47a2-a48c-76e5cdf4cb7a · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Rethinking the long-range dependency in Mamba/SSM and transformer models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 12

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source=pdf_text observed=2026-08-05T10:22:53.072372Z digest=sha256:2b16e5497494839c12a3f22845724ae9847abc1c630f9d2bfe649a4ee0b72987

Observation c0f36830-d956-438a-b138-f9c1e8512640 · outbound

This paper cites GIT: A Generative Image-to-text Transformer for Vision and Language.

Rethinking the long-range dependency in Mamba/SSM and transformer models GIT: A Generative Image-to-text Transformer for Vision and Language

Reference 13

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source=pdf_text observed=2026-08-05T10:22:53.077166Z digest=sha256:30a7a1fdff9d9dbee6d06a7433cbf449ddbb003a7848007d20ef61391346f5da

Observation e94452ce-1c27-4834-aeec-c84d7b174fcd · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Rethinking the long-range dependency in Mamba/SSM and transformer models Highly accurate protein structure prediction with alphafold

Reference 14

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.082217Z digest=sha256:90c774c767d1ef2aae42dfe9eab4f6bb058af70f52562c3cfcc02c4b2023be26

Observation 6e91d3cd-81b0-40e1-817d-68ae4c20a20f · outbound

This paper cites scgpt: toward building a foundation model for single-cell multi-omics using generative ai.

Rethinking the long-range dependency in Mamba/SSM and transformer models scgpt: toward building a foundation model for single-cell multi-omics using generative ai

Reference 15

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source=pdf_text observed=2026-08-05T10:22:53.086766Z digest=sha256:f7e743aeb0b9161bb136d00cd1b677626c73b368250b73fb4d95a4e218df7053

Observation 425b5aff-d2f4-438f-9e89-c6504f5d45f4 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Rethinking the long-range dependency in Mamba/SSM and transformer models Generating Long Sequences with Sparse Transformers

Reference 16

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source=pdf_text observed=2026-08-05T10:22:53.091290Z digest=sha256:5fe7b7fd171ab2e4401bd44671c4865e712bcc5092b1de69a94e7d9d4d87997d

Observation 8a004de2-4b02-48d4-880b-f8536b8d2d87 · outbound

This paper cites Reformer: The Efficient Transformer.

Rethinking the long-range dependency in Mamba/SSM and transformer models Reformer: The Efficient Transformer

Reference 17

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source=pdf_text observed=2026-08-05T10:22:53.098837Z digest=sha256:f255135fc0640ecd2bf77565d4d8eaf7fff4e7609d1f7a2d6440e73e7c3a373b

Observation 517ba40c-6903-499e-93ec-99ec0b5c9533 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Rethinking the long-range dependency in Mamba/SSM and transformer models Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 18

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source=pdf_text observed=2026-08-05T10:22:53.103200Z digest=sha256:668377b067c271afe976420d1cf9e2d582ec67735c99218613ab0821f23da128

Observation ddca5d65-a6e9-4b6a-a02d-4a8524b5bb44 · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Rethinking the long-range dependency in Mamba/SSM and transformer models GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 19

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source=pdf_text observed=2026-08-05T10:22:53.107714Z digest=sha256:5787a50ed4ded1ebbba6948ca171f2a2fe5cbeb850715e8d68b1a5904e7d2b5e

Observation 2c36757d-d827-4fc3-8474-1056f35b3897 · outbound

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

Rethinking the long-range dependency in Mamba/SSM and transformer models Efficiently Modeling Long Sequences with Structured State Spaces

Reference 20

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source=pdf_text observed=2026-08-05T10:22:53.112341Z digest=sha256:be52f7d05ad7c24e4a34a5c98e647c717e7653344fc52a541d57fe30b5ce5362

Observation 588f4e20-19de-4615-83be-c8599554d052 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Rethinking the long-range dependency in Mamba/SSM and transformer models Simplified State Space Layers for Sequence Modeling

Reference 21

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source=pdf_text observed=2026-08-05T10:22:53.116640Z digest=sha256:1c10c847757038581cdd14a699b1a8ad85d02d4f6160ddafa99bcc01236677ca

Observation 860168a6-dc70-405b-9925-33da5bdc6249 · outbound

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

Rethinking the long-range dependency in Mamba/SSM and transformer models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 22

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source=pdf_text observed=2026-08-05T10:22:53.121155Z digest=sha256:2d9eb834aed3b50e019b6a68aaea2815cf102b2478ce2d02744042b75a533af8

Observation 3f6a580d-1524-4793-ad58-f617aeeb4ab8 · outbound

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

Rethinking the long-range dependency in Mamba/SSM and transformer models Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 23

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source=pdf_text observed=2026-08-05T10:22:53.125368Z digest=sha256:4ef8050f73976f652075d02cb7df182d7f37c9ac260810aedc5d7c4f08e3ad40

Observation f141c0c2-8426-448e-9028-6f72f964f9cc · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

Rethinking the long-range dependency in Mamba/SSM and transformer models U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 24

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source=pdf_text observed=2026-08-05T10:22:53.129943Z digest=sha256:0275f40ee72dd827730f59373e56ee7243a9e6f4e91f8c8915e08a00a4ad4f2a

Observation 5c56ab32-8913-4388-afbb-4944724fcdbf · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

Rethinking the long-range dependency in Mamba/SSM and transformer models VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 25

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source=pdf_text observed=2026-08-05T10:22:53.136110Z digest=sha256:c4e68a57ee8f3df09ca085aafd864aee960b914c9a43bab6838acd4c19957ab6

Observation 473be9f9-dc3d-487b-bd2a-62ae57186db9 · outbound

This paper cites FD-Vision Mamba for Endoscopic Exposure Correction.

Rethinking the long-range dependency in Mamba/SSM and transformer models FD-Vision Mamba for Endoscopic Exposure Correction

Reference 26

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source=pdf_text observed=2026-08-05T10:22:53.148605Z digest=sha256:ba92f347eb6914696a931ce0faa914b6c09fa88e66c817e8ebe48224f8f9b1b0

Observation 854a8695-7b94-48cc-b115-c4902f3c2787 · outbound

This paper cites Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation.

Rethinking the long-range dependency in Mamba/SSM and transformer models Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation

Reference 27

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local_arxiv, observed 2026-08-05T10:22:53.522822Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.154068Z digest=sha256:23a11be97188503de2c9897d40f2ed5ee37c79b17f2d1cdd01c662c884338dff

Observation 3441a622-b5c5-4aab-b824-f7efc87a4d65 · outbound

This paper cites MedMamba: Vision Mamba for Medical Image Classification.

Rethinking the long-range dependency in Mamba/SSM and transformer models MedMamba: Vision Mamba for Medical Image Classification

Reference 28

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source=pdf_text observed=2026-08-05T10:22:53.158694Z digest=sha256:35a0ff315eb8b07c1a5a104d833df2fbbfd6c6f2df62dd1468842c421c62538b

Observation 36c3f459-74f3-47e3-901d-0abec78a595e · outbound

This paper cites ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes.

Rethinking the long-range dependency in Mamba/SSM and transformer models ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes

Reference 29

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local_arxiv, observed 2026-08-05T10:22:53.435188Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.167399Z digest=sha256:09dc3fe3e889844ff836dbf4cdf4a41d5453b03c2b15e042da7901380f8529c0

Observation 68246afc-0b28-4e0c-9235-ded80dcd6013 · outbound

This paper cites Hippo: Recurrent memory with optimal polynomial projections.

Rethinking the long-range dependency in Mamba/SSM and transformer models Hippo: Recurrent memory with optimal polynomial projections

Reference 30

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source=pdf_text observed=2026-08-05T10:22:53.178482Z digest=sha256:771d9125cb5259c26bce4f49187e8d99fd5de7fb3e4531c371c78b4091c0f2e9

Observation 89402e50-ca0e-44ae-ae9a-9e864dd8a10f · outbound

This paper cites Block-Biased Mamba for Long-Range Sequence Processing.

Rethinking the long-range dependency in Mamba/SSM and transformer models Block-Biased Mamba for Long-Range Sequence Processing

Reference 31

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source=pdf_text observed=2026-08-05T10:22:53.184571Z digest=sha256:07a5672a463e64d79377fc74c03f39edd959a30f731e3c883c7da27a1a220ba9

Observation b7806e44-e988-447d-acc3-a0eb3050dde2 · outbound

This paper cites ReMamba: Equip Mamba with Effective Long-Sequence Modeling.

Rethinking the long-range dependency in Mamba/SSM and transformer models ReMamba: Equip Mamba with Effective Long-Sequence Modeling

Reference 32

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source=pdf_text observed=2026-08-05T10:22:53.191008Z digest=sha256:e3067f78848662d114ed4b1b9b830938e36fa415f023e4e29d8697d692dbd24c

Observation a5222919-e5f0-425f-8cc8-48c1d35ea35b · outbound

This paper cites Spatial-mamba: Effective visual state space models via structure-aware state fusion.

Rethinking the long-range dependency in Mamba/SSM and transformer models Spatial-mamba: Effective visual state space models via structure-aware state fusion

Reference 33

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raw_fallback, observed 2026-08-05T10:22:54.188486Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.195991Z digest=sha256:244e08c303ac1a5d75b686afd893de902e1b58d5743b2a009d654cd20030b3a1

Observation 59aca043-8364-49e4-b617-4a0e01b64324 · outbound

This paper cites SST: Multi-scale hybrid mamba-transformer experts for long-short range time series forecasting.

Rethinking the long-range dependency in Mamba/SSM and transformer models SST: Multi-scale hybrid mamba-transformer experts for long-short range time series forecasting

Reference 34

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.202263Z digest=sha256:b0bb6f782936c8bd36664f6f03dc1d6a06d498d568d8c8260c3a9686523645f4

Observation 2dc073ca-68f0-446e-a616-006685b693c5 · outbound

This paper cites Efficient hybrid long sequence modeling with state space augmented transformers.

Rethinking the long-range dependency in Mamba/SSM and transformer models Efficient hybrid long sequence modeling with state space augmented transformers

Reference 35

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raw_fallback, observed 2026-08-05T10:22:54.134255Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.207561Z digest=sha256:68d84fe6602aac2c67908ca4a6dee1eb85fdbc7bd51610562edc58804a757365

Observation 3224fc27-b6c5-462e-b7fb-c8cf3de641ec · outbound

This paper cites State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory.

Rethinking the long-range dependency in Mamba/SSM and transformer models State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:22:54.108411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.212040Z digest=sha256:23ff44d3ede4ce395bbbb37d3ff0b200a7db7cb5f6ac117c13281adaeb678d18

Observation a9c9b961-ce64-4372-86fd-251d23dbfc9c · outbound

This paper cites Optimizing Deeper Transformers on Small Datasets.

Rethinking the long-range dependency in Mamba/SSM and transformer models Optimizing Deeper Transformers on Small Datasets

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T10:22:53.216437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:22:53.216437Z digest=sha256:f7ff75b128cc1d21d5351945a0fa6a4cf7a3640b8d125fe7aeff732d7b971e7f

Observation 4e90bf99-6f83-484e-8bcd-9a6f7d40f551 · outbound

This paper cites Escaping the Big Data Paradigm with Compact Transformers.

Rethinking the long-range dependency in Mamba/SSM and transformer models Escaping the Big Data Paradigm with Compact Transformers

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T10:22:53.221304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:22:53.221304Z digest=sha256:d70e05e246eb79cf4a4d0d7bd1458a10fa612d277340f02dc3618198c123ff5d

Observation 5055d1f2-ae73-4e64-a994-08493ee3d8c7 · outbound

This paper cites Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors.

Rethinking the long-range dependency in Mamba/SSM and transformer models Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T10:22:53.226128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:22:53.226128Z digest=sha256:ea5c3cb90af006ad98ff5c5a00284680b36f55c845865a55897cb3a56bc5f4ec

Observation 5e14ea7b-f1f4-4e54-8fc0-443d36e17c21 · outbound

This paper cites High-dimensional probability: An introduction with applications in data science , volume 47.

Rethinking the long-range dependency in Mamba/SSM and transformer models High-dimensional probability: An introduction with applications in data science , volume 47

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T10:22:53.231770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:22:53.231770Z digest=sha256:9165f3ed0709ed006b24f197406da382f9d9289bef6f88caf558898d2662bd48

Observation bb00744f-0025-4465-8b22-7442eeef32d5 · outbound

This paper cites Wainwright.

Rethinking the long-range dependency in Mamba/SSM and transformer models Wainwright

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:22:54.065277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T10:22:53.238293Z digest=sha256:37c36526e039e9112f5e193db4c7f54597ee8f416fcc3c238ca64c5ae17812c8

Pith citing papers

Observation c3926c96-d0b5-431f-aba3-c9f0236aa066 · inbound

HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment cites this paper.

HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment Rethinking the long-range dependency in Mamba/SSM and transformer models

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:05:59.436362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-10T17:19:23.588889Z digest=sha256:6d33f3cdd38c16c7429956ee339830f2c89a701a82153df7eea0f7e7f83995e5

Observation 9329dda2-7102-41d9-9565-a45b12a75245 · inbound

HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment cites this paper.

HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment Rethinking the long-range dependency in Mamba/SSM and transformer models

Reference 28

Resolution
unresolved
no resolver link, observed 2026-07-12T23:54:12.337711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:54:12.337711Z digest=sha256:c1f49a8469b3680f94c674796b3ad1f1ac52167ce81415898fbc932cbbe3de91