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Paper Citation Record · LEDGER

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 4 inbound Pith citation observations for arXiv:2412.13328.

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

pith.paper-citation-record.v1
2412.13328 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:17:50.112407Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:03:25.368313Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:36:45.097919Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 882493b5-5b1d-4c92-9ea1-206280a6203f · outbound

This paper cites Just read twice: closing the recall gap for recurrent language models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Just read twice: closing the recall gap for recurrent language models

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.870263Z digest=sha256:9ebf24203d1e4871603731077d202e62baf67e5d68b26ce16246940d5a89a078

Observation 91ff23ab-10e0-47cc-a8dc-e84493a33e31 · outbound

This paper cites L ong B ench: A bilingual, multitask benchmark for long context understanding.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models L ong B ench: A bilingual, multitask benchmark for long context understanding

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.878530Z digest=sha256:fe2a1921f61a37f00fef9275e43783ec349d95bfc2db6bccbd366d70f6c2113b

Observation 9b721e0c-e366-49b5-9df2-1cb43850d099 · outbound

This paper cites Longformer: The Long-Document Transformer.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Longformer: The Long-Document Transformer

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.884588Z digest=sha256:d914c53dc4d9bf97ace12816181b0773f95d2f2c2b981cf1108a373ad84510d8

Observation bd2199a8-60f4-4cc2-8e3e-7d71f2e8b92c · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Extending Context Window of Large Language Models via Positional Interpolation

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.891014Z digest=sha256:5138373f4dee55a192aded7131cd09ab4e9367198886a18b05f58ed60fc78d3c

Observation f268febc-5d6b-40b1-a5e4-623f9835df24 · outbound

This paper cites Longlo RA : Efficient fine-tuning of long-context large language models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Longlo RA : Efficient fine-tuning of long-context large language models

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-11T13:17:51.003291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:49.896968Z digest=sha256:e31f32d869b4182161259e90e7844c371675b7ed87058e7d169493d07e988da5

Observation 56a80308-7b13-4baf-b79a-97d35d1858ad · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.902641Z digest=sha256:8e705edc1b8047240f60a043f68d2670389e7f8c24dea1e64d7e934050ed8cdf

Observation b89ef6bb-fb15-4fc3-b9eb-b0e840339fba · outbound

This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Flashattention-2: Faster attention with better parallelism and work partitioning

Reference 7

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source=arxiv_source observed=2026-08-11T13:17:49.909844Z digest=sha256:c1a3a9ea675cacfeb5f38aaa0cc4266563d3495863f05fde717f63f6a908cf23

Observation bfced16f-391c-459f-95cd-44c76a532de3 · outbound

This paper cites Transformers are ssms: generalized models and efficient algorithms through structured state space duality.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Transformers are ssms: generalized models and efficient algorithms through structured state space duality

Reference 8

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raw_fallback, observed 2026-08-11T13:17:50.969194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:49.915647Z digest=sha256:1506b9e3ee22ae2dcdf5fc374f94a92bf4f16c2ec3bc0099c18cc5e83e44aaf1

Observation 35461595-eefe-4b9b-b199-58a8ab6265e1 · outbound

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

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T13:17:49.920570Z digest=sha256:289da5c0d5ee3f53128832c925abcc20f977f77722d5c2ff44ddc95f1a43848a

Observation ac468e51-efde-4a58-8ca7-dfe8df7d4b55 · outbound

This paper cites The Llama 3 Herd of Models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models The Llama 3 Herd of Models

Reference 10

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source=arxiv_source observed=2026-08-11T13:17:49.926639Z digest=sha256:57b68cf398d8ddf701aec8271c3766520b672c8a6705d3651524ad799512449f

Observation 9afe069f-59c0-4cd7-8aad-76917416d944 · outbound

This paper cites Parameter-Efficient Fine-Tuning of State Space Models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Parameter-Efficient Fine-Tuning of State Space Models

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.932640Z digest=sha256:4065ea941c327619cbe9e738f2e2b6564e2096a1d5e82e1e9feb2e709c8a044a

Observation 80264623-b11d-4e02-8486-0cc4bf37812e · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models A framework for few-shot language model evaluation, 07 2024

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.938496Z digest=sha256:ae6ca7e376d1de54531e65a597cba85cdd93992e6d79f09b2d7a6b97ddd68474

Observation dbb27202-deb7-4ee8-b609-32f7de9cb3a5 · outbound

This paper cites The Zamba2 Suite: Technical Report.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models The Zamba2 Suite: Technical Report

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.944262Z digest=sha256:98ed0d9ade1da3574d43211758aed09764f1796b11d244b05747e160018ea878

Observation 5f225b63-69b9-4ea9-b4b8-18736b566bb1 · outbound

This paper cites Zamba: A Compact 7B SSM Hybrid Model.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Zamba: A Compact 7B SSM Hybrid Model

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.949758Z digest=sha256:ad1fde1e8e90039189af555cda8c5fd4a153041078e1f65515dda00b5c67cf75

Observation 10ecef21-5855-491d-b081-13a8de0062af · outbound

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

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.956129Z digest=sha256:33b591a3af9b4199aa5c54ab8c7dab92cef9d6cafb87f7007b889fdbb6a3ce0c

Observation 0b67f814-76af-4e86-b122-b0ff882ebc5a · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 16

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raw_fallback, observed 2026-08-11T13:17:50.949685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:49.961962Z digest=sha256:929539bc4d859dc474c0344fa2c63b3b244a693490ec837a49833a6fef090108

Observation 37114281-c495-4853-a33d-1df03218e912 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Efficiently modeling long sequences with structured state spaces

Reference 17

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source=arxiv_source observed=2026-08-11T13:17:49.967204Z digest=sha256:d8038e5a7f8ebed88269e55fde8f57711a8f1d68f213f0c5e3983fb68b3b2f98

Observation 41d3a64a-b167-4353-8e01-0f16147e44cc · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 18

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source=arxiv_source observed=2026-08-11T13:17:49.972203Z digest=sha256:92d15aefc3a7d411dc10c3511cf1e13b9c877bddbf7b0d08260110a235e47db2

Observation 91ffb047-a5c6-4702-831c-95d079ad6b3d · outbound

This paper cites Lo RA : Low-rank adaptation of large language models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Lo RA : Low-rank adaptation of large language models

Reference 19

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source=arxiv_source observed=2026-08-11T13:17:49.979865Z digest=sha256:39157d66b58f4d0566cc551c53467962d5874ae3e42dd646c13a9bf6c1a6a778

Observation 21713759-3eec-407b-a8ff-790f027026f6 · outbound

This paper cites Kakade, and Eran Malach.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Kakade, and Eran Malach

Reference 20

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raw_fallback, observed 2026-08-11T13:17:50.907014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:49.985794Z digest=sha256:8294627b3a3d32b9e82919e246167d8a82935ecb81c3199ca32cce45d14c51a6

Observation 5998954d-9755-477d-bd51-287ca11d8116 · outbound

This paper cites A new approach to linear filtering and prediction problems.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models A new approach to linear filtering and prediction problems

Reference 21

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source=arxiv_source observed=2026-08-11T13:17:49.992133Z digest=sha256:c2bcdca792be7c0d1af007b9aef6f35652643dff798cc483822cc6396e388ff3

Observation e62fe381-9961-49d5-82bb-71d889104a49 · outbound

This paper cites Needle in a haystack - pressure testing llms., 2023.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Needle in a haystack - pressure testing llms., 2023

Reference 22

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raw_fallback, observed 2026-08-11T13:17:50.878012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:49.999056Z digest=sha256:1e63369866350badcc8751d9b984eebaaeee499b0b7d37fc42eacde863690666

Observation 504490ba-9a8d-47ce-9a28-cc37f1b0cb92 · outbound

This paper cites Reformer: The efficient transformer.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Reformer: The efficient transformer

Reference 23

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raw_fallback, observed 2026-08-11T13:17:50.858575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.004797Z digest=sha256:429b220c625d3af10b91ecc68b0aba43497e423733e048daff30b36c222c71a5

Observation 6d58761d-8d0d-4c61-ac28-9ec0103d8f41 · outbound

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

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Jamba: A Hybrid Transformer-Mamba Language Model

Reference 24

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source=arxiv_source observed=2026-08-11T13:17:50.011744Z digest=sha256:72c5558b13d4df6842897186a07950c7e5ac6037d6f676c785fe7ae45e36ea4b

Observation 48b6bd14-8048-4200-a6a5-45d88cd009f9 · outbound

This paper cites Random-access infinite context length for transformers.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Random-access infinite context length for transformers

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-11T13:17:50.833815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.017876Z digest=sha256:782f6334654d3369bf8118f7126b6af56137870cd94428bd31deebf3c6e10321

Observation 4073eaf0-3557-465a-a466-50e9fe675b41 · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 26

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source=arxiv_source observed=2026-08-11T13:17:50.024190Z digest=sha256:2cfa3bfc2cba61134c6a6738587715a666ecfb8906207c379d871f47295a904d

Observation aca8725f-8328-4d10-b570-318845852a81 · outbound

This paper cites Resurrecting recurrent neural networks for long sequences.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Resurrecting recurrent neural networks for long sequences

Reference 27

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no resolver link, observed 2026-08-11T13:17:50.032936Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:50.032936Z digest=sha256:c2fe8315ff8ea91d7a9d1ac8fd7e2e6396894c425e983564cf17b3d8e3ac42b0

Observation 518d6248-0dfe-49f0-b3ea-19c32309e2aa · outbound

This paper cites Compressive Transformers for Long-Range Sequence Modelling.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Compressive Transformers for Long-Range Sequence Modelling

Reference 29

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source=arxiv_source observed=2026-08-11T13:17:50.044905Z digest=sha256:a98ee5ab656c6feeca9af18de433ffa04e0ef2de1289ed58accccb3a802f951e

Observation 25cc2dd1-d61f-41c3-8772-1aebafb08b95 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-11T13:17:50.803885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.050282Z digest=sha256:86170bc21d84b033f8e5d71bd4ba5276823eba9740a20b2c2492c71ae8583770

Observation 14c94d2f-d4f2-4136-9692-b3fd84bc2eb6 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Roformer: Enhanced transformer with rotary position embedding

Reference 31

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no resolver link, observed 2026-08-11T13:17:50.055693Z

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source=arxiv_source observed=2026-08-11T13:17:50.055693Z digest=sha256:2f256916d1a6995550333f35762f6a3ae0a5e096d23eb5ed358fbbef2ce81780

Observation e07e052a-cb64-43f1-8cdd-7634813984f0 · outbound

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

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Retentive Network: A Successor to Transformer for Large Language Models

Reference 32

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:50.060930Z digest=sha256:85519709db2df75bcc2f1d03199909d921c4146437dd06278d43c1e089b6cdf7

Observation 07be2ee7-0767-48e3-b3b3-9e3d28996c18 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models LLaMA: Open and Efficient Foundation Language Models

Reference 33

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source=arxiv_source observed=2026-08-11T13:17:50.066772Z digest=sha256:f56a00567f54c2e520212cdbcc4403019dc66e425f8f08362721d4cec5d8aa40

Observation c0a4afe6-fd97-4125-b57b-38f35fc48562 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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source=arxiv_source observed=2026-08-11T13:17:50.073427Z digest=sha256:cbb88564940c3c74109c3012e45cf7175950455f0044fa8d6852c1d8fd35d330

Observation 9dd9d08b-4aee-4f94-9b54-c6fd980c4f09 · outbound

This paper cites Attention is all you need.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Attention is all you need

Reference 35

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:50.079275Z digest=sha256:b23b4c26ddbf9178d21b939ae8409313d54016ad97a6ba54fb1fda261f141cf5

Observation 86624abd-add7-40c6-98a0-40276aa9fe74 · outbound

This paper cites An Empirical Study of Mamba-based Language Models.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models An Empirical Study of Mamba-based Language Models

Reference 36

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source=arxiv_source observed=2026-08-11T13:17:50.084628Z digest=sha256:d2ece0d5fe77db94fd072f02ddee59116cfd9989711eff2955dcb965eef3cef3

Observation 6bd61d83-af68-4a5c-81d1-9adcb0b53f67 · outbound

This paper cites Gated linear attention transformers with hardware-efficient training.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Gated linear attention transformers with hardware-efficient training

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-11T13:17:50.759738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.091299Z digest=sha256:22c49ca59e4580eb3093b31e92717d7e3f611b67a299e9f7e9ddd096b8f2adff

Observation 8593ecd5-80d9-4a23-9297-cc25de4b721c · outbound

This paper cites Parallelizing linear transformers with the delta rule over sequence length.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Parallelizing linear transformers with the delta rule over sequence length

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:17:50.729200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.097298Z digest=sha256:e938bbd9624dfb5f1965e24133a01733d5e872e6f427643e4a140cdff8ea1c77

Observation 2184fe2b-dfe7-411c-bec1-69aa47476cf9 · outbound

This paper cites Gated delta networks: Improving mamba2 with delta rule.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Gated delta networks: Improving mamba2 with delta rule

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T13:17:50.102355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:50.102355Z digest=sha256:d6b94fa507fc150ace50703cc4cc8a4b35a42080ed5b4f94287510534ab20709

Observation 9ddca98a-9edd-4b7f-8d36-00f4ec58a5f6 · outbound

This paper cites B mojo: Hybrid state space realizations of foundation models with eidetic and fading memory.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models B mojo: Hybrid state space realizations of foundation models with eidetic and fading memory

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:17:50.699464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.107211Z digest=sha256:01c660e1e82dfad8b2859e92db39bdd65d4693ab6d0a221bcbe0cedfc32b6826

Observation 5aeedc52-bc79-45c1-9c69-f35ff49dc6b8 · outbound

This paper cites Lots of code, 2017.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Lots of code, 2017

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:17:50.673191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-11T13:17:50.112407Z digest=sha256:163ec328e10f5d79cac85344afb8c284466700025e0dcfc0e243be07e118dbe7

Pith citing papers

Observation a1d0e94c-c38e-4585-a619-9a6ee41d3b8d · inbound

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression cites this paper.

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-21T18:00:27.073660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-21T17:59:23.826110Z digest=sha256:1aa86c6bb175092e2d14fef435de73ff3ccb0369936fd044f651996413f9a5d7

Observation 5e8a8e07-7854-4c4c-9cff-c2152229a5ab · inbound

DLLG: Dynamic Logit-Level Gating of LLM Experts cites this paper.

DLLG: Dynamic Logit-Level Gating of LLM Experts Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T07:36:45.099619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-28T06:50:32.988192Z digest=sha256:925c5c332068832c4f82b58a56ea5f83c5286eac6bba8f75618c5935225c0e5b

Observation bfb33b55-a254-4736-9b0e-ac4aef770f4d · inbound

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale cites this paper.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-02T06:39:23.417302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T06:39:23.417302Z digest=sha256:a9e87e750f554b0fc42244f7df0599fb05b04a57f46f644210763657edeca6d3

Observation 380a1cb1-8ccf-4367-b2c1-7080e1d36380 · inbound

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale cites this paper.

The Capability Convergence Hypothesis: Capability from Access Structure, Not Scale Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-03T02:03:25.368313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:03:25.368313Z digest=sha256:96a35b0c2dc529c950ed691f562a02229a33994cf01ebba44a8f21516dbc7923