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Paper Citation Record · LEDGER

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2502.04958.

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

pith.paper-citation-record.v1
2502.04958 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:54:10.668785Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T22:20:38.938305Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T22:23:21.326850Z

Reference resolution

38 of 38 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f63371b4-b36d-4507-9685-231aadac7dd1 · outbound

This paper cites Structured Pruning of Deep Convolutional Neural Networks.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Structured Pruning of Deep Convolutional Neural Networks

Reference 1

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local_arxiv, observed 2026-08-08T20:54:11.197149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T20:54:10.484807Z digest=sha256:63a1b09c227c20997624f5530b2f91ac9c0a941b972eb634db116073d18fd744

Observation caead39c-5d37-4cb8-9a2e-a412c3c40dbe · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 2

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.490879Z digest=sha256:910eb28fe4cd68b35a39665886d6976e47bf701b398474cc18ad478a9f464c4e

Observation fab06dbc-d845-4aae-86ce-03c708de9917 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 3

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source=arxiv_source observed=2026-08-08T20:54:10.496091Z digest=sha256:2daea7e45308ad806f5da4ea7b6907fbd72378034c7689c015ce6d3ec7074b8b

Observation d961e195-a815-4ae1-86ff-16efccf4778f · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 4

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.501865Z digest=sha256:93f533fe5c432c86149257d51357bdfbcb3537e86ccb88a3d12323cb697445ee

Observation be03b876-e26b-4e5b-9705-afb7f58fdcf7 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 5

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.506847Z digest=sha256:6b4415566c72d1d530136d1203b9152ef21feaaea2e293c55035880ea3ffb181

Observation 3963d838-6f72-4e7b-b86b-02cce32013ca · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model QLoRA: Efficient Finetuning of Quantized LLMs

Reference 7

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no resolver link, observed 2026-08-08T20:54:10.517785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.517785Z digest=sha256:904252b4e79d94e916d9cea169b701ee237dc67dee31c8df6f8b854810397751

Observation 6b31efd2-9fd9-492e-81dd-255382b70c8b · outbound

This paper cites Sparse Low-rank Adaptation of Pre-trained Language Models.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Sparse Low-rank Adaptation of Pre-trained Language Models

Reference 8

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T20:54:10.522584Z digest=sha256:1330c99d3371d2cffaf3504909d813f0b56ea01d729d57cec453219f102dfdc0

Observation d5c5cb5a-8c57-4f2a-b977-295868625434 · outbound

This paper cites Dolan and Chris Brockett.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Dolan and Chris Brockett

Reference 9

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-08T20:54:10.527384Z digest=sha256:8a03bb4ae0ac99b6043c5c72cbb32029ec436b652250d683c38562e259decf35

Observation 36327ce8-4612-4138-9a2d-b6834583184c · outbound

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

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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source=arxiv_source observed=2026-08-08T20:54:10.531999Z digest=sha256:791c82efe49648500bbe3a01500d8183c38ca3530def5a2cbdb30db0a98511fe

Observation 67c75efd-5040-4c58-9678-0fa52a687682 · outbound

This paper cites HiPPO: Recurrent Memory with Optimal Polynomial Projections.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model HiPPO: Recurrent Memory with Optimal Polynomial Projections

Reference 11

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.536813Z digest=sha256:2c2dc23fc2565679c6cb78a395b425c26366165597171d92d63258fd178b8db6

Observation b92600ea-85fd-4b9e-9354-3dfe982ae3ff · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T20:54:10.541864Z digest=sha256:a9f8c6ad9c3737e52d17ffae972c99e5c47d60970b7746394d55e729b0a2dc73

Observation 12f6c73a-93b7-4b6f-84a9-50152ed69620 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 13

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source=arxiv_source observed=2026-08-08T20:54:10.546588Z digest=sha256:34eba96ae50095897efdc259dd4e4a2a78caf025da991e6d4a4f090bd846f243

Observation d66d06c7-9c70-4025-ab40-7367442bf234 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Parameter-Efficient Transfer Learning for NLP

Reference 14

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source=arxiv_source observed=2026-08-08T20:54:10.551378Z digest=sha256:ad00986bb3ebc8e784d14362e07975dfbed5db5612eff7e1fd8c36865c23295a

Observation 3555d198-4914-4b1e-af3c-2a4a0a5102c3 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 15

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.555997Z digest=sha256:d35e9f078d1cf77984f4632176824445b750c97e5bee39cb1fb6e2a1b9086a67

Observation 02b3ff50-a83c-4cd5-99f4-743e78a916fc · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 16

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source=arxiv_source observed=2026-08-08T20:54:10.560517Z digest=sha256:051be221b86d43dc1e5f34149bc80d82b088894209db058f76b2933107f291be

Observation 95c293a8-c330-4f0b-8c3f-1d75a8e1fab4 · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model VeRA: Vector-based Random Matrix Adaptation

Reference 17

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source=arxiv_source observed=2026-08-08T20:54:10.564810Z digest=sha256:38aad878fbd5c30a6af70bbe7e6c8bd318526418440465a597398de821a94cc2

Observation 3b893dcd-8f27-4e9e-8a7d-40db187d7e59 · outbound

This paper cites The NarrativeQA Reading Comprehension Challenge.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model The NarrativeQA Reading Comprehension Challenge

Reference 18

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source=arxiv_source observed=2026-08-08T20:54:10.569693Z digest=sha256:de24679e03c3da4dde114c09dba8f080d8b21bb6fe722d5a55abe553960624eb

Observation 3f5fc609-49c2-4b1c-90dc-6cd3b89314e2 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-08T20:54:10.574422Z digest=sha256:271772fbdf45b46ff400f57211409eb8761e641aa38c12927c39acab4e55e7a8

Observation e91b2cca-f942-4f5c-8c3f-137ea382a018 · outbound

This paper cites MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Reference 20

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source=arxiv_source observed=2026-08-08T20:54:10.579181Z digest=sha256:78a6eac8aea8cd654ff5a5a41bebce94ed63a4b2776180ec5a4feae053028ee7

Observation ba29d1fd-e405-4833-8ee2-1b9a795413e1 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 21

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source=arxiv_source observed=2026-08-08T20:54:10.584009Z digest=sha256:2cf5299fee867d3eb17ed7a3e1e412e511ed6e223a4ac894bb74b324cafc1e29

Observation df072850-be06-4ed9-9d18-47e2a91ecabc · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.588783Z digest=sha256:31080462539bec518d411433f4bc786625b96856dcd9d4d9cfb89d061c4114a6

Observation 1cea7b6d-03f0-4d79-b005-f3be4fb59c66 · outbound

This paper cites HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.593617Z digest=sha256:1aaa63119b8f9fbd46eb1d8041e600da0c3870a9a03e805425ea44032ec7ff67

Observation 7b6da912-f647-4ac9-b5d1-0898e2c014a1 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 24

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verified exact
doi, observed 2026-08-08T20:54:10.728549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T20:54:10.598383Z digest=sha256:15bad96eaf62091ca5cba79ad9bcb77e1b3c92562dd3de55d8bf33c88df4e115

Observation b3c51b38-47c8-4d6d-9fc3-bf6104c821f8 · outbound

This paper cites WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Reference 25

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source=arxiv_source observed=2026-08-08T20:54:10.603068Z digest=sha256:7aa27b3bbe736ba8067882374fa7b1f82e2d93b32449d93c59570f2bc534b1e0

Observation 2e1e0a5a-2ced-4212-9f22-24402fcc7a43 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-08T20:54:10.607834Z digest=sha256:88257e44e032856b2afc9b77db7e14c36bf32c3b2083f4d33b45165f7d437665

Observation 918b440a-68b7-4579-a8cd-44ca445cf393 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.612599Z digest=sha256:fbb33ec59be622b788a84664e3745405196129ab2cb84aebd56e55cfd5a0dbf1

Observation 8a5d709e-fe13-4141-b20f-8b7f9aa10d77 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 28

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source=arxiv_source observed=2026-08-08T20:54:10.617242Z digest=sha256:2d5d6744b7e8b7d2fc3fa26eb4b7f352914372c64f26ebf9a7bc93a285223993

Observation 6fd94553-9ef4-4da5-a61f-8eda27aa921a · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 29

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source=arxiv_source observed=2026-08-08T20:54:10.621733Z digest=sha256:9a54ad4d7ba6e09280fdc9e1b3b692d138aa3457e4ce981d6d815f1627c8da38

Observation f38e15f7-c06d-4b28-881c-ec9eb291dc65 · outbound

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

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.626309Z digest=sha256:6cb7d7fba175644e648b45cd6370ebdac53bafba95ec26486f3d4d2b742fb78b

Observation 0254b9ec-3565-4b91-8db5-7bf555c043f3 · outbound

This paper cites Neural Network Acceptability Judgments.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Neural Network Acceptability Judgments

Reference 31

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source=arxiv_source observed=2026-08-08T20:54:10.631118Z digest=sha256:9fe76d8cfcbb7210e92b2a6d5604098c0462ea997a7ff639eb5919c5c806c7aa

Observation b8dad6fe-358e-4ecc-a6e1-1b2f2c813992 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 32

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raw_fallback, observed 2026-08-08T20:54:11.235516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T20:54:10.635736Z digest=sha256:9b09690b4903b243b289647a7d016ef2ba37698d480f28947bbf36fad51b5eb3

Observation d4927c62-88e1-486b-9fd8-93c7ba27d657 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-08-08T20:54:10.640031Z digest=sha256:21c0e9a09dd2d892f5a20c7f0e0ca0299ebae6bd0e34484f140a0580244747d5

Observation deeb3919-5091-460d-9ed0-2fdfde551f54 · outbound

This paper cites IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks

Reference 34

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local_arxiv, observed 2026-08-08T20:54:10.909097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T20:54:10.644726Z digest=sha256:8d82bbb92f0534087dc55aa9edf1b824eafb3cba2170b4309d2fd09ed6ddff61

Observation 5734132b-240e-4add-8d89-1260e5934860 · outbound

This paper cites an unresolved cited work.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model Unresolved cited work

Reference 35

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:54:10.649457Z digest=sha256:d2adda9ed5cd1cb3a9ecf966479f6e5e43b7665367ee74d19f497250e9c4a4dd

Observation 760a7d2e-79eb-4702-9abc-2ac4208b86cd · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 36

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source=arxiv_source observed=2026-08-08T20:54:10.654211Z digest=sha256:a4844f1e30ceb233729cb57aa6bedd4625566ccf58fa314e533f1e215f782d0d

Observation ff8bcbba-7737-4ee6-bcde-5841c033d5aa · outbound

This paper cites ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension

Reference 37

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source=arxiv_source observed=2026-08-08T20:54:10.659073Z digest=sha256:72b04b812531bed825ba3fb4e2f54d9549a696edc3eb0fcfcbdd044476bc315c

Observation 793592b3-e2bb-41b4-8d2f-4546d1316ca8 · outbound

This paper cites URL: " 'urlintro :=.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model URL: " 'urlintro :=

Reference 38

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source=arxiv_source observed=2026-08-08T20:54:10.663757Z digest=sha256:c69c46841c1fd39057ba681cf950b955cd13343db58ac72031fc539e5c5bb18f

Observation fbb53ff5-a892-470d-986a-ea2407716389 · outbound

This paper cites write newline.

SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model write newline

Reference 39

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source=arxiv_source observed=2026-08-08T20:54:10.668785Z digest=sha256:227d23d668cec9815ccebc3faa47ed1b0182b9c6b786f41a1e8e2879f866baca

Pith citing papers

Observation ec94f833-e1c1-4fb8-bf0e-727f8c672594 · inbound

S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models cites this paper.

S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model

Reference 19

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arxiv_id, observed 2026-05-13T22:23:21.328512Z

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source=pdf_text observed=2026-05-13T22:20:38.938305Z digest=sha256:df0929fbc20575cf9837cc17e0ca23d2a2497f39f3680655094c2b9c2feed2df