Paper Citation Record · LEDGER

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation

As of 23 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2501.04315.

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

pith.paper-citation-record.v1
2501.04315 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:10:00.615053Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:10:07.924290Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 665bdf85-456f-455f-92f3-affb81795985 · outbound

This paper cites Redpajama-data: An open source recipe to reproduce llama training dataset,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Redpajama-data: An open source recipe to reproduce llama training dataset,

Reference 1

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Observation 4abbd47d-33c0-44b1-b97f-fd14798af590 · outbound

This paper cites Openllama 3b v2 finetuned on sharegpt,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Openllama 3b v2 finetuned on sharegpt,

Reference 2

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Observation 30845dd2-06e3-4825-a471-0978dcbd5ef0 · outbound

This paper cites Mamba-gpt-3b-v2,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Mamba-gpt-3b-v2,

Reference 3

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Observation 5e342e86-43e8-4654-a872-f4c8937bb64d · outbound

This paper cites Openalpaca: A fully open-source instruction-following model based on openllama,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Openalpaca: A fully open-source instruction-following model based on openllama,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.885640Z

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Observation 682a7360-01a3-489a-b4d0-2feb6fb84dd0 · outbound

This paper cites Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

Reference 5

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Observation 8b3ec9cd-0b73-4c40-b997-01b6751c6292 · outbound

This paper cites Advancing model pruning via bi-level optimization,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Advancing model pruning via bi-level optimization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.875841Z

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Observation f7340b62-2f16-465c-a368-69b17c49ee97 · outbound

This paper cites Pruning-as-search: Efficient neural archi- tecture search via channel pruning and structural reparameterization,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Pruning-as-search: Efficient neural archi- tecture search via channel pruning and structural reparameterization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.865599Z

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Observation fd3e7a24-63a9-40ba-a43e-fa0300a5e648 · outbound

This paper cites Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.856452Z

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Observation f99bd9bd-0fc7-4511-a3c1-c8cb369b39e5 · outbound

This paper cites You already have it: A generator- free low-precision dnn training framework using stochastic rounding,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation You already have it: A generator- free low-precision dnn training framework using stochastic rounding,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.847361Z

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Observation ac5875a4-77f6-4a85-a774-62c34a894212 · outbound

This paper cites Layer freezing & data sieving: missing pieces of a generic framework for sparse training,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Layer freezing & data sieving: missing pieces of a generic framework for sparse training,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.838209Z

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Observation 691c7f17-3b16-423b-95a7-00c79bee2305 · outbound

This paper cites Mest: Accurate and fast memory- economic sparse training framework on the edge,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Mest: Accurate and fast memory- economic sparse training framework on the edge,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.828648Z

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Observation cd1cb72b-646c-4cad-9a7e-795d12cbf1c4 · outbound

This paper cites Waxing-and-waning: a generic similarity-based framework for efficient self-supervised learning,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Waxing-and-waning: a generic similarity-based framework for efficient self-supervised learning,

Reference 12

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Observation 5bcb53a8-6f90-4608-9ec2-3aa37c680d23 · outbound

This paper cites Exploring token pruning in vision state space models,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Exploring token pruning in vision state space models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.809327Z

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Observation 26a19b33-ef43-43c2-bf96-7cbbf1cfb19c · outbound

This paper cites Pruning foundation models for high accuracy without retraining,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Pruning foundation models for high accuracy without retraining,

Reference 14

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Observation 97a12c48-14fc-4942-9ede-39c2c521402c · outbound

This paper cites Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.788105Z

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Observation d88d4049-5cf0-4e12-adff-69ad4819b15f · outbound

This paper cites Tinyadc: Peripheral circuit-aware weight pruning framework for mixed-signal dnn accelerators,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Tinyadc: Peripheral circuit-aware weight pruning framework for mixed-signal dnn accelerators,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.777068Z

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Observation fdb842d3-af98-47aa-a1a5-1c8e960824d0 · outbound

This paper cites Automatic mapping of the best- suited dnn pruning schemes for real-time mobile acceleration,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Automatic mapping of the best- suited dnn pruning schemes for real-time mobile acceleration,

Reference 17

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Observation 605c72cb-736b-4c8d-b93e-2f93e6a15b26 · outbound

This paper cites Peft: State-of- the-art parameter-efficient fine-tuning methods,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Peft: State-of- the-art parameter-efficient fine-tuning methods,

Reference 18

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Observation 4a8ec4f4-896c-4166-a94d-ee13cf95c569 · outbound

This paper cites Tsla: A task-specific learning adaptation for semantic segmentation on autonomous vehicles platform,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Tsla: A task-specific learning adaptation for semantic segmentation on autonomous vehicles platform,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.748260Z

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Observation 4f1664eb-fbf2-45dd-9c97-dd730c7bb40e · outbound

This paper cites A scalable real-time semantic segmentation network for autonomous driving,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation A scalable real-time semantic segmentation network for autonomous driving,

Reference 20

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Observation 1266b36a-5c74-4e99-9a45-1404408a406e · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation LoRA: Low-rank adaptation of large language models,

Reference 21

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Observation 1d9de0b6-3f8d-4757-a35c-426f7e7f0c45 · outbound

This paper cites Physical-space multi-body mesh detection achieved by local alignment and global dense learning,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Physical-space multi-body mesh detection achieved by local alignment and global dense learning,

Reference 22

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Observation 5c70e48b-ecd3-481c-a76f-2970c8de0c81 · outbound

This paper cites Df-vton: Dense flow guided virtual try-on network,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Df-vton: Dense flow guided virtual try-on network,

Reference 23

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Observation 8f5a5e91-cf75-449c-9cbd-b23ecf58f5cc · outbound

This paper cites An explainable convolutional neural networks for automatic segmentation of the left ventricle in cardiac mri.,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation An explainable convolutional neural networks for automatic segmentation of the left ventricle in cardiac mri.,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.698686Z

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Observation 27f8f52a-ac11-4d0a-ac13-35c5c2765314 · outbound

This paper cites Fast and memory-efficient video diffu- sion using streamlined inference,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Fast and memory-efficient video diffu- sion using streamlined inference,

Reference 25

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Observation 59a921d1-2878-43ec-b3ba-3776dc6a7485 · outbound

This paper cites Instructgie: Towards generalizable image editing,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Instructgie: Towards generalizable image editing,

Reference 26

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Observation 9a8be1e1-ae5c-4bb0-9cdc-7783cdf2b7dd · outbound

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

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 27

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This paper cites Work in progress: Mobile or fpga? a comprehensive evaluation on energy efficiency and a unified optimization framework,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Work in progress: Mobile or fpga? a comprehensive evaluation on energy efficiency and a unified optimization framework,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.670111Z

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Observation c36d7990-a1d0-49b8-827f-cfd17df45c1a · outbound

This paper cites Efficient pruning of large lan- guage model with adaptive estimation fusion,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Efficient pruning of large lan- guage model with adaptive estimation fusion,

Reference 29

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This paper cites Mobile or fpga? a comprehensive evaluation on energy efficiency and a unified optimization framework,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Mobile or fpga? a comprehensive evaluation on energy efficiency and a unified optimization framework,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.660774Z

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Observation a90dc59c-aa5d-437f-8e3b-e5c23aa1c487 · outbound

This paper cites Efficient transformer-based large scale language representations using hardware-friendly block structured pruning,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Efficient transformer-based large scale language representations using hardware-friendly block structured pruning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.651667Z

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Observation 79ad3994-b192-4a7b-8679-460f3507c3b7 · outbound

This paper cites Search for efficient large language models,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Search for efficient large language models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.642542Z

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Observation bb7f38db-0ab1-4874-b2db-f2a79398a6e2 · outbound

This paper cites Rethinking token reduction for state space models,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Rethinking token reduction for state space models,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.632729Z

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Observation 9fe8d963-5b9e-4adc-b030-227c8b1a29fe · outbound

This paper cites A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA

Reference 34

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Observation 5ef16d4b-505c-4329-95ae-df5dbef22e8a · outbound

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

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation LLaMA: Open and Efficient Foundation Language Models

Reference 35

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This paper cites An efficient cnn for radiogenomic classification of low-grade gliomas on mri in a small dataset,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation An efficient cnn for radiogenomic classification of low-grade gliomas on mri in a small dataset,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.623550Z

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Observation 0ecc7fa4-74e6-4428-8b2c-28f0f0b582e5 · outbound

This paper cites An interpretable cnn for the segmentation of the left ventricle in cardiac mri by real-time visualization.,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation An interpretable cnn for the segmentation of the left ventricle in cardiac mri by real-time visualization.,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.613068Z

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Observation a0cfab7e-2847-4149-bd82-f4e072ce86dd · outbound

This paper cites Brain tumor classification on mri in light of molecular markers,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Brain tumor classification on mri in light of molecular markers,

Reference 38

Resolution: verified exact; raw_fallback, observed 2026-08-10T21:44:11.373564Z

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Observation 6b971f97-7d8f-4a88-b621-a92b7659991f · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.603584Z

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Observation 98f9c587-0d0d-47cb-8455-552035684f29 · outbound

This paper cites All you need is a good init.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation All you need is a good init

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T21:44:11.170282Z

Source-reported events for the cited work

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Observation 5e47f1ce-7861-4c6b-bfc0-68614fe3084f · outbound

This paper cites Llama 3 model card,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Llama 3 model card,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.593709Z

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Observation a9a93bc4-bbb5-43cd-b434-aada29e21fa2 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Prefix-tuning: Optimizing continuous prompts for generation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.582073Z

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Observation 75b4f66b-67d5-4ba9-a111-8e3eb3ab2a5f · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Parameter-efficient transfer learning for nlp,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.569454Z

Source-reported events for the cited work

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Observation cf05d62e-1aeb-4104-bba6-e69bd7c12d87 · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Towards a unified view of parameter-efficient transfer learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.559191Z

Source-reported events for the cited work

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Observation 91963442-a70b-44e0-8b91-068be213e453 · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation BoolQ: Exploring the surprising difficulty of natural yes/no questions,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.548357Z

Source-reported events for the cited work

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Observation a08d3ebb-5523-435a-af76-222a064d8106 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Piqa: Reasoning about physical commonsense in natural language,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.536557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5d5a6e77-cc47-41ad-8828-588b805eec11 · outbound

This paper cites Social IQa: Commonsense reasoning about social interactions,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Social IQa: Commonsense reasoning about social interactions,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.524971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f1eeee2-62f2-4087-8016-7a5ad797bea1 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence?,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Hellaswag: Can a machine really finish your sentence?,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.514284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7df80f60-3666-47bd-8971-33909a8d01b7 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Winogrande: An adversarial winograd schema challenge at scale,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.503691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a82488f1-27ad-4ae7-bac3-fd7a75b70a54 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T21:44:11.203167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 025e9418-8e64-4ad9-af38-ec6df1a880ad · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering,.

RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation Can a suit of armor conduct electricity? a new dataset for open book question answering,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:44:11.492877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation eeeceb25-dbae-490c-b39b-c84c34924aa4 · inbound

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation cites this paper.

Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:10:07.976715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.