MAP: Revisiting Weight Decomposition for Low-Rank Adaptation | Paper Citation Record

Observation 1ca0b6fc-f0ec-49aa-8481-806934849642 · outbound

This paper cites online" 'onlinestring :=.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation online" 'onlinestring :=

Reference 1

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Observation 772970f9-25e5-4619-9cfa-96585a0affe7 · outbound

This paper cites write newline.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation write newline

Reference 2

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Observation 614b927c-bac2-480d-bd63-147f6848bdab · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 4

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This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 5

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 6

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 7

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 8

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 9

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Observation 1987957f-6eb5-4953-87cb-d6b91aac1091 · outbound

This paper cites SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:49.599886Z

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Observation feb3ab40-3542-4bc5-abbc-b12f6eff2eab · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:55.438457Z

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Observation 17016420-7995-4654-8f92-57afb46e764d · outbound

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

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:49.767661Z

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Observation 1eb47673-376e-42f4-bc42-c572c518928d · outbound

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

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:49.859670Z

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Observation 9659ae7c-bbe5-4087-9139-acbf0559f55e · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 14

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Observation 98e94dde-014e-48e4-a371-ce294e712a30 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:49.930347Z

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Observation 1c1326f5-5fa3-4adf-a857-810abf5f5f2b · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 16

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Observation ead4524a-68f9-438b-bebc-40a840d72ac4 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 17

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Observation dfe9c070-53a7-4071-a6da-a0e05cf0d086 · outbound

This paper cites TriLoRA: Integrating SVD for Advanced Style Personalization in Text-to-Image Generation.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation TriLoRA: Integrating SVD for Advanced Style Personalization in Text-to-Image Generation

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:56:54.129162Z

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Observation 0d42d794-501a-4cd7-90a6-557c51663075 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:55.327029Z

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Observation 90e4b258-f9ab-4db2-9f5f-48f8cba04033 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 20

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Observation 60f2895d-e6e4-403e-aac4-09edf17a3ef5 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.216063Z

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Observation 47f0c38a-ad61-4ce8-8206-d8ba346f9348 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:55.206249Z

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Observation eb1c6da4-568f-4f59-ada0-c9354b2e8999 · outbound

This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 23

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Observation 038993e7-6df5-478d-8bfd-b28463de1aa5 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.392573Z

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Observation 4c753790-2f88-457f-834c-f87194925986 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation LoRA: Low-Rank Adaptation of Large Language Models

Reference 25

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Observation 406df8a0-638b-4d79-a790-89c9bd2bfca1 · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.473580Z

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Observation 3250105b-6543-4f89-aeca-7a5a8124601c · outbound

This paper cites FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.559783Z

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Observation 6c2f0713-e5c6-41f6-bb79-48227136aba3 · outbound

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

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation VeRA: Vector-based Random Matrix Adaptation

Reference 28

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Observation 1bc11847-01dd-493b-9c6d-5f29cf5762d9 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 29

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Observation 08ff6a5f-b83f-4e92-aaf3-b4a0781dddb9 · outbound

This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Measuring the Intrinsic Dimension of Objective Landscapes

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.695663Z

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Observation 85bd7e26-3591-4d20-84a9-159d907207a0 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.732444Z

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Observation c134938e-d312-4aa1-b2ad-1a813e0bd2c3 · outbound

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

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:50.798645Z

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Observation 60955e72-c881-47c6-a171-eb9863c74cf6 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 33

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Observation 92c7d13e-ba6b-4f59-887c-f867d163ffa3 · outbound

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

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 34

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Observation 3e43ce8a-f62a-4134-a9d2-eb8ca11c12a0 · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 35

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Observation 3bd94438-8996-4db4-a437-e165a9d2b8e1 · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 36

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Observation ab64a806-c0ba-4b3e-97bf-0c18372255ef · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:51.113652Z

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Observation 3b12dfd5-e290-4470-871a-65e9a6f516c4 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 38

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Observation 0f41ec39-c53f-4df4-bad1-eabcc13f0278 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 39

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Observation 066a85f9-69f5-4d63-9387-44265fd4bb41 · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 40

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Observation 412d7d24-3de1-479b-8195-8f1b11c8383c · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:51.322054Z

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Observation 38f231e8-e570-42a3-a9d9-01605eca9236 · outbound

This paper cites BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:56:53.910034Z

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Observation c3da1922-b8a8-405a-b652-aac7a30f0c21 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:55.065393Z

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Observation b6dbbc48-a78c-49f7-80c0-332cf8f4ff0a · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 44

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Observation 8645d99c-6688-4ee2-a047-bc55b370aef4 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:51.597734Z

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Observation dcf33d18-a400-4702-ba0a-41f7c37537b3 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 46

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Observation 7ba04f40-4f82-4ad7-b5a2-2cbf35b6b88e · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:51.759936Z

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Observation 9dbb478c-0298-4279-9995-ddbb71eb3d8a · outbound

This paper cites Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:51.841376Z

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Observation 8a124e8f-8759-40a2-849e-88028d60b2cb · outbound

This paper cites Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:51.925097Z

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Observation d53014e1-eb9e-461b-9347-860866c9d17d · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:54.951779Z

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Observation 23423fc4-8c29-4922-8637-863edc35a8f6 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 51

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Observation c8775977-fb64-4597-b76d-677539022e15 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation SocialIQA: Commonsense Reasoning about Social Interactions

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:52.189707Z

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:52.249882Z

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Observation 9d73af99-a228-4b6e-872f-c5adb2b7e2a1 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:54.779882Z

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Observation 42cfacb5-e244-4047-ab73-78df1388b5e1 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:54.611917Z

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Observation 1d230afc-c454-4b01-a4af-5cc7613755dc · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 56

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Observation a6e4204e-565c-463d-9f71-05b8dfcc3064 · outbound

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MAP: Revisiting Weight Decomposition for Low-Rank Adaptation LLaMA: Open and Efficient Foundation Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:52.589167Z

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Observation 8c8a6283-fccb-4fa9-b195-cc647ef2e1bb · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:52.677424Z

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Observation 591586fa-f677-4a93-a88b-84a5c13b1e2e · outbound

This paper cites MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:52.746767Z

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Observation ca19bc3b-5444-47f1-b1e6-b143cd5084b1 · outbound

This paper cites BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:56:53.658863Z

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Observation 312527e4-cef5-4b02-bd61-8b4ebf6c2730 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:52.901271Z

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Observation 7d5e6af9-169d-47c5-86ae-4979a1c678c8 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 62

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Unavailable: canonical work link unavailable.

Observation 4b088509-c8a0-4c50-91b3-2fc77116cfd3 · outbound

This paper cites A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:53.049016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f48dfd-b751-4153-915b-f05cf7d24463 · outbound

This paper cites Mixture-of-Subspaces in Low-Rank Adaptation.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Mixture-of-Subspaces in Low-Rank Adaptation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:53.154441Z

Source-reported events for the cited work

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Observation 492ccd47-4c2e-416e-b426-b5b1d3e79990 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:54.471730Z

Source-reported events for the cited work

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Observation d4a9b2c2-83c4-4e50-8ab4-bac69f165638 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:53.335957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3ee00a-b4cd-4497-87bc-f7b973b9915c · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:53.391418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83e44434-17ee-4b8a-82a8-b40c5f592214 · outbound

This paper cites an unresolved cited work.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:56:54.306642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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