Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning | Paper Citation Record

Observation 7fa26ea9-22d6-410b-9502-ee123d636ba5 · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

Reference 1

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Observation 26b7a2cf-c658-48c5-a3e3-b6130722bcb6 · outbound

This paper cites Classification Problem Solving.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Classification Problem Solving

Reference 2

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This paper cites , title =.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning , title =

Reference 3

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This paper cites New Ways to Make Microcircuits Smaller---Duplicate Entry.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning New Ways to Make Microcircuits Smaller---Duplicate Entry

Reference 4

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This paper cites Clancey and Glenn Rennels , abstract =.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Clancey and Glenn Rennels , abstract =

Reference 5

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This paper cites and Rennels, Glenn R.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning and Rennels, Glenn R

Reference 6

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Poligon: A System for Parallel Problem Solving

Reference 7

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This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Transfer of Rule-Based Expertise through a Tutorial Dialogue

Reference 8

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This paper cites The Engineering of Qualitative Models.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning The Engineering of Qualitative Models

Reference 9

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This paper cites 2023 , eprint=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2023 , eprint=

Reference 10

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This paper cites Pluto: The 'Other' Red Planet.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Pluto: The 'Other' Red Planet

Reference 11

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This paper cites Attention is All you Need , url =.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Attention is All you Need , url =

Reference 12

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2020 , eprint=

Reference 13

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This paper cites arXiv preprint arXiv:2504.07139 , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning arXiv preprint arXiv:2504.07139 , year=

Reference 14

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Observation 56a918ba-39a8-4fb5-85d2-72fffde59e0d · outbound

This paper cites Nemotron-4 340B Technical Report.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Nemotron-4 340B Technical Report

Reference 15

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This paper cites Qwen2.5-Coder Technical Report.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Qwen2.5-Coder Technical Report

Reference 16

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This paper cites The Llama 3 Herd of Models.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning The Llama 3 Herd of Models

Reference 17

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This paper cites arXiv preprint arXiv:2512.13607 , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning arXiv preprint arXiv:2512.13607 , year=

Reference 18

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This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning , year=

Reference 19

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This paper cites Magistral.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Magistral

Reference 20

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This paper cites OLM o: Accelerating the Science of Language Models.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning OLM o: Accelerating the Science of Language Models

Reference 21

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This paper cites Smith and Hannaneh Hajishirzi , booktitle=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Smith and Hannaneh Hajishirzi , booktitle=

Reference 22

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This paper cites Olmo 3.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Olmo 3

Reference 23

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This paper cites DeepSeek-V3 Technical Report.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning DeepSeek-V3 Technical Report

Reference 24

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 25

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This paper cites 2025 , eprint=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2025 , eprint=

Reference 26

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Observation 1ec2a1a8-c8c9-4d27-afe9-3f40f7de60cb · outbound

This paper cites DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections

Reference 27

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This paper cites 2024 , eprint=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2024 , eprint=

Reference 28

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This paper cites , booktitle =.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning , booktitle =

Reference 29

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This paper cites Journal of Machine Learning Research , year =.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Journal of Machine Learning Research , year =

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:11:03.031093Z

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This paper cites Automatic early stopping using cross validation: quantifying the criteria , journal =.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Automatic early stopping using cross validation: quantifying the criteria , journal =

Reference 31

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This paper cites Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Revisiting Scaling Laws for Language Models: The Role of Data Quality and Training Strategies

Reference 32

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2025 , eprint=

Reference 33

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2026 , eprint=

Reference 34

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning The Thirteenth International Conference on Learning Representations , year=

Reference 35

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts

Reference 36

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This paper cites How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition

Reference 37

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2025 , eprint=

Reference 38

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2026 , url=

Reference 39

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Observation cb812d02-a546-4f08-bfbe-d200173fc496 · outbound

This paper cites From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 40

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Advances in Neural Information Processing Systems , editor =

Reference 41

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This paper cites Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 42

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Observation 97dd98aa-8cf1-4c5c-ba55-d683f409f3a5 · outbound

This paper cites C ommonsense QA : A Question Answering Challenge Targeting Commonsense Knowledge.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning C ommonsense QA : A Question Answering Challenge Targeting Commonsense Knowledge

Reference 43

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

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 44

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This paper cites Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

Reference 45

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2021 , eprint=

Reference 46

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning and Gardner, Matt

Reference 47

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning 2018 , eprint=

Reference 48

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This paper cites H ella S wag: Can a Machine Really Finish Your Sentence?.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning H ella S wag: Can a Machine Really Finish Your Sentence?

Reference 49

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 50

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning B ool Q : Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 51

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Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the Conference on Health, Inference, and Learning , pages =

Reference 52

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This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 53

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This paper cites Proceedings of the 2022 conference on empirical methods in natural language processing , pages=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the 2022 conference on empirical methods in natural language processing , pages=

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:11:02.917434Z

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Observation 222a1d89-64c8-40b8-97dc-ec19b59e88fe · outbound

This paper cites Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

Reference 55

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Observation ed7522e3-e34d-4adf-b026-b1d0bff25497 · outbound

This paper cites International conference on machine learning , pages=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning International conference on machine learning , pages=

Reference 56

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Observation ff6d394a-78c2-4a3f-83aa-5e5d3916c931 · outbound

This paper cites Advances in neural information processing systems , volume=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Advances in neural information processing systems , volume=

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.140289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a82404f-7951-4552-85b8-971ab7801c3e · outbound

This paper cites Advances in neural information processing systems , volume=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Advances in neural information processing systems , volume=

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:11:02.893010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 829e4c4a-7b91-425d-ab22-aaf48cd68d26 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Advances in Neural Information Processing Systems , volume=

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.147245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebbc110b-d718-43fd-9b49-163849057606 · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:11:02.874722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cbc188b6-4755-47e8-8a31-01a7ef8f67ec · outbound

This paper cites SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.153955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c3142d9-9c37-4bad-b1a6-fea1d8088ebc · outbound

This paper cites arXiv preprint arXiv:2603.21606 , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning arXiv preprint arXiv:2603.21606 , year=

Reference 62

Resolution: verified exact; raw_fallback, observed 2026-08-08T17:11:02.623407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e21ff3b-aaee-48db-b830-59de57f52ae3 · outbound

This paper cites arXiv preprint arXiv:2505.18738 , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning arXiv preprint arXiv:2505.18738 , year=

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.161150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37a8c7e3-a17d-40f7-b4ae-336e2dcde4b2 · outbound

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

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.164466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0628d89-d5e9-4bdf-846c-3e47b31f96a0 · outbound

This paper cites Proceedings of the 36th International Conference on Machine Learning , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the 36th International Conference on Machine Learning , year=

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:11:02.861214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a34dbec1-d63d-4b7c-a627-7db9e58f1e33 · outbound

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

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.172024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9929ffa-4503-4b52-8aa6-f7e6a9ef0135 · outbound

This paper cites Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , year=

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.176825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 801ec374-b896-46de-9b5f-e2fbe2c4e803 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning QLoRA: Efficient Finetuning of Quantized LLMs

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.181412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba074b6f-cdc0-4e93-bc70-8f533cff1713 · outbound

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

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.185238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd21da5b-ecb2-4f2f-a5a7-220bb84837e4 · outbound

This paper cites arXiv preprint arXiv:2308.10792 , year=.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning arXiv preprint arXiv:2308.10792 , year=

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.188994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cd40e0c-24eb-451e-9d74-b021d0a861be · outbound

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

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.192352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 613486fe-4142-4316-9292-2f82f01213ad · outbound

This paper cites LoRA-GA: Low-Rank Adaptation with Gradient Approximation.

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning LoRA-GA: Low-Rank Adaptation with Gradient Approximation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-08T17:11:02.195801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers