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Paper Citation Record · LEDGER

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts

As of 8 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2506.00495.

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

pith.paper-citation-record.v1
2506.00495 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:09:16.082796Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:01:46.458539Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:51:07.429970Z

Reference resolution

52 of 52 outbound references displayed

  • verified exact2
  • verified fuzzy14
  • unresolved35
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7683bbbb-542d-4a21-99f3-248b5425294d · outbound

This paper cites Optuna: A next- generation hyperparameter optimization framework.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Optuna: A next- generation hyperparameter optimization framework

Reference 1

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raw_fallback, observed 2026-08-07T12:09:20.579934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:11.634049Z digest=sha256:6741bce9ad8210791ec122bdd22fdffcbd51f39c6b95e96579fdc1b543dabc48

Observation 0ad25361-6050-4fc0-9571-b5c1b08b8c91 · outbound

This paper cites Lawyer-instruct, 2024.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Lawyer-instruct, 2024

Reference 2

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raw_fallback, observed 2026-08-07T12:09:20.398749Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:11.693806Z digest=sha256:d79f24b8300fc112e279f5da02a53551288950dd4ffade4cebe38f7fc71d4072

Observation 1ef382ed-9a9a-40cb-b28a-92d33167cddd · outbound

This paper cites LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development

Reference 3

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local_arxiv, observed 2026-08-07T12:09:17.823694Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:11.782813Z digest=sha256:444927c3c05f728cd7e96b159086ad072953c23b113042206af45fe71d59d59e

Observation c6c3ea2a-3dc2-47b0-84d3-79e2ede3326e · outbound

This paper cites Code alpaca: An instruction-following llama model for code generation.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Code alpaca: An instruction-following llama model for code generation

Reference 4

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raw_fallback, observed 2026-08-07T12:09:20.209464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:11.847185Z digest=sha256:14e4a44f6bb93cd8b6249dc414266cca98746c1b5bbb7e503893200e13503c0e

Observation b66aaec8-d160-43ba-bfb3-e2c5a3e8e6e6 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Evaluating Large Language Models Trained on Code

Reference 5

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:11.903523Z digest=sha256:dd9d14309812a27fd3e6c7a08eb2f08a99297b74cecead39d78f8690342093a9

Observation 2d0ef40b-9c6f-4ca2-9015-ee2215dd5902 · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 6

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source=pdf_text observed=2026-08-07T12:09:12.021487Z digest=sha256:575b21fffab970d275bb5279c408013b4a80d95f35a063ba68f76d4240a71145

Observation c09ad824-72cd-46f1-aca0-212920c393dc · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Training Verifiers to Solve Math Word Problems

Reference 7

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source=pdf_text observed=2026-08-07T12:09:12.137416Z digest=sha256:50b1dca2edc9023b003c840929da215052ae5a7b8d61dad921718a67fa977706

Observation ea3c9c56-9ebb-4ecf-9443-872ff52aae0f · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Sparse Low-rank Adaptation of Pre-trained Language Models

Reference 8

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source=pdf_text observed=2026-08-07T12:09:12.204135Z digest=sha256:7d7136161472f16c5af685bc365fc8ee2d88fb08af0bfe2e5854a19b432c83d2

Observation 82c30370-8b25-41b2-81ec-59f267d1155f · outbound

This paper cites LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Reference 9

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source=pdf_text observed=2026-08-07T12:09:12.260399Z digest=sha256:57eb2e3a22629e9cd2e8c2446ffc8e086f2437e7ee93e731e4900fe756e2a754

Observation 0113ec4d-12cc-47a3-a9ee-8541f257414a · outbound

This paper cites LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Reference 10

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source=pdf_text observed=2026-08-07T12:09:12.308512Z digest=sha256:833966ef780a7639ea3a2188179434e119e5597f4c598304100f6c0117a77d09

Observation 13e82321-11c0-4552-aca1-724394c2d4d1 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 11

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raw_fallback, observed 2026-08-07T12:09:19.998930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:12.426724Z digest=sha256:8b52decc08fc401fb6f7cc1c08f54c26a3ba9c2b7c3ce754b074506ea3fc576c

Observation 99355fc6-1aec-49fa-933d-c834be2ef5b3 · outbound

This paper cites MoLA: MoE LoRA with layer-wise expert allocation.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts MoLA: MoE LoRA with layer-wise expert allocation

Reference 12

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raw_fallback, observed 2026-08-07T12:09:19.761730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:12.548266Z digest=sha256:f137a5ae36ccfc6c72bafa61a49a8b409e916d9672f42345f5fe889a9a69e096

Observation a7539167-998f-4850-97f6-2d71c6791861 · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 13

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source=pdf_text observed=2026-08-07T12:09:12.683982Z digest=sha256:006e09cef308186411b5e560d3d6e34229356e34b130f51b775ceda3645168cd

Observation 35191f7c-ee29-4acb-b4d7-5ac735ccebf7 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Measuring Massive Multitask Language Understanding

Reference 14

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source=pdf_text observed=2026-08-07T12:09:12.747324Z digest=sha256:e147b978c35968f80ca8fa2a5b11f0e51111fcbffabfec4f42d96871c2e80377

Observation 65d3186f-8a03-4440-a8e6-a798f8ac1388 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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source=pdf_text observed=2026-08-07T12:09:12.828043Z digest=sha256:1a8e74a2fd7ecad12c267ca6652462a1ea3097647e48b2535318828d99938d1c

Observation 59efa1c4-7256-4603-85e4-1dddbf36c5c0 · outbound

This paper cites LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 16

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source=pdf_text observed=2026-08-07T12:09:12.947347Z digest=sha256:daa158a31e56e1c69455fb3568814dcf48bca0c7ece4ec63e021ee45218cb113

Observation 6ff322d2-0b8e-4641-885d-63f033c2b1e1 · outbound

This paper cites Adaptive mixtures of local experts.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Adaptive mixtures of local experts

Reference 17

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source=pdf_text observed=2026-08-07T12:09:13.039183Z digest=sha256:83572dbbbeb47396b88b1a39c40cdfd281835d8e98f43d2a728d045c3c247eca

Observation 8d115b63-0b99-4016-91a5-b56091d284de · outbound

This paper cites Identifying and mitigating vulnerabilities in llm-integrated applications.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Identifying and mitigating vulnerabilities in llm-integrated applications

Reference 18

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raw_fallback, observed 2026-08-07T12:09:19.538346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:13.137198Z digest=sha256:3adfbf147f196f451190978a6a9d8132967c90be3731d7d0868d65a0c0273927

Observation 15d9fede-8697-47c9-b17f-cd54e020620f · outbound

This paper cites MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Reference 19

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source=pdf_text observed=2026-08-07T12:09:13.194756Z digest=sha256:99efc639c1533d3c72ea11ed931dc6c38320e04fc60879d41704ab765c44185b

Observation 7073d266-c72e-4a0b-999c-6545f0ca91c1 · outbound

This paper cites Less is More: Selective Layer Finetuning with SubTuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Less is More: Selective Layer Finetuning with SubTuning

Reference 20

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source=pdf_text observed=2026-08-07T12:09:13.253965Z digest=sha256:a275a31a1637fba15ba6f9166e50e59bf77b8e899aab7eecd72726075812735a

Observation 476400f6-a7f8-4adc-94f1-13ad6cadf667 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts VeRA: Vector-based Random Matrix Adaptation

Reference 21

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source=pdf_text observed=2026-08-07T12:09:13.327298Z digest=sha256:76d2d10624c1fc6872d307e129b7ed61c63b370f4b3fd7704aaf079c6c36a604

Observation e4011ca0-94cc-44f0-af08-36b777fe1ee2 · outbound

This paper cites Optimal brain damage.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Optimal brain damage

Reference 22

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source=pdf_text observed=2026-08-07T12:09:13.415948Z digest=sha256:70821055608bcec4f4763d6ff2d856665c192c0c1481444ec698bef4d3cb725d

Observation 7424c8fc-b454-4235-843c-f4c8b92703db · outbound

This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 23

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source=pdf_text observed=2026-08-07T12:09:13.508685Z digest=sha256:c87d263d8542a3f0db92e2012b92dbd24396167fde6b791e216a5d9674c3c969

Observation 267a91a9-8fc1-4630-b922-1050960b6381 · outbound

This paper cites Conditional adapters: Parameter-efficient transfer learning with fast inference.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Conditional adapters: Parameter-efficient transfer learning with fast inference

Reference 24

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raw_fallback, observed 2026-08-07T12:09:19.390243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:13.599639Z digest=sha256:f63f97de12fae9f9cdd86c1205698f781b1f13ce7a4827dc0c92b356aad3e7e5

Observation 8ac650bd-c276-4e7d-bf25-c907bf2df1a4 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 25

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source=pdf_text observed=2026-08-07T12:09:13.680914Z digest=sha256:e69f7883a0275d75facfe6a91d3d0a0f93ee5b8c1e2c23216e8fa5a55eada8af

Observation e75d4cba-4d09-4444-882b-d743e9294843 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 26

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source=pdf_text observed=2026-08-07T12:09:13.790010Z digest=sha256:622a6b42724ce1984c20ea61baefc66e2bd4d3792096dad101f6e55e4c513ae2

Observation f50f1ee1-0d78-4e8a-98ff-fce668dbe521 · outbound

This paper cites Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge

Reference 27

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source=pdf_text observed=2026-08-07T12:09:13.856626Z digest=sha256:66a395bc42a4c7eb90045535ca75fb0aa7434cbb21a81ecbaeb6e7590f706d4d

Observation 463410fe-f299-4cda-b946-f65651bcc183 · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 28

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source=pdf_text observed=2026-08-07T12:09:13.966715Z digest=sha256:ad6ee0fe9c4f0a28c75f97db1b318a8f2f5fd46555629fef90128ba4a49f4d03

Observation 7720f447-ffda-46b3-8793-79ddcecee064 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 29

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source=pdf_text observed=2026-08-07T12:09:14.018675Z digest=sha256:273df80843be3792675f2c5c6211b83141d1fdca8df5ff912d5e9ac6beda16a6

Observation 63c044d9-1594-43df-a25e-72eaea3b12f8 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 30

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source=pdf_text observed=2026-08-07T12:09:14.108874Z digest=sha256:3421dc0305bb913bc3664904915a90794ab735df7935014d747ccd806414732a

Observation bc2d7b63-5914-4398-b325-88cd06979c10 · outbound

This paper cites Gpt understands, too.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Gpt understands, too

Reference 31

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source=pdf_text observed=2026-08-07T12:09:14.194630Z digest=sha256:d01c5cd1fed25e55d0aa8ad46b2b29e23b49cf63e9228e5d6c51d25852126bf3

Observation eebf33fb-65d8-42ba-a3b5-b570debd78e9 · outbound

This paper cites ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

Reference 32

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source=pdf_text observed=2026-08-07T12:09:14.299953Z digest=sha256:af21603c4da8d4c6f6831e1f4a4e87db94786db77458008f35cb3d736c774e80

Observation 9cb5dd07-0a0e-4f7c-b738-0b78eb777066 · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts The flan collection: Designing data and methods for effective instruction tuning

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-07T12:09:19.120563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:14.426059Z digest=sha256:8e67fdbfe6963fe60695aa3fde97936b67cf5b435c0da88445d185cfdbe85b7d

Observation 6bce41b6-6fd3-42ea-b72f-9ccd78d319ae · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Llm-pruner: On the structural pruning of large language models

Reference 34

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raw_fallback, observed 2026-08-07T12:09:18.911647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:14.492378Z digest=sha256:101d6790451d79fd4725f838a00098a627d965e8f9da54c78a3b0da4ae1b9496

Observation d99fac71-411c-43cb-ab37-0d7cb3741954 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:14.600815Z digest=sha256:96c772ddb1caa4b42d00b7c1fe9fe7bdd3d6efa321df75eac685839f91ebf16a

Observation 7b01ed15-55f5-4760-930e-2c6724856a38 · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

Reference 36

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raw_fallback, observed 2026-08-07T12:09:18.721469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:14.697383Z digest=sha256:5ea22942c85408166b5a3681de6f539a0c15405c3a8535eb49c04026413720aa

Observation 9b2ca90e-7073-4e19-bd7d-a2da2c2c71a6 · outbound

This paper cites Multi-head adapter routing for cross-task generalization.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Multi-head adapter routing for cross-task generalization

Reference 37

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raw_fallback, observed 2026-08-07T12:09:18.521590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:14.739016Z digest=sha256:9ce529711ce0977374d84ee53de0ead1e370157df7a5b4ddd7eadb157074bf26

Observation 4582706e-1f12-4388-9402-9a06cb53819c · outbound

This paper cites LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

Reference 38

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:14.828645Z digest=sha256:73eddf97328bc7ae5926a4c10cb840d787e530215d921f1c8e5ce837c15a1e73

Observation 4f3e6791-b887-4ff9-9fb4-2a6882427481 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying

Reference 39

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source=pdf_text observed=2026-08-07T12:09:14.911036Z digest=sha256:065fc1820a85111ef55cb89347fdb89a207c4b38f531f3e052bcf60206c2a1f5

Observation 74c9f7c5-94c6-444c-bb2b-412accc9cf35 · outbound

This paper cites On the effect of dropping layers of pre-trained transformer models.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts On the effect of dropping layers of pre-trained transformer models

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:09:18.356371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:14.988464Z digest=sha256:8a3df9489e518231a26c40a1562320f0c1b0910848a0a8989db3e37dfd68f47a

Observation e8c1a75d-c3e5-43c2-8d75-b59da1c84211 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 41

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no resolver link, observed 2026-08-07T12:09:15.073697Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:15.073697Z digest=sha256:cbf0b993e621b6f81fc3821cd14963bfdb1674991ce8d9f56d47e3457bee033f

Observation 538c6522-9b89-4391-8dac-dcca1108c594 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 42

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no resolver link, observed 2026-08-07T12:09:15.147892Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:15.147892Z digest=sha256:dc7377caec58549030624c03569477a4a798d517ea383d92438cb81b1405fbaa

Observation 6ad5e942-e6d5-46a9-a548-b12eff57d5fa · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Gemma 2: Improving Open Language Models at a Practical Size

Reference 43

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no resolver link, observed 2026-08-07T12:09:15.248008Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:15.248008Z digest=sha256:9551e8045e286f5f0ce152164aa254e3292e10b57f81fd6adbd7eed898d52730

Observation 482b0006-63b8-4908-af19-71a8acc16119 · outbound

This paper cites HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:15.315090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:15.315090Z digest=sha256:d052985cf54f2c25850802c8e8555e14538d841a5bfa2b5531e7ceac9adae431

Observation 61b5f1ca-7afa-479a-87cf-bedd8617bc80 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:15.426608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:15.426608Z digest=sha256:5f9e5db1df87163264a382a3f2a6249991d757aff4fe16af456c3ae54d75a34b

Observation d672f9e3-34e0-439c-90d1-0522e1fd63e4 · outbound

This paper cites DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T12:09:15.530983Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:15.530983Z digest=sha256:5a3dc7763c3781e0cd3dafe15305be4c4477f3f89546739d62192ddb9499b7e8

Observation 7de5f910-32d8-4ab3-8de0-cd873ccf9534 · outbound

This paper cites Eigendamage: Structured pruning in the kronecker-factored eigenbasis.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Eigendamage: Structured pruning in the kronecker-factored eigenbasis

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:09:18.206008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:15.640336Z digest=sha256:af732dacbbc7013f99cb3e7e3c7ced273bb928ea45491d53ce39cab2010bfde9

Observation dab70a53-010a-4440-aa7a-6607e60ae327 · outbound

This paper cites AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

Reference 48

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no resolver link, observed 2026-08-07T12:09:15.732927Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:15.732927Z digest=sha256:90984a3fcd5c23bbc7f0422c26ccd74599383da6ab6fb5b742510bb1e1aa545a

Observation f43b12e6-3060-43a9-ad56-6a47d15013d5 · outbound

This paper cites Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:09:18.033356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:15.809137Z digest=sha256:7591895914551afd1aae6300e55693a7ed57f2ceedc4b4c60b612f8ddec64653

Observation b202b104-fc60-40bd-ac11-596a10c6a66a · outbound

This paper cites CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:09:16.407359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:09:15.900942Z digest=sha256:78c79390862dbe67e6feee21e389e3e10a81fbb01f8358fab81708e01252befe

Observation 6f6c7e3b-f613-4e7b-aa57-f37df9495d79 · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 51

Resolution
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no resolver link, observed 2026-08-07T12:09:15.974806Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T12:09:15.974806Z digest=sha256:ba9bade123dba6047e22d748feba019be7ce4531758cfdc466a65fb8ae014959

Observation f82c49eb-4f3b-4c9e-89a4-ce18d4b775f1 · outbound

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

FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 52

Resolution
malformed identifier
no resolver link, observed 2026-08-07T12:09:16.082796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:09:16.082796Z digest=sha256:6f608c506d594169930f592ddb30b2d421b747eb020649c378c921e8a325885a

Pith citing papers

Observation cf0213bb-963b-4605-99a1-ce6dd3a2fdce · inbound

One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning cites this paper.

One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-11T18:51:07.433669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T13:40:50.908566Z digest=sha256:57b2a41c5f14cc4b1d92e3b1bfec5ac95d49d543be20ec057bf58451305aacca

Observation 5d6a5c94-e727-46c0-9243-badeb91d54df · inbound

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning cites this paper.

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts

Reference 9

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unresolved
no resolver link, observed 2026-08-02T09:01:46.458539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T09:01:46.458539Z digest=sha256:461bfc2e2022b5610fbc182cc73a6ae79b1958f7e514cb775327dd90956504a9