Paper Citation Record · LEDGER

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.07140.

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

pith.paper-citation-record.v1
2507.07140 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:07:37.681809Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

verified exact2
verified fuzzy8
unresolved37
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f9d611b4-073d-4b41-b586-b39883496d2a · outbound

This paper cites Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:32.674276Z

Source-reported events for the cited work

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Observation 873f1282-6e97-4d27-a97a-19a5480acb26 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:32.743441Z

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Unavailable: canonical work link unavailable.

Observation a42f5d32-6a7d-4e88-949d-d0a5587b942e · outbound

This paper cites Evolutionary Optimization of Model Merging Recipes.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Evolutionary Optimization of Model Merging Recipes

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:32.863487Z

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Observation f44d5d83-0698-4983-b47a-901f446f1a26 · outbound

This paper cites Composable sparse fine-tuning for cross-lingual transfer.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Composable sparse fine-tuning for cross-lingual transfer

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:39.769681Z

Source-reported events for the cited work

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Observation 0a4aeed2-eebd-48aa-bb2f-dd851460e72e · outbound

This paper cites Scaling Sparse Fine-Tuning to Large Language Models.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Scaling Sparse Fine-Tuning to Large Language Models

Reference 5

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Source-reported events for the cited work

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Observation 36385d74-c133-4369-81ba-6fef5e81fe05 · outbound

This paper cites Single-Shot Pruning for Offline Reinforcement Learning.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Single-Shot Pruning for Offline Reinforcement Learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:07:38.097396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d175b7d-4c45-4c75-88ea-fad93b4ac589 · outbound

This paper cites Efficient reinforcement learning by discovering neural pathways.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Efficient reinforcement learning by discovering neural pathways

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:39.559979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0a200b22-e91f-44ed-bfdd-2ef043505fa8 · outbound

This paper cites Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:33.590533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89154af1-fb03-4652-bc71-4af87a22ee19 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Qlora: Efficient finetuning of quantized llms

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:33.744974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01b6fea3-c96b-42cc-8038-fa1ffd9faca2 · outbound

This paper cites Rigging the Lottery: Making All Tickets Winners.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Rigging the Lottery: Making All Tickets Winners

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:33.894102Z

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Observation 83882ea9-f38f-4f9b-bca8-e41c350caf86 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:34.082327Z

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Unavailable: canonical work link unavailable.

Observation 617e4b26-cff8-42b1-a678-748c1759ce21 · outbound

This paper cites Megablocks: Efficient sparse training with mixture-of-experts.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Megablocks: Efficient sparse training with mixture-of-experts

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:39.250152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 519cc976-3acb-4590-8975-e6daab49537a · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:34.323945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb63afb6-739a-42e3-b842-07325d5ccdd1 · outbound

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

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:34.472679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 625ccb9f-17b6-4c3b-bf11-0e810c029c37 · outbound

This paper cites SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:34.638624Z

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Unavailable: canonical work link unavailable.

Observation 98a45260-4bb8-44e8-9fad-257b6f4218ad · outbound

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

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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Source-reported events for the cited work

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Observation c8f65f69-f06c-48e7-ab45-8b75940b8964 · outbound

This paper cites LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:07:37.936654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88e4e900-82aa-4c00-815d-1a88128a8f51 · outbound

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

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bd7a445-bd8c-48f8-be62-dd6a9e60ae37 · outbound

This paper cites Editing Models with Task Arithmetic.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Editing Models with Task Arithmetic

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.237210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab92cd52-95cd-4d80-b70d-75f0b44cf24d · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 22

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Source-reported events for the cited work

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Observation 830802d7-d547-4ad1-924b-a2d151df5b4a · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc78394e-25e5-41a8-a878-a3b35ea1801f · outbound

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

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.593691Z

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Observation 257409a9-3286-4c5f-907f-8f7083e32b2c · outbound

This paper cites Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.695120Z

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Unavailable: canonical work link unavailable.

Observation 307412f8-9ec6-4604-949b-9008ea4e6bba · outbound

This paper cites The Flan Collection: Designing Data and Methods for Effective Instruction Tuning.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:35.794165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78c09d55-878c-42a8-8687-8b6c916a760e · outbound

This paper cites Merging Models with Fisher-Weighted Averaging.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Merging Models with Fisher-Weighted Averaging

Reference 27

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Observation 9f34d01b-83f9-4c29-8a5a-83aa9597af52 · outbound

This paper cites Merging models with fisher-weighted averaging.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Merging models with fisher-weighted averaging

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:39.040806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4bfd8cdd-3df4-4e9f-aca3-5eda233f5b46 · outbound

This paper cites Skeletonization: A technique for trimming the fat from a network via relevance assessment.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Skeletonization: A technique for trimming the fat from a network via relevance assessment

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:38.871794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4983760f-3925-4083-a3e1-6006985844dd · outbound

This paper cites Learning to Route Among Specialized Experts for Zero-Shot Generalization.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Learning to Route Among Specialized Experts for Zero-Shot Generalization

Reference 30

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Observation 776300d9-f182-427f-8620-b207e5d9aa6b · outbound

This paper cites Towards Modular LLMs by Building and Reusing a Library of LoRAs.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Towards Modular LLMs by Building and Reusing a Library of LoRAs

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d36ed58d-7f22-42d9-8b79-14e400ec324c · outbound

This paper cites Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs

Reference 32

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Observation d67e5d15-9a07-4467-a930-2bacbae67ced · outbound

This paper cites LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks

Reference 33

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Observation e641931a-8c7e-4af0-a8a9-a0e1545a3be8 · outbound

This paper cites Controlling text-to-image diffusion by orthogonal finetuning.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Controlling text-to-image diffusion by orthogonal finetuning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:38.546969Z

Source-reported events for the cited work

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Observation 989c74ad-b698-4d7d-94bc-83483ee7b220 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 35

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Observation 35dbe9c3-5361-4e5d-805c-286c03ba797a · outbound

This paper cites Overcoming catastrophic forgetting with hard attention to the task.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Overcoming catastrophic forgetting with hard attention to the task

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:38.383453Z

Source-reported events for the cited work

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Observation 78c19b63-ec63-4cc5-b92e-c26be8272c4f · outbound

This paper cites In defense of structural sparse adapters for concurrent llm serving.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts In defense of structural sparse adapters for concurrent llm serving

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:07:38.249909Z

Source-reported events for the cited work

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Observation d9b71dcf-fa57-483c-9ff7-06ddaa2c2f24 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 38

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Observation bfd30180-3b91-4856-86ee-e03327149355 · outbound

This paper cites Sampling Generative Networks.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Sampling Generative Networks

Reference 39

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Observation c0f772ff-fac1-4aef-8a0b-cbd2781dd05e · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:37.115621Z

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Observation b2e3f8ca-f941-44e3-aa8b-fc336d3312d8 · outbound

This paper cites TIES-Merging: Resolving Interference When Merging Models.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts TIES-Merging: Resolving Interference When Merging Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:37.170618Z

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Observation a49c98b7-3b4b-404a-9598-85c85b8697e7 · outbound

This paper cites A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:37.247810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50f6427a-6a53-420d-a4f3-e1483cba258f · outbound

This paper cites What Matters for Model Merging at Scale?.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts What Matters for Model Merging at Scale?

Reference 43

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Source-reported events for the cited work

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Observation b5e36ff9-2e9a-47f3-9c58-af6506c4c686 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:37.383490Z

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Observation 95ac8e9d-fa23-4d8e-a8a8-f2e31c924a3e · outbound

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

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 45

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Observation cf925502-7073-4148-bf15-b5970342807c · outbound

This paper cites write newline.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts write newline

Reference 46

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Source-reported events for the cited work

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Observation d6737e9d-bfc4-44ee-a718-eb5c31a0a94b · outbound

This paper cites @esa (Ref.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts @esa (Ref

Reference 47

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Observation e875e3c5-309c-4a6c-8d57-415d2cdd90e7 · outbound

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Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Unresolved cited work

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:37.627106Z

Source-reported events for the cited work

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Observation f813b2b1-a548-412e-8891-fffe12749886 · outbound

This paper cites an unresolved cited work.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:37.681809Z

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Pith citing papers

No inbound Pith citation observations are available.