Paper Citation Record · LEDGER

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2505.06272.

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

pith.paper-citation-record.v1
2505.06272 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:54:06.397550Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

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

measured 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

24 of 24 outbound references displayed

verified exact0
verified fuzzy5
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8b020e12-1a7d-498d-b022-64e6ceb25d78 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.298534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 224601d6-c2d5-49e2-ae69-dc0aa493390d · outbound

This paper cites GPT-4 Technical Report.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.303410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd17ed15-43dc-4fe4-a20c-9d2615429d52 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.307841Z

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Unavailable: canonical work link unavailable.

Observation 1025d71e-dfc2-4b80-8050-ec71bd224afa · outbound

This paper cites Clip-kd: An empirical study of clip model distillation,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Clip-kd: An empirical study of clip model distillation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.312633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4317133e-a589-4329-97bf-ec1ae4898ce9 · outbound

This paper cites Sketch-fusion: a gradient compression method with multi-layer fusion for communication-efficient distributed training,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Sketch-fusion: a gradient compression method with multi-layer fusion for communication-efficient distributed training,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:54:06.819235Z

Source-reported events for the cited work

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

Observation ba15e38a-dd70-4672-97a5-0a6002747367 · outbound

This paper cites etag: Class- incremental learning via embedding distillation and task-oriented gener- ation,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning etag: Class- incremental learning via embedding distillation and task-oriented gener- ation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:54:06.804816Z

Source-reported events for the cited work

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

Observation ebd80a52-f6f6-4403-8236-20f85abc1be0 · outbound

This paper cites Continual learning in the frequency domain,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Continual learning in the frequency domain,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:54:06.790413Z

Source-reported events for the cited work

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

Observation 5a9a781d-1d18-49e5-a1c2-3687f198a9cb · outbound

This paper cites Brain-inspired fast-and slow-update prompt tuning for few-shot class- incremental learning,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Brain-inspired fast-and slow-update prompt tuning for few-shot class- incremental learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:54:06.775898Z

Source-reported events for the cited work

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

Observation c3190910-9d1c-4840-b73a-3d4376e99d3c · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Lora: Low-rank adaptation of large language models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.331674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d559f8c-eab2-457c-a4da-ecc21898c3ef · outbound

This paper cites Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.335447Z

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Unavailable: canonical work link unavailable.

Observation a279cdee-9b6f-4b93-b89e-1ebc1e4840b2 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.339397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 292821c0-e19b-42c3-bb5e-bcf545c65e25 · outbound

This paper cites Higher Layers Need More LoRA Experts.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Higher Layers Need More LoRA Experts

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.344158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cad261c7-f611-4de7-acd1-e7c557852911 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.348538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddd9c0c5-4610-49bf-b40d-0e73ca575602 · outbound

This paper cites Sensitivity-aware visual parameter-efficient fine-tuning,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Sensitivity-aware visual parameter-efficient fine-tuning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.352882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a98d8bac-6be8-4c33-8f33-d18f61862c64 · outbound

This paper cites Hydralora: An asym- metric lora architecture for efficient fine-tuning,.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Hydralora: An asym- metric lora architecture for efficient fine-tuning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:54:06.742735Z

Source-reported events for the cited work

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

Observation 1e2efc2b-f6c1-4341-876e-7cd36263f504 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.361315Z

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Unavailable: canonical work link unavailable.

Observation fbd187d7-0cbb-405c-9204-0a5298d3ab20 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Piqa: Reasoning about physical commonsense in natural language,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.366193Z

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Unavailable: canonical work link unavailable.

Observation b6575f36-015a-4abb-a03f-50b8cd5f63a0 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning SocialIQA: Commonsense Reasoning about Social Interactions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.370540Z

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Unavailable: canonical work link unavailable.

Observation 22e6cdaf-8f84-44f8-8508-1da2d4b5cdcb · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Winogrande: An adversarial winograd schema challenge at scale,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.375349Z

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Observation 16511ad2-cae2-4105-ae8f-a6f432b7f227 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.379644Z

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Observation c39f698c-003d-45bd-84f5-412f9b710d87 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.384115Z

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Unavailable: canonical work link unavailable.

Observation f3bc0404-e330-4336-a14c-cfa477b39de9 · outbound

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

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.388872Z

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Observation 9c101713-5917-4a00-abb0-f1eef8f2b204 · outbound

This paper cites Qwen2.5 Technical Report.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Qwen2.5 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.393240Z

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Unavailable: canonical work link unavailable.

Observation ff9e0d81-dc4d-43cf-90f7-f0e92bccdc51 · outbound

This paper cites Decoupled Weight Decay Regularization.

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning Decoupled Weight Decay Regularization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T23:54:06.397550Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.