Paper Citation Record · LEDGER

AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2405.00361.

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

pith.paper-citation-record.v1
2405.00361 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:56:37.607680Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:39:56.499282Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 62b36667-f201-4fac-9ecb-9a8dc9cb548e · inbound

Mixture of Experts (MoE): A Big Data Perspective cites this paper.

Mixture of Experts (MoE): A Big Data Perspective AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 113

Resolution: unresolved; no resolver link, observed 2026-08-10T18:56:37.607680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c844aeeb-aa71-4810-8042-a3c5000156fa · inbound

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation cites this paper.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.133258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4be188eb-5aa5-431f-a93e-9809ba2f0f06 · inbound

Mixture of LoRA Experts with Multi-Modal and Multi-Granularity LLM Generative Error Correction for Accented Speech Recognition cites this paper.

Mixture of LoRA Experts with Multi-Modal and Multi-Granularity LLM Generative Error Correction for Accented Speech Recognition AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:17.661796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa14951d-eed7-4c3f-b580-faa416d73f57 · inbound

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging cites this paper.

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T19:55:06.802838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d87e4331-c0b1-4452-9514-4ef7f8620440 · inbound

Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression cites this paper.

Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T22:54:16.686552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e2f9cae8-2c42-4edc-a2f1-9fc1c27ad1cb · inbound

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation cites this paper.

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:40:58.597355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 201d6b47-e8c9-4622-bc41-a3795dfd337f · inbound

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation cites this paper.

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:53:51.958054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27078aa5-a8d7-467b-b5e4-3eebde39cdfd · inbound

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning cites this paper.

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:57:10.198747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc56ccfd-3443-438d-89bc-f97b5904e204 · inbound

LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective cites this paper.

LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:59:55.361362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 617ef432-407b-4de7-ac9c-67cb7b8ec609 · inbound

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models cites this paper.

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T15:39:56.500531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90066e4b-3895-4183-921e-c5929d93720a · inbound

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications cites this paper.

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T17:05:51.116990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 78fe5924-448e-483c-b9f7-6cc7df78761a · inbound

JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications cites this paper.

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T09:44:37.179334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.