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Paper Citation Record · LEDGER

Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2305.14705.

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

pith.paper-citation-record.v1
2305.14705 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:36:24.751346Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

20
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6874725e-f5b4-4b49-a59c-ee7b452f77f4 · inbound

A Survey on Multimodal Large Language Models cites this paper.

A Survey on Multimodal Large Language Models Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 67

Resolution
verified exact
arxiv_id, observed 2026-05-16T02:56:42.536369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T02:56:41.658658Z digest=sha256:8eb08f9f9fd55b41b72d29b2a1a28b1edf2ec248ff528a67062341fdc73a3b2f

Observation 21e857c4-df1f-49e1-bff8-ac8d1c221c1c · inbound

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models cites this paper.

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:50:07.408393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-11T22:50:06.399707Z digest=sha256:62f8770edac1f2db5be1c2d737c2ebf00a008b3c366e09b5978fe6c9ca21c686

Observation 144451d9-da79-4b3d-b5b1-e461984fbaae · inbound

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning cites this paper.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-08T13:36:24.751346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T13:36:24.751346Z digest=sha256:46924cd38a27b526c1605b31b84968738f0e4a290983c17aaefe09eba546219e

Observation a9c7c309-7663-46b6-8347-d76a8358fc00 · inbound

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis cites this paper.

Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:27.914720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:27.914720Z digest=sha256:96d732a89ba58574d3e19c7fcac0d7a948cf51d16dfb7584ad9e36a2f0ee8d66

Observation 0d748abb-7b20-4f1a-8bb2-014fa264841b · inbound

APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training cites this paper.

APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:11.236058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:07:11.236058Z digest=sha256:865b47487995749c51d719f8a9cb8f6cc85eaa479fb669511930783a5f2eb71a

Observation a5d4eb8e-f8e3-4c3e-b53c-4d4bda061ca5 · inbound

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models cites this paper.

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:55:59.323075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T17:23:40.872418Z digest=sha256:ecfd3a055bd2ff54aff833582704e5ed2d07b89fb1c245320b35c81f6ba0f799

Observation 6f66e3b0-76c4-4daa-b866-a951894e277c · inbound

AlignCultura: Towards Culturally Aligned Large Language Models? cites this paper.

AlignCultura: Towards Culturally Aligned Large Language Models? Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:56:04.781500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T02:36:36.854805Z digest=sha256:7a58ac32289c02779add46f183a1007704be1fe47e26b68ca6b9f8dabdaaffba

Observation 2044f7f2-f18b-4461-a0ec-af99e0563afb · 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 Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 49

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T15:39:56.554064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T01:32:40.435742Z digest=sha256:55ab7145e4ec672d2e7591c7dd8629f4232a67db03251177043b3daca97b5b2b