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Paper Citation Record · LEDGER

MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.09027.

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

pith.paper-citation-record.v1
2404.09027 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:40:41.241352Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T22:08:58.535869Z

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 42c9a92c-7ec6-4e13-a6d9-717f79cacef9 · inbound

DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment cites this paper.

DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T12:40:41.241352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T12:40:41.241352Z digest=sha256:696801a20462c64fcca2bf455cf3e63308c61d3cd7cf668e2944c21fc93d10ff

Observation c9615f0f-88ae-4afe-971d-e7be3d599e5d · inbound

TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs cites this paper.

TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-30T07:44:21.917608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T07:40:21.132616Z digest=sha256:e918613d8b231f14b91fabbdf59bfe86376f13a8ada02cc12048b077ab239340

Observation 6de9e6d2-4e19-4c69-93e4-27bda698a138 · inbound

Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering cites this paper.

Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-01T10:05:40.989812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-01T05:53:55.141657Z digest=sha256:f6ef6d2b7a19ff536c194a555881ed0668925a5ae14a9103aeebf0669009b2a4

Observation 0da0d6d0-6382-46c1-8f7c-3678cbacbd97 · inbound

Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering cites this paper.

Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:57:19.017830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-02T19:53:25.252752Z digest=sha256:a258289d6a92131224850b2c426a982a5a257ea74248653a51d88c999f45d970

Observation 2bb8cf28-0710-4395-b1ef-bace14850d3c · inbound

Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering cites this paper.

Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-03T22:08:58.538547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-03T22:08:53.025783Z digest=sha256:75be4644fcc6b0cf64f7f053d9d63d94aa7c5c268de2557964433b42f2d7926c