Pith. sign in

Paper Citation Record · LEDGER

Can LLMs' Tuning Methods Work in Medical Multimodal Domain?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.06407.

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

pith.paper-citation-record.v1
2403.06407 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:56:17.342793Z

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

0
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 d8532c49-fb9f-4f03-b318-d2ba7df6684b · inbound

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions Can LLMs' Tuning Methods Work in Medical Multimodal Domain?

Reference 116

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:55:49.920793Z

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=2026-05-23T21:54:26.670284Z digest=sha256:02c5fd0d3b74ae117a3d32f56a6f111d6522af66367478b1b54b20807c8f76af

Observation c8fbd863-3275-45cd-a98b-fa2ddea684b4 · inbound

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials cites this paper.

Generative Artificial Intelligence Extracts Structure-Function Relationships from Plants for New Materials Can LLMs' Tuning Methods Work in Medical Multimodal Domain?

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T22:56:17.342793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:56:17.342793Z digest=sha256:a2ef9530ecb62136aec8871c94a4b1e6166c21a7e06137aa60812c6190328ffd