Paper Citation Record · LEDGER

MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.03004.

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

pith.paper-citation-record.v1
2502.03004 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:17:06.057653Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T18:57:31.594048Z

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 bb52f53e-7d87-40fe-87d1-ad33245f5aa5 · inbound

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design cites this paper.

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:30:49.332885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ddb8633a-92e1-426d-a5bc-558bc4c58c62 · inbound

CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation cites this paper.

CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:35:52.399458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c8214106-188a-41e0-98eb-b0a94d3b5b42 · inbound

AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports cites this paper.

AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T12:32:10.821189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 851b6855-5018-4f0a-9faf-41d2d49c337f · inbound

What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA cites this paper.

What Makes a Medical Checker Trainable? Diagnosing Signal Collapse and Reward Hacking in Checker-Guided RAG for Biomedical QA MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:33:58.807945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a836b743-a870-4f67-818f-5a3a141cdc4c · inbound

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning cites this paper.

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 110

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T18:57:31.595370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b4428f5-2a7e-4f80-9b25-c3d81ea6ee04 · inbound

When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification cites this paper.

When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T00:17:06.057653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.