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Paper Citation Record · LEDGER

Baichuan-M1: Pushing the Medical Capability of Large Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2502.12671.

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

pith.paper-citation-record.v1
2502.12671 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:24.011816Z

measured 1 of 1 external citation measurements

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

Source: pith, 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

5
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4c32e658-c443-46e1-aeed-e7fb5d426ae3 · inbound

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models cites this paper.

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:24.011816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:24.011816Z digest=sha256:27e3efb8e6d8f88414939bdeaea5c5efe0d3b8d7746e643beee5b7f9eb197196

Observation 24bfe449-e53f-4e0d-aec2-224d75561c5e · inbound

Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making cites this paper.

Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T13:33:41.740417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:33:41.740417Z digest=sha256:1b41b98381fb8bb438c9054f78cd53eb2ef7f0255866ac430b870e6e2a08916a

Observation cc62cba1-e802-4c56-b8a4-ee65af2ba0d0 · inbound

MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine cites this paper.

MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T11:50:54.521576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:50:54.521576Z digest=sha256:887b3f19f36a86a0ebf2cea89e2a3bbf1801b2441df771ad4732ee8b4f149bb9

Observation 6b767e79-e982-48ca-88df-c7d3b82bbd83 · inbound

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following cites this paper.

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T04:43:21.144848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:43:21.144848Z digest=sha256:63ea7aca8f284d5805e26a613f224c68142142f8cc93edf25fb879b63fdf8a83

Observation 00bfc95d-4598-4802-a203-d9aea1a245c1 · inbound

MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine cites this paper.

MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T04:50:45.273861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:50:45.273861Z digest=sha256:7b0bbf27d443bbcc0db7abaeabe7f3240b8832ab35c57fa637a042a621808759

Observation a394206b-0e39-40f7-87ad-60d348ebc3e9 · inbound

CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learning cites this paper.

CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learning Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T11:00:49.851196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:00:49.851196Z digest=sha256:3ab76c5b116bd81a2f2d0a649ec282dd4f866d43328c6a5d75edca89375b8804

Observation f44404ee-5c10-44a5-ab6e-c51efd405a1d · inbound

Baichuan-M2: Scaling Medical Capability with Large Verifier System cites this paper.

Baichuan-M2: Scaling Medical Capability with Large Verifier System Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-05T11:50:14.873675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:50:14.873675Z digest=sha256:5a785b8f43da67f3b8eeb2d06c967fb4e9affb80ff6dd428d5285218a3100ba5

Observation 9535c2f1-5b06-4b78-abc4-f7f02777645e · inbound

A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.

A Survey of Reinforcement Learning for Large Reasoning Models Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 220

Resolution
verified exact
arxiv_id, observed 2026-05-18T00:05:31.561021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-18T00:02:24.352947Z digest=sha256:6b362372334ba768bf0afa1b324fcad90e29371a6033b2ef2c452b4abbd708a7

Observation c55990dd-9a5b-42a4-8dcf-83f9fbe71f54 · inbound

Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm cites this paper.

Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-04T14:44:39.089985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T14:44:39.089985Z digest=sha256:20a8f646f2068fdfdfffbad5c99c0125eb45cbf1c0d111a5d9732fa47a3ef7a3

Observation 585dcfb9-e992-4cfd-8698-4b40685751e2 · inbound

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning cites this paper.

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-16T10:57:46.891766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-16T10:54:22.183741Z digest=sha256:ee33e22d34eae00f68ab75be8661b03f13b6ecc0f07fd2db27d03645d0cd0544

Observation da414913-740e-464d-849d-da37cbc2a45b · inbound

Medical Reasoning with Large Language Models: A Survey and MR-Bench cites this paper.

Medical Reasoning with Large Language Models: A Survey and MR-Bench Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-15T10:25:26.728672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-15T10:21:39.892271Z digest=sha256:759a621de361e28606487e2cec25c3317ce6180ae7116ad966dbc4ad306011c2

Observation f38b78ed-2885-47fc-ae34-a7f13f8d2187 · inbound

ReMedi: Reasoner for Medical Clinical Prediction cites this paper.

ReMedi: Reasoner for Medical Clinical Prediction Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 47

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:01:05.957725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-09T14:20:29.672994Z digest=sha256:4feaa59e82e9b30d8f6b6675659f709abfb6c3e6c409a376a51e23cc6bc957ff

Observation ab353cb3-1a55-468d-913c-5a3e4140a5c9 · inbound

Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support cites this paper.

Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-22T06:21:09.827940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-22T06:21:06.821499Z digest=sha256:745b7c3110caca53666e16fa333748d063b1138800386d5d5a459f9953945752

Observation 0473e41a-7228-4f95-9922-bb1f39554082 · inbound

Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care cites this paper.

Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-27T17:01:07.405226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T16:58:54.293859Z digest=sha256:674835e82f026741846a9e3f5ffe3cbb1d01ad4c80ecba7ad5eb9064625dc8f2

Observation 86790bd6-1927-4b57-b1de-4ea40e15a410 · inbound

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA cites this paper.

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 273

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T09:47:59.539047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-27T10:21:12.782864Z digest=sha256:4467178db69dbae6a0dc43ce4bd5b9aa1e1dd1add0ab3a55b1eeaed2421fd57e

Observation 0a32dd11-ef7b-4842-8e11-e126b9b56d06 · inbound

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation cites this paper.

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:27:18.622200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-02T19:21:44.653877Z digest=sha256:5b4b6954ce6a3918c6f0ec89e59e0e603caa326fd44ba2706ee12419421825ad

Observation ee9913fe-b9c7-41fb-a1bd-c04ed2130312 · 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 Baichuan-M1: Pushing the Medical Capability of Large Language Models

Reference 105

Resolution
metadata mismatch
local_arxiv, observed 2026-07-10T18:57:31.516474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-10T18:50:22.827472Z digest=sha256:a16ee5266dc8402c6acee39a5ec43ce65eb9893d683d58f0b1a16fc84062ca64