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Paper Citation Record · LEDGER

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2506.12307.

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

pith.paper-citation-record.v1
2506.12307 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:59:15.598200Z

measured 36 of 36 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-10T18:50:22.827472Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T18:57:31.625203Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8401f3de-baca-447b-856d-a4ace2fbfc4b · outbound

This paper cites Phi-4-reasoning Technical Report.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Phi-4-reasoning Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:09.833831Z digest=sha256:4601f00b7d34b2e2f5c5125681867c2c0d49a6253b9fe04f5b122d85c011cca7

Observation 056a1ff2-d7cc-4709-ac9a-4ec2702727da · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 2

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source=arxiv_source observed=2026-08-07T00:59:10.081098Z digest=sha256:17655537460511483e969d7baa269b3305785f250e7c893c709b0513ccd8e837

Observation 04de581b-a237-43cd-9123-0c3a50d17f75 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 3

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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.

source=arxiv_source observed=2026-08-07T00:59:10.276299Z digest=sha256:9f285c596ce0de843932b411d055bf9e91f215b73a45013c802e5e9491d066cc

Observation 31c6dc5b-2677-45d6-9443-ebba305728e6 · outbound

This paper cites HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

Reference 4

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T00:59:10.422171Z digest=sha256:5eb160b4684b00080afc8fdea4ae7e76b268079730f60292de05d481dbeb6335

Observation 99af1e2b-ddad-407c-9840-1d3933616316 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 5

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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.

source=arxiv_source observed=2026-08-07T00:59:10.549901Z digest=sha256:3571dd63ea60f4da2b949641beab9492b1e9f4f0b3aeb81f355429fe66f89068

Observation 1e43fd60-59c2-4faf-b2ea-d4f8eb9dcba4 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:10.772605Z digest=sha256:290d917212d5028bf923db4c5629528d3129e5d201122d62dc96fae1500415e0

Observation bb78a099-e268-46bb-9a19-9a1792162fef · outbound

This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Reference 7

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T00:59:10.949543Z digest=sha256:8d6ee51de75e2201d9758a8da388ab41b6c518d6fdfc3163ec9c6d3d9192f5fc

Observation 46322889-addc-4d97-9840-264a60c2d1f8 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-07T00:59:11.183268Z digest=sha256:e96f9f02830b6bde065b2b30e366316c0971808eff269383bf9e7cc94f6b508c

Observation 84c77575-cc46-4928-8c8f-384b66d037ee · outbound

This paper cites O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning

Reference 9

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T00:59:11.360197Z digest=sha256:bc02ce13a2de58839f6f77fada6047dacb17e8c90daac0b2918f9a1d7412fe03

Observation 6f783802-b219-495c-8450-3daf0ed02b0e · outbound

This paper cites OpenAI o1 System Card.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning OpenAI o1 System Card

Reference 10

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source=arxiv_source observed=2026-08-07T00:59:11.506088Z digest=sha256:2f806aba5f20ff1deee4d017ae59e5b76d0169c7c8685df5db509b6f43a63662

Observation 4e8a4fb8-8f3b-430a-9ee1-a147d52dc6c2 · outbound

This paper cites Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models

Reference 11

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source=arxiv_source observed=2026-08-07T00:59:11.712948Z digest=sha256:b1d7e1b7d32bb6b68f7de35b14a168fab13ff2b904f54aba06ea138e9a6238dc

Observation f7a337e5-a406-4d6d-9a74-1117d2153a68 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 12

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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.

source=arxiv_source observed=2026-08-07T00:59:11.947481Z digest=sha256:e8e1b855253073204a3752711ffb82b35c0ea35b63cd107bdf3f91dd75e8af0c

Observation f2db5fb0-5f05-4ff8-89c2-03d53c708fd5 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:12.084690Z digest=sha256:2304cf54d7665c0f0eb81f7169f5b8b8229e1cedd0ae4179e304d3908bfffb4f

Observation 1f49c605-4ba8-4fb4-a4b2-360e9411bd1d · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 14

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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.

source=arxiv_source observed=2026-08-07T00:59:12.212532Z digest=sha256:0598e0628209890ec43bd727cf8ffc167fda205191bb29537d2b8d39335e7ad6

Observation fc937c5d-b53c-43e1-a2de-5cce7ee5bb53 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-07T00:59:12.353500Z digest=sha256:b1b1ea7f673e04659eb2bb83931230ae784d14ce11e1deec294b0bddd63566c7

Observation cb20ba7a-af7f-4a4d-a0b5-5d4b83a6c639 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:12.490526Z digest=sha256:3522e869f19e07ab812cdfc38be476a86353a1a3a976efba85c668fae9b5494c

Observation ac93fe8b-42b7-47d1-9945-99ab4958ccc8 · outbound

This paper cites MedCoT: Medical Chain of Thought via Hierarchical Expert.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning MedCoT: Medical Chain of Thought via Hierarchical Expert

Reference 17

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source=arxiv_source observed=2026-08-07T00:59:12.703977Z digest=sha256:2c90e157cd035997bec8188a33983d21fa1e5b5f16a4bc458daadef7b4b19f45

Observation fe447467-8de9-422f-aeff-768cf53608a1 · outbound

This paper cites s1: Simple test-time scaling.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning s1: Simple test-time scaling

Reference 18

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source=arxiv_source observed=2026-08-07T00:59:12.896460Z digest=sha256:5ad50dffefc2dbf8eb32cdc086cef8e4add3d7526f146be00fad32ee83e6ee6f

Observation 3687db58-cecc-43ed-9611-cae57eb00cdf · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-07T00:59:13.028033Z digest=sha256:186eb1940acd051a6d41e37797c6d48872513c725cafe7be2e659f1b52652c3b

Observation 51bedf58-c688-4b4a-bf4c-49668a85f01e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 20

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source=arxiv_source observed=2026-08-07T00:59:13.211229Z digest=sha256:1a35383df8db7927542c60c7190b99660917c764205ade38e87a0ba33db6909c

Observation 5688dbab-e7b8-4dbe-9205-a259ecfb5437 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 21

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:13.346100Z digest=sha256:60ddb69ab978f2d699a2a981d90e20e3e412dffb515592612807d9449020be56

Observation 5b442089-496d-4b5a-98ed-c8938c82a44c · outbound

This paper cites A Long Way to Go: Investigating Length Correlations in RLHF.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning A Long Way to Go: Investigating Length Correlations in RLHF

Reference 22

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:13.509910Z digest=sha256:04e6df18d305b19840e555eb75cfc7530cd097b72ba9f5ac7e4e713b9056e4e7

Observation 2bd87863-1112-4f2b-bd04-c846b5b0315b · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 23

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source=arxiv_source observed=2026-08-07T00:59:13.702592Z digest=sha256:2c69a69fba6fea5306bf3340737f9b05089a4289f6a35c60bc4f5f9ab7aea13c

Observation 9d0c47de-a844-4976-bfd7-516e04eb9b0e · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 24

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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.

source=arxiv_source observed=2026-08-07T00:59:13.886171Z digest=sha256:fd88c3c6457923a2b0fd617f9c7eb60f94d322d74acf1c871e97e26f7708e23c

Observation 7cdb5a5a-7032-4b64-a3a9-134e3745bd11 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-07T00:59:14.069904Z digest=sha256:9b89b6fe72ae2c079d645ef9b670b0929c588d738f5a22e3ace2498f89fb93b7

Observation f3313ee2-93db-4c35-9ba7-ea55de94f989 · outbound

This paper cites Qwen2.5 Technical Report.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Qwen2.5 Technical Report

Reference 26

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source=arxiv_source observed=2026-08-07T00:59:14.261785Z digest=sha256:89f56a2f34cfa687d56e9ebfa1aaf77332901c9f5cafd46b40371f0b42a69d95

Observation 41080bab-eecf-4ffd-a170-102d4a00c6d1 · outbound

This paper cites FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training

Reference 27

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no resolver link, observed 2026-08-07T00:59:14.414021Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:14.414021Z digest=sha256:1eff50cb230797145c3912f6b35e1e001ae7f88229ac9a73497954ac656d0e94

Observation ae500814-b1e4-4faa-bb52-f6f79ade8617 · outbound

This paper cites Following Length Constraints in Instructions.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Following Length Constraints in Instructions

Reference 28

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no resolver link, observed 2026-08-07T00:59:14.613450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:14.613450Z digest=sha256:495d45d66c319f2f48f5f97688b0cc6eacc4a74762fbe6de3f65c64e0e4b993b

Observation f95b5e92-aa72-4268-aa69-ba3242d98517 · outbound

This paper cites an unresolved cited work.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Unresolved cited work

Reference 29

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raw_fallback, observed 2026-08-07T00:59:16.451830Z

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.

source=arxiv_source observed=2026-08-07T00:59:14.755403Z digest=sha256:7125a5bd7c1bcd8b5c916a5f111a31b801318ccef0dc41f26e514d77559439b1

Observation 6e31e4cb-653f-425f-867c-c7a1b88411bb · outbound

This paper cites Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning Med-RLVR: Emerging Medical Reasoning from a 3B base model via reinforcement Learning

Reference 30

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source=arxiv_source observed=2026-08-07T00:59:14.911209Z digest=sha256:05e6d8918c2568d4246b534318288226d5d29fae18ebb6fa0c1ef20a4ac70cce

Observation 85f4c374-610f-4f6c-b165-40700cb6eb05 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 31

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:15.080266Z digest=sha256:05d79a8a40667f8cabe63255bcf5ab61d915bb71bae644d057b962db17e8154f

Observation b21cae6e-9d43-4d4a-81f2-714733e39145 · outbound

This paper cites MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:15.260193Z digest=sha256:42684475daa9e35b407722e30c16112758e5e2a7bdbc18c93cb19a51b2c177dd

Observation 7ce861ae-a985-4971-9f30-cdaa5fb58be6 · outbound

This paper cites online" 'onlinestring :=.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning online" 'onlinestring :=

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:15.435680Z digest=sha256:11c1acb67e9d28626f41bc605fab3b66f7cf7b351728e9964eb328b20124a0fa

Observation 092f3c12-39f9-4ef5-9958-81a71dc70da8 · outbound

This paper cites write newline.

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning write newline

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:59:15.598200Z digest=sha256:d9b305d1d0487e8f7a36484309ff851bc30144d2d2f7a416d44863ebc589ae93

Pith citing papers

Observation 7d7bdbbb-83c5-4a6f-9d9d-56823e545fba · inbound

Scalable Stewardship of an LLM-Assisted Clinical Benchmark with Physician Oversight cites this paper.

Scalable Stewardship of an LLM-Assisted Clinical Benchmark with Physician Oversight Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

Reference 18

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malformed identifier
arxiv_id, observed 2026-05-16T20:23:23.811816Z

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.

source=pdf_text observed=2026-05-16T20:21:40.867354Z digest=sha256:c65d24a24e26e14c035ba2da101ae5ccb1ae70a2624dac4b93129ae9bea03336

Observation cfa7d1cf-2e59-4fb1-8a59-84a31d1fb65d · 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 Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

Reference 104

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metadata mismatch
local_arxiv, observed 2026-07-10T18:57:31.626465Z

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.

source=pdf_text observed=2026-07-10T18:50:22.827472Z digest=sha256:cbfc6e9366b9a2c2cef58d471544b629b5f5bb410554e4c7b8f8019133d46e1c