Pith. sign in

Paper Citation Record · LEDGER

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.12583.

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

pith.paper-citation-record.v1
2412.12583 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:03:18.964121Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy17
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f90c77da-5c51-40d7-9b5f-d829356c13ee · outbound

This paper cites Assessment and Plan.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Assessment and Plan

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.285923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.898277Z digest=sha256:412ebd16063154153ef00454385b202e648da87968a95afd714bf13cfa9f0a35

Observation 9f14e15e-fbdf-4a0f-b71c-3ca288c69835 · outbound

This paper cites OpenAI o1 System Card.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise OpenAI o1 System Card

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.865985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.865985Z digest=sha256:ba03ede6dfda8f10d117b9a6ae17fd46790379854b6359c6c5116e7b155b178b

Observation e0b758f3-fc61-4ee2-90a1-52ac400d43be · outbound

This paper cites Enhancing LLM Reasoning with Reward-guided Tree Search.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Enhancing LLM Reasoning with Reward-guided Tree Search

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.869765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.869765Z digest=sha256:5f4e2d96e646844cb032e5c7f89829c1a30d9c6f5df54f7e6f309115c38394c9

Observation 6c91382e-92cc-4d9a-b214-0ce2cc2d7563 · outbound

This paper cites Improving Clinical Note Generation from Complex Doctor-Patient Conversation.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Improving Clinical Note Generation from Complex Doctor-Patient Conversation

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.873431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.873431Z digest=sha256:a0e0064233f512abd93de24704531e05bad20b37b22487528fb90ca4f3bcd539

Observation a9a4f6bf-00b6-457c-852e-77d4b4e9d382 · outbound

This paper cites Step_score.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Step_score

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.232618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.916562Z digest=sha256:6bff0fcae1450a2ee2154177feb645e908815cad20d687bec674bee94fa813b1

Observation 60dd2646-674d-4d26-9c26-164de05f72a0 · outbound

This paper cites Problem_no.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Problem_no

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.222370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.919881Z digest=sha256:527de0fd9059a6f6a12db45ba7a79fcc60f1b6ebe9fff0573e056209cee6cff5

Observation a315d57f-2173-47ca-a188-3b197134fbf1 · outbound

This paper cites Note_completeness_score.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Note_completeness_score

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.213006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.923494Z digest=sha256:eb98dd8409b588c5baf0b202a82ced7f3378922c018eda1f5acb2d3677db1f3f

Observation 069924c8-2e2d-40f6-9e83-05af51590693 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.889655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.889655Z digest=sha256:142a3ee2a7f990db9a71829e2548029406595b355e6bb2670ab90a777e1a72d3

Observation e3bc556b-9625-479a-ac3d-d8a859302911 · outbound

This paper cites an unresolved cited work.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:03:19.274866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.902042Z digest=sha256:811dff36d625518c5ef0c987ca617cf33ad0637d82eb65a66504f474e636106f

Observation ed1de98d-c448-4a62-a77e-59f519af3236 · outbound

This paper cites Assessment and Plan.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Assessment and Plan

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.264486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.905506Z digest=sha256:748b9215be6dfb99adf1ab1c486f67562337b56e649f5a982658e12f2f48c75a

Observation a4cb0c77-6e76-4221-a4d1-aad9156a297a · outbound

This paper cites Follow-up instructions:.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Follow-up instructions:

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.254189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.909352Z digest=sha256:6da74e2c130b1cef0521d0a568abab6d9c5bf89c2b5b3514e22386b5f6db8b6c

Observation 59f1dfa9-39e0-4db1-bcfb-a9619532c188 · outbound

This paper cites Assessment:.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Assessment:

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.243007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.913049Z digest=sha256:cecdb7ca87b9b680e1b7d1a4e9e69b029097105dfe607e9ea856464d38a1804c

Observation 9eb794e5-7aaa-480a-8fac-b16bdb8a40ae · outbound

This paper cites Problems.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Problems

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.202951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.927064Z digest=sha256:ff5b98ea7e71509e4a4b79f4af5ae7736233ef5e2d26c79f7e5432c6e1559247

Observation c75f9158-a2ad-4fe7-9e42-77683dfa3f59 · outbound

This paper cites Assessment and Plan.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Assessment and Plan

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.192091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.931126Z digest=sha256:d2ea001467bc60b5f3bd4f69489a468222502152d0946c59bbc288fee282e3a7

Observation 5046d286-9cad-4bb2-9546-d03566f3be83 · outbound

This paper cites an unresolved cited work.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:03:19.181527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.934779Z digest=sha256:b901e9966d939a8b05128fdc7f70f13bffbf74bb95cb77fa20eff2b101927067

Observation d9b0c787-2e97-4f58-bc50-bae8aab83416 · outbound

This paper cites Most conversations occurred in the outpatient setting.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Most conversations occurred in the outpatient setting

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.170957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.938927Z digest=sha256:a9a9ac92c98f3d880d8d9aa911b79d923d7ee83636a79de89a0ec88c9cb86de2

Observation 2ad56c2c-91f6-4e1c-adee-2c4d3c53d271 · outbound

This paper cites Assessment and Plan,.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Assessment and Plan,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.159688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.942611Z digest=sha256:a4f18e33a73937a46b7666de906ec44d91bba3efa36b50c1b70d20cab1c5a3a4

Observation fb22863c-8f09-41b9-905e-c82b0470d602 · outbound

This paper cites Assessment and Plan.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Assessment and Plan

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.147994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.946307Z digest=sha256:ee3f383a91c8e338726dfbddeb0a11992596a6d045b3355924a74eb0bf338338

Observation cd484009-b526-40be-9beb-7d4c2088e954 · outbound

This paper cites an unresolved cited work.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-11T14:03:19.135082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.949949Z digest=sha256:5039466e7afdb971f5eb136de8f457d2eb470b9513f098cd2d066ddd49bfd9fe

Observation 2d2c0c4c-a384-43ef-872f-13dd0c28a7b4 · outbound

This paper cites This is appreciated but not mandatory.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise This is appreciated but not mandatory

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.122497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.953609Z digest=sha256:77c1d82a854a27ebb3c12e16aafe013b547834ba67db4c942e3f51bd2b2ff43c

Observation d464af44-17e3-4ab4-875f-5ae58086eba6 · outbound

This paper cites He experiences episodic shortness of breath, eye watering, and occasional diarrhea after heavy drinking.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise He experiences episodic shortness of breath, eye watering, and occasional diarrhea after heavy drinking

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.111086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.957018Z digest=sha256:4dd67a350cea305c8152e3c03609e6783f6da4ab82f8b6a7a0d2736f664c9e05

Observation ac7a7538-9329-40ee-ae94-ee7c77713660 · outbound

This paper cites This suggests that she has chronic hepatitis C.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise This suggests that she has chronic hepatitis C

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.098590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.960693Z digest=sha256:7b55832710680b5ea5ebd532fb15d53f643f240c6c8b2d3f4734f3ca630c8dc3

Observation 0941633d-2271-45c7-a34c-1836a56babe8 · outbound

This paper cites She denies any other symptoms.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise She denies any other symptoms

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.086736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.964121Z digest=sha256:0cd99b7df6ab64b80acd1f5128344510f93a7470c706423b36f18170385bfe3d

Observation f3441654-e8a6-4dcc-82c3-166a57e186da · outbound

This paper cites +” score label ex- ceeds that of its “ −.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise +” score label ex- ceeds that of its “ −

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T14:03:19.297399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-11T14:03:18.893560Z digest=sha256:7934f9bdfab42f560b45a19d98f30aad8c72f901d0206fa55fe00fa7d63dace1

Observation fbc91d87-fdc7-4ae0-96ea-3457ee901e3e · outbound

This paper cites O1 Replication Journey: A Strategic Progress Report -- Part 1.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise O1 Replication Journey: A Strategic Progress Report -- Part 1

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.885861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.885861Z digest=sha256:8264fdde4e980a8c2ede3723460ff7fc1615a684a7468ec5ec70f1a9e6497246

Observation 49d2a545-6aaa-4729-8469-6dc41e93ed0a · outbound

This paper cites Let's Verify Step by Step.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Let's Verify Step by Step

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.877810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.877810Z digest=sha256:b87af58e17ea8bae5422ae808660d6ecaadd0ace31277d41ea115bafcca0baaa

Observation eb4b9d9e-dacf-4928-b6f9-cb4672b8d41e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Training Verifiers to Solve Math Word Problems

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.861035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.861035Z digest=sha256:7958b29d50dd8cdb1ca2a1c34fcede954333682e880859fff970c4bef4f39f3c

Observation 12e23caf-5740-48e4-a599-552c1dc2ba0d · outbound

This paper cites Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-11T14:03:18.881721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:03:18.881721Z digest=sha256:414da7091786da8981d87b714e00ab25b8e94e8528955f710e6517d649111998

Pith citing papers

No inbound Pith citation observations are available.