Paper Citation Record · LEDGER

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation

As of 12 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 1 inbound Pith citation observation for arXiv:2501.06741.

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

pith.paper-citation-record.v1
2501.06741 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:53:56.300445Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:52:05.733129Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:52:20.186663Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy1
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 882ff514-57ed-47d7-8bcc-f32c3466077b · outbound

This paper cites Large Language Models Are State-of-the-Art Evaluators of Translation Quality.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Large Language Models Are State-of-the-Art Evaluators of Translation Quality

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.191737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 389ad26e-0a48-4bb7-913a-355de93269e3 · outbound

This paper cites USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.198767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93991e0d-f6de-4fb6-ba4a-404e489acfc8 · outbound

This paper cites Capabilities of GPT-4 on Medical Challenge Problems.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Capabilities of GPT-4 on Medical Challenge Problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.206713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96a249d0-3282-4040-a257-7d68e68b00e5 · outbound

This paper cites Branch-Solve-Merge Improves Large Language Model Evaluation and Generation.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.217266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac96481b-be99-494c-b3bb-42ee5e36b152 · outbound

This paper cites Large Language Models are Inconsistent and Biased Evaluators.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Large Language Models are Inconsistent and Biased Evaluators

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.224529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2b5ffa6-3716-420c-a140-911b28ebdccd · outbound

This paper cites Asking and Answering Questions to Evaluate the Factual Consistency of Summaries.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Asking and Answering Questions to Evaluate the Factual Consistency of Summaries

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.238721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccfc4ce7-4224-40df-b690-285a2aa1ca80 · outbound

This paper cites Style Over Substance: Evaluation Biases for Large Language Models.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Style Over Substance: Evaluation Biases for Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.249297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fac25b3b-e79e-499b-8a1b-072d0e206087 · outbound

This paper cites In Proceed- ings of the 2020 Conference on Empirical Methods in Natu- ral Language Processing (EMNLP), 9241–9250.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation In Proceed- ings of the 2020 Conference on Empirical Methods in Natu- ral Language Processing (EMNLP), 9241–9250

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:56.755075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d41210ac-5147-479a-aa5e-5c5897aa6753 · outbound

This paper cites MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.278719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49dad49a-2057-42ca-a7c3-01ac5d9b86ab · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.289464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b673bde-5b80-45cc-979a-96f1c7f0ce63 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.169294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4387d3a-de10-43fd-9cbf-87c3941d24c7 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation BERTScore: Evaluating Text Generation with BERT

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.267239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c7278fb-3f26-40f9-b3c0-f912b6c37490 · outbound

This paper cites GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.182418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cedc0dea-639a-4d9e-a392-bdaba14cdb5e · outbound

This paper cites Towards a Unified Multi-Dimensional Evaluator for Text Generation.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Towards a Unified Multi-Dimensional Evaluator for Text Generation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.300445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8432b77-8764-4c34-9eab-5c8ff4ae6ef0 · outbound

This paper cites Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:56.159764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2d190c1-7565-4136-a009-a7ab8486f8fd · outbound

This paper cites an unresolved cited work.

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:53:56.776761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation c6b61609-6d4b-4b90-ab54-d02a5c529017 · inbound

MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems cites this paper.

MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:52:20.264865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.