Paper Citation Record · LEDGER

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 3 inbound Pith citation observations for arXiv:2506.18034.

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

pith.paper-citation-record.v1
2506.18034 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:28:15.694746Z

measured 43 of 43 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:26:53.228726Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:58:03.698799Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy23
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 434eb2dd-0e27-455e-b2a1-e5bc8338ca12 · outbound

This paper cites Data in brief28, 104863 (2020).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Data in brief28, 104863 (2020)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:14.874276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ca46b21-910d-4dca-be2b-83c796657f67 · outbound

This paper cites In: ECCV.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: ECCV

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.957156Z

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.

Observation 4f75e949-183a-46b4-8e73-8342627b7307 · outbound

This paper cites Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:14.914804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e2d66fd-7b4e-410a-9665-92cd591b931b · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:14.950889Z

Source-reported events for the cited work

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Observation 19f9c60c-bb9c-4a0e-a965-9fb8b3a1c0be · outbound

This paper cites In: MICCAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: MICCAI

Reference 5

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Unavailable: canonical work link unavailable.

Observation 130930e1-d3bf-4d3b-aa96-fc67bdedf5a1 · outbound

This paper cites discriminability: Batch spectral penalization for adversarial domain adaptation.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster discriminability: Batch spectral penalization for adversarial domain adaptation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.926767Z

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.

Observation db04da42-f694-4dae-a344-cb82d2a053bd · outbound

This paper cites In: CVPR.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: CVPR

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.912724Z

Source-reported events for the cited work

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Observation acd33758-3669-47b8-b8eb-37b2ce701faa · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.074979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f820e18-a461-42cf-842e-593f2dc1b83a · outbound

This paper cites In: CVPR.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: CVPR

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.900830Z

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.

Observation ea3fe3df-bc4f-4a41-b8ae-31dc241e44cf · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.107951Z

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Unavailable: canonical work link unavailable.

Observation 5b5fecac-56a0-45ad-bf46-33102619d617 · outbound

This paper cites The Llama 3 Herd of Models.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.113903Z

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Unavailable: canonical work link unavailable.

Observation b09c6314-0f3d-4f28-8190-0057e288561f · outbound

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

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.120583Z

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Unavailable: canonical work link unavailable.

Observation 7e14b631-b95f-446a-81b8-94ce670005ad · outbound

This paper cites In: WACV.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: WACV

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.882989Z

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.

Observation 5dbb7bed-8cbf-4232-b0d9-7b0473142636 · outbound

This paper cites TMI42(5), 1484–1494 (2022).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster TMI42(5), 1484–1494 (2022)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.869938Z

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.

Observation f12a1d4e-2777-497e-bb2c-7933f48fa3c5 · outbound

This paper cites Nature methods18(2), 203–211 (2021).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Nature methods18(2), 203–211 (2021)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.235567Z

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Unavailable: canonical work link unavailable.

Observation 954a498d-bd13-4686-ad84-ecdbf3eae903 · outbound

This paper cites In: Proc.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: Proc

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.832731Z

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.

Observation e73ae49f-e539-400f-936c-8b4b3275a170 · outbound

This paper cites In: ICLR.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: ICLR

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.813621Z

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.

Observation 90cb39c2-7812-4187-b9c3-535a320bebee · outbound

This paper cites NeurIPS36(2024).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster NeurIPS36(2024)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.798663Z

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.

Observation 61eeb8c0-787d-4801-bf84-c69fd50839ae · outbound

This paper cites In: ICML.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: ICML

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.781911Z

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.

Observation 88f1882f-018a-4659-98de-9c9897507aa4 · outbound

This paper cites TIM71, 1–15 (2022).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster TIM71, 1–15 (2022)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.739816Z

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.

Observation ba32830f-efe0-4445-9d83-e7f395f71de0 · outbound

This paper cites Towards Accurate Unified Anomaly Segmentation.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Towards Accurate Unified Anomaly Segmentation

Reference 21

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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.

Observation 39aa0589-0cce-45bf-95ad-a2044ceaac7c · outbound

This paper cites Frozen Transformers in Language Models Are Effective Visual Encoder Layers.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Frozen Transformers in Language Models Are Effective Visual Encoder Layers

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02455207-3adc-4ed2-a8c7-40435f577b67 · outbound

This paper cites In: CVPR.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: CVPR

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.726813Z

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.

Observation 6f2a8c08-9644-45e9-b71c-577c13c3316f · outbound

This paper cites In: 10th International symposium on medical information processing and analysis.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: 10th International symposium on medical information processing and analysis

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.709534Z

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.

Observation d60992b3-5fb4-4f1d-9f3f-fb5a76e8f605 · outbound

This paper cites In: ACMMM.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: ACMMM

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.688859Z

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.

Observation 018951b1-299b-4ede-9b8d-4b36f1b5e1ba · outbound

This paper cites In: MICCAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: MICCAI

Reference 26

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Observation c77b6bfc-89e6-456a-99be-3a23fb98c16a · outbound

This paper cites In: 2007 15th European signal processing conference.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: 2007 15th European signal processing conference

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.409753Z

Source-reported events for the cited work

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Observation 3ce6f96e-0aef-4093-bfdf-5555b6e1f159 · outbound

This paper cites In: MICCAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: MICCAI

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.639827Z

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.

Observation f9559466-d80b-4dae-b4bd-8a55eebfe0b2 · outbound

This paper cites an unresolved cited work.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:16.612676Z

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.

Observation 2f41f62f-ce1c-436f-bbf6-43e7a059423b · outbound

This paper cites MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.502128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ef682f4-a145-48da-9bd7-b3aee384fbec · outbound

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Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Medical Image Analysis p

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.585314Z

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

Observation 2f7ffafb-5411-4b74-8b7e-f8c6064d3214 · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: International Conference on Medical Image Computing and Computer-Assisted Intervention

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.549173Z

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.

Observation eb1503d9-9659-4a51-892d-e7ab66501fd8 · outbound

This paper cites In: MICCAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: MICCAI

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.500509Z

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.

Observation c02ff227-9a2d-4b55-a2b3-02d9577cd3df · outbound

This paper cites Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.586313Z

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Observation 4f4ca84d-9a30-4778-9b5b-762367468d81 · outbound

This paper cites In: CVPR.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: CVPR

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.478611Z

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.

Observation 5400b10f-a214-4d4a-a643-35ab7437a204 · outbound

This paper cites In: MICCAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: MICCAI

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:15.595419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3c10140-8e52-4ff5-bbcc-6a68c20c28a5 · outbound

This paper cites In: AAAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: AAAI

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.450986Z

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.

Observation b1b1ba13-7e89-44a9-b9bd-56eb7ff110c4 · outbound

This paper cites In: MICCAI.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: MICCAI

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.375460Z

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.

Observation a95c431e-bf8d-4b21-a0da-b19f75bc635e · outbound

This paper cites In: ICML.

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster In: ICML

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.281832Z

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.

Observation e1209cd4-ed62-4612-ae48-dc320a49a523 · outbound

This paper cites TMI39(6),1856– 1867 (2019).

Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster TMI39(6),1856– 1867 (2019)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:16.135709Z

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.

Pith citing papers

Observation b59ff3b7-b109-43b2-a619-846cb26a42b5 · inbound

Large Language Model Evaluated Stand-alone Attention-Assisted Graph Neural Network with Spatial and Structural Information Interaction for Precise Endoscopic Image Segmentation cites this paper.

Large Language Model Evaluated Stand-alone Attention-Assisted Graph Neural Network with Spatial and Structural Information Interaction for Precise Endoscopic Image Segmentation Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:53.228726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b98cc199-3957-4891-98b7-781b2e625619 · inbound

DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning cites this paper.

DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:58:03.702230Z

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.

Observation 51da866f-3a1b-4a22-b961-28b24a221ecc · inbound

DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning cites this paper.

DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T16:26:27.395176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.