Paper Citation Record · LEDGER

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data

As of 8 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2506.04717.

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

pith.paper-citation-record.v1
2506.04717 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:39:02.625110Z

measured 10 of 10 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 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

10 of 10 outbound references displayed

verified exact0
verified fuzzy10
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bc76cad-5d50-47b9-bea2-2c26b24d8be1 · outbound

This paper cites 32‐2: Multi AI Approaches for Improving OLED Display Pattern Repair in Manufacturing Processes.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data 32‐2: Multi AI Approaches for Improving OLED Display Pattern Repair in Manufacturing Processes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:05.067813Z

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.

Observation ad5d26a6-7f8a-40e0-97a6-9e281c8128da · outbound

This paper cites A more reliable defect detection and performance improvement method for panel inspection based on artificial intelligence.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data A more reliable defect detection and performance improvement method for panel inspection based on artificial intelligence

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.837207Z

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.

Observation ecc1f2f3-2f27-473f-b1d6-68c7709d182b · outbound

This paper cites P‐236: Late‐News Poster: Hard defect detection and classification for display panel products.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data P‐236: Late‐News Poster: Hard defect detection and classification for display panel products

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.548214Z

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.

Observation d493f8c2-ebfc-4836-9fce-ec4fdd16bab3 · outbound

This paper cites 81‐1: Invited Paper: Data Augmentation for Applying Deep Learning to Display Manufacturing Defect Detection.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data 81‐1: Invited Paper: Data Augmentation for Applying Deep Learning to Display Manufacturing Defect Detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.312485Z

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.

Observation 6f17e938-01d9-4341-9dc0-f3e7e520107a · outbound

This paper cites P‐234: Late‐News Poster: A Data‐Centric Approach to Minimize Defect Leakage in an AI‐based Automated Surface Inspection System for Display Manufacturing Process.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data P‐234: Late‐News Poster: A Data‐Centric Approach to Minimize Defect Leakage in an AI‐based Automated Surface Inspection System for Display Manufacturing Process

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:04.040512Z

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.

Observation f1d8d7d5-a7eb-4e98-8d66-03700d5e5f0c · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data Images speak in images: A generalist painter for in-context visual learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.795570Z

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.

Observation e59d6dd2-b967-401a-98f0-fd6f964bd7a6 · outbound

This paper cites Seggpt: Towards segmenting everything in context.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data Seggpt: Towards segmenting everything in context

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.534419Z

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.

Observation 4747f1f3-c752-48eb-9a17-ec1f688cb636 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data Masked autoencoders are scalable vision learners

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.323574Z

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.

Observation 2cba26eb-9906-4aef-a131-5ab941fb67c4 · outbound

This paper cites Segment everything everywhere all at once.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data Segment everything everywhere all at once

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:03.121218Z

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.

Observation 459f0caf-8f81-44c2-bda9-6ec27920da3f · outbound

This paper cites Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool.

Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:39:02.892182Z

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.

Pith citing papers

No inbound Pith citation observations are available.