Paper Citation Record · LEDGER

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation

As of 20 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2605.28239.

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

pith.paper-citation-record.v1
2605.28239 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T12:47:56.539826Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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 exact5
verified fuzzy0
unresolved3
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 46a6d209-278f-4c6d-a85d-75e806edc46a · outbound

This paper cites Qwen2.5-VL Technical Report.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation Qwen2.5-VL Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-06-29T12:53:26.700626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c4889d7-ea90-497b-b050-9478a24ae8dd · outbound

This paper cites SAM 3: Segment Anything with Concepts.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation SAM 3: Segment Anything with Concepts

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T12:53:26.692729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0aab898-13f0-4193-b248-21cb7de909ae · outbound

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

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-06-29T12:53:26.697842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 280b6d07-d155-475e-8d12-e0b0da9f3fb3 · outbound

This paper cites LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:53:26.683995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68d46feb-1245-4e7c-be43-d0b50e2cfe0b · outbound

This paper cites Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:53:26.687250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8da58725-9607-4129-bfc3-5bf0116749e1 · outbound

This paper cites Text Augmented Spatial-aware Zero-shot Referring Image Segmentation.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation Text Augmented Spatial-aware Zero-shot Referring Image Segmentation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:53:26.690285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2b1705a2-534d-4cd6-ba45-9994a1fe5369 · outbound

This paper cites Llafs++: Few-shot image segmentation with large language mod- els.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025a.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation Llafs++: Few-shot image segmentation with large language mod- els.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025a

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T12:47:56.539826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a0d36ce-5d5f-4871-8fc3-d4a8add71ff4 · outbound

This paper cites Each instance is an image–expression pair with a binary mask as supervision.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation Each instance is an image–expression pair with a binary mask as supervision

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T12:47:56.539826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3504220-48b8-4a01-8bec-a9fc685843ab · outbound

This paper cites The dataset includes 26,711 images, 54,822 objects, and 104,560 expressions.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation The dataset includes 26,711 images, 54,822 objects, and 104,560 expressions

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T12:47:56.539826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 679957de-bea6-459e-b060-efb55bdc1d3a · outbound

This paper cites When constructing the foreground/background/ignored regions, we suppress ambiguous boundary supervision by marking a 3-pixel-wide band around region boundaries as ignored.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation When constructing the foreground/background/ignored regions, we suppress ambiguous boundary supervision by marking a 3-pixel-wide band around region boundaries as ignored

Reference 10

Resolution: malformed identifier; arxiv_id, observed 2026-06-29T12:53:26.695455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.