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Paper Citation Record · LEDGER

Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.06510.

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

pith.paper-citation-record.v1
2404.06510 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T01:50:59.184754Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:35:41.922397Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6ca20c80-5653-48d1-a572-ae881d322c97 · inbound

ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents cites this paper.

ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:35:41.925254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-07-01T05:23:21.162004Z digest=sha256:748cbd9693fc9bd0547a0b65067f8d51e74dd6369fd131e2d9a4487b79224f7d

Observation f3e83c79-bc31-40b7-9e6d-d93cfdfa3f97 · inbound

MentalThink: Shaping Thoughts in Mental SVG World cites this paper.

MentalThink: Shaping Thoughts in Mental SVG World Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?

Reference 227

Resolution
unresolved
no resolver link, observed 2026-07-12T01:50:59.184754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T01:50:59.184754Z digest=sha256:b9624d4c8ba338cd9d9f0973b7ac400fa4c94c431639759154415db6cc247da6