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Paper Citation Record · LEDGER

InfMLLM: A Unified Framework for Visual-Language Tasks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.06791.

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

pith.paper-citation-record.v1
2311.06791 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T21:15:25.010442Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:33:33.478765Z

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 8af5640d-bf82-4d4c-b402-c5939b0adb6c · inbound

Hallucination of Multimodal Large Language Models: A Survey cites this paper.

Hallucination of Multimodal Large Language Models: A Survey InfMLLM: A Unified Framework for Visual-Language Tasks

Reference 229

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:33:33.480870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-11T12:33:32.631346Z digest=sha256:63df197e9e1749f5c2706369ce68c597aa427dd8b229d64df3c6d260475a8631

Observation f236b25a-a134-43b3-a47d-85bbc32c7c0a · inbound

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering cites this paper.

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering InfMLLM: A Unified Framework for Visual-Language Tasks

Reference 26

Resolution
unresolved
no resolver link, observed 2026-07-11T21:15:25.010442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T21:15:25.010442Z digest=sha256:dabd37d63eb3e08d94147fa501c059fe45a3ba583636433ccb2ba192f75c5058