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Paper Citation Record · LEDGER

Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.10702.

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

pith.paper-citation-record.v1
2409.10702 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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:08:43.808786Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T05:45:22.071873Z

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 e1694ad8-8121-4e03-9066-c0c19ab48bfc · inbound

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data cites this paper.

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-05T18:08:43.808786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:08:43.808786Z digest=sha256:6274c8f187dae995ebf8a6e5f52a907a5305bf12424997bc882e37cb7c5cbf62

Observation c1728b43-f647-4d1d-b783-ae7972465cc1 · inbound

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring cites this paper.

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-05-09T05:45:22.078083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-08T19:36:05.119054Z digest=sha256:60fe153282aa0103d8d228a888b8d96de28b1d30aa3ae16d3241624d7261facd