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Paper Citation Record · LEDGER

Robust Imitation Learning from Corrupted Demonstrations

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.12594.

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

pith.paper-citation-record.v1
2201.12594 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:57:29.691478Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:37:57.345777Z

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 a7019a4a-0667-4492-b716-1ca76b12bfdd · inbound

SoK: Cybersecurity Assessment of Humanoid Ecosystem cites this paper.

SoK: Cybersecurity Assessment of Humanoid Ecosystem Robust Imitation Learning from Corrupted Demonstrations

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-05T16:57:29.691478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:57:29.691478Z digest=sha256:20b667e5a96a88d299cd714361c57c75da9bdcbd0708d20c39576bc483ab1be2

Observation e2db0466-6282-4ef6-a12e-1df7bf4dc7e0 · inbound

Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics cites this paper.

Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics Robust Imitation Learning from Corrupted Demonstrations

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:37:57.347090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-27T09:52:38.167538Z digest=sha256:9b1e0b8f2b1ea8da867d697a3df2bd130596c38f7e11e9e36f1b5bfa6ec46eea