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Paper Citation Record · LEDGER

Multi-Task Interactive Robot Fleet Learning with Visual World Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.22689.

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

pith.paper-citation-record.v1
2410.22689 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:10:33.667204Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:39:44.749129Z

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 1e4574e9-b054-4894-aba4-794ce88bf411 · inbound

SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning cites this paper.

SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning Multi-Task Interactive Robot Fleet Learning with Visual World Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T13:10:33.667204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:10:33.667204Z digest=sha256:ff0efb4a0a883abb2cc871d03458f3ae191840a2c66b28372ac18c658102b1df

Observation ab5d7d07-d49f-4209-8e4b-d0cb1696efac · inbound

Whole-Body Conditioned Egocentric Video Prediction cites this paper.

Whole-Body Conditioned Egocentric Video Prediction Multi-Task Interactive Robot Fleet Learning with Visual World Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T22:27:12.337553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:27:12.337553Z digest=sha256:9ae983e706752b5cabdcf2f08953259f59ee4123894e91a83af52a5c2c409b43

Observation 903cb375-d718-4807-989b-48538d11a557 · inbound

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies cites this paper.

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies Multi-Task Interactive Robot Fleet Learning with Visual World Models

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:57:26.073526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-27T18:35:24.934570Z digest=sha256:1e622beeb9512b8ad129703b648b6f79edfd19538e5ea9671af1193264ed2c67

Observation f258a38d-2476-46e6-a80b-2733daffae9d · inbound

Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents cites this paper.

Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents Multi-Task Interactive Robot Fleet Learning with Visual World Models

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:39:44.751225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T08:43:06.983870Z digest=sha256:8abbc4660078ee786ff0ff6cac2e0181520cc587a0963e28236f563c9c9066be

Observation 5e67bf50-a428-4b98-b1d6-44e8b77cc92c · inbound

Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection cites this paper.

Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection Multi-Task Interactive Robot Fleet Learning with Visual World Models

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T02:46:03.612702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T02:46:03.612702Z digest=sha256:82f1e3514c82adf3b100293b2941d0edcc97bc6430833501519a6dd60567234f