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Paper Citation Record · LEDGER

Residual-MPPI: Online Policy Customization for Continuous Control

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.00898.

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

pith.paper-citation-record.v1
2407.00898 v5

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 2026-06-28T21:05:59.983228Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:27:25.864043Z

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 db05c93c-61a7-4688-8c15-81e9a97e2f7e · inbound

Generalized Model Predictive Path Integral Control as Expectation--Maximization cites this paper.

Generalized Model Predictive Path Integral Control as Expectation--Maximization Residual-MPPI: Online Policy Customization for Continuous Control

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-01T20:26:12.994704Z

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=2026-06-28T21:05:59.983228Z digest=sha256:ce84914c6b90dfc45a2ea2acdb7f1db4a2a5f90527d9a09c27eab69c7adba16a

Observation 1345ce3b-8041-4313-b818-0769d113c96c · inbound

Reinforcement Learning for Flow-Matching Policies with Density Transport cites this paper.

Reinforcement Learning for Flow-Matching Policies with Density Transport Residual-MPPI: Online Policy Customization for Continuous Control

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:27:25.865965Z

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=2026-06-27T18:55:02.040180Z digest=sha256:d0cafde8ebb7d1ef86d43eeb66cd576ee97030edf055258b9f2e3c79602984bf