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Paper Citation Record · LEDGER

Skill-based Model-based Reinforcement Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2207.07560.

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

pith.paper-citation-record.v1
2207.07560 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:57:51.042725Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:06:16.225688Z

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 1dc598fb-f260-499d-aa51-7c6153fb00d2 · inbound

Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study cites this paper.

Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study Skill-based Model-based Reinforcement Learning

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:56:03.833452Z

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=2026-05-10T17:22:10.367439Z digest=sha256:d4c62f49616206ec9ec0802bb2bd74d4acc60d7ee864b6b805846873617ed6b7

Observation 80a1af52-b479-4bbc-b718-3cef2fa9efda · inbound

Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study cites this paper.

Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study Skill-based Model-based Reinforcement Learning

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:50:50.207224Z

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=2026-05-11T00:47:07.042535Z digest=sha256:c0d69747c21c155e9e62af7590b6c14bb7b1da42d74d43a5f5964b2d4c8a4993

Observation 42221b40-0f91-4f6c-bee7-60f374387bbe · inbound

Hierarchical Behaviour Spaces cites this paper.

Hierarchical Behaviour Spaces Skill-based Model-based Reinforcement Learning

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:01:12.738426Z

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=2026-05-08T03:33:48.527621Z digest=sha256:70e55aec8aae3957ca35bdbdfe029adcc6762abc70f16012838d4272a4447c19

Observation 14670143-83af-41f4-ab9b-36f051290e3b · inbound

Atomic-Probe Governance for Skill Updates in Compositional Robot Policies cites this paper.

Atomic-Probe Governance for Skill Updates in Compositional Robot Policies Skill-based Model-based Reinforcement Learning

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:21:26.141496Z

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=2026-05-07T11:12:30.501043Z digest=sha256:fedae1cc8b6550714187e97e0daeb697fd3b86fc42c36aa19980ec26511c719a

Observation c6bdd626-fa8f-456f-9826-feede1fb1333 · inbound

Atomic-Probe Governance for Skill Updates in Compositional Robot Policies cites this paper.

Atomic-Probe Governance for Skill Updates in Compositional Robot Policies Skill-based Model-based Reinforcement Learning

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:06:24.126515Z

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=2026-05-08T03:25:26.291747Z digest=sha256:79216ec9bc84faa1cc2f2ac5d7412f596d8e7c5ef7ed5dc25af1e2f15fe39c84

Observation 3cbd2259-3ebd-47c6-8ea9-985bc5d1cebc · inbound

IMWM: Intuition Models Complement World Models for Latent Planning cites this paper.

IMWM: Intuition Models Complement World Models for Latent Planning Skill-based Model-based Reinforcement Learning

Reference 42

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:06:16.227796Z

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=arxiv_source observed=2026-06-28T15:48:17.098963Z digest=sha256:ca677e0f3ec65f12d4a3d45fc9bae6d82f797f887f7beccb43497ede4a63e149

Observation 4f661d69-37da-4c8c-a4d6-b198a84cd523 · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Skill-based Model-based Reinforcement Learning

Reference 235

Resolution
unresolved
no resolver link, observed 2026-08-04T19:45:35.223095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:45:35.223095Z digest=sha256:be9ec756cd75b755f4360eb927e4a2d172c9e10724fddcee6e473ef4206e1bee

Observation 0c257387-727d-4507-a38e-e0064d82d088 · inbound

Distill Skills into Weights, Not Prompts: Abstract Skills as Privileged Signals for On-Policy Self-Distillation cites this paper.

Distill Skills into Weights, Not Prompts: Abstract Skills as Privileged Signals for On-Policy Self-Distillation Skill-based Model-based Reinforcement Learning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T05:57:51.042725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:57:51.042725Z digest=sha256:114e7a7172d46e1aea30a2f9571366fa67f776e25c60f714378c1f844cd18ae7