Paper Citation Record · LEDGER

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2605.26828.

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

pith.paper-citation-record.v1
2605.26828 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T16:45:44.659577Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact1
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ed6e7735-852e-4f3e-a9d4-68d2d292925e · outbound

This paper cites Bilevel Learning for Bilevel Planning.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Bilevel Learning for Bilevel Planning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:41.325972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4db7c45-41f4-43f7-921a-0d444f7977c5 · outbound

This paper cites VisualPredicator: Learning abstract world models with neuro-symbolic predicates for robot planning,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming VisualPredicator: Learning abstract world models with neuro-symbolic predicates for robot planning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf045aea-f80e-4841-99bc-bde71a967d80 · outbound

This paper cites 71 952–71 980.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming 71 952–71 980

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e3baa9-b424-4a8b-9287-17ff95ad19ad · outbound

This paper cites Embodied active learning of relational state abstractions for bilevel planning,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Embodied active learning of relational state abstractions for bilevel planning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ace647d5-fbdb-42c4-bd50-77d8928e020b · outbound

This paper cites Learning neuro-symbolic skills for bilevel planning,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Learning neuro-symbolic skills for bilevel planning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fe56a43-874a-48c8-86cd-905e09644c46 · outbound

This paper cites Learning programs by learning from failures,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Learning programs by learning from failures,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19412538-d96e-456d-8a27-1d7f44eabf9c · outbound

This paper cites Inductive learning of robot task knowledge from raw data and online expert feedback,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Inductive learning of robot task knowledge from raw data and online expert feedback,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0dc8df7-9a59-4711-9f75-cc387cfdd40f · outbound

This paper cites Relational af- fordance learning for task-dependent robot grasping,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Relational af- fordance learning for task-dependent robot grasping,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64ff32dd-9ab5-4171-b944-b92d07241952 · outbound

This paper cites The robot engineer.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming The robot engineer

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 048476f6-835e-44af-bd28-30a592301b6d · outbound

This paper cites A relational approach to tool-use learning in robots,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming A relational approach to tool-use learning in robots,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ac63ca6-9ac3-41fe-a3cb-0eb6e333c1d1 · outbound

This paper cites Closed loop interactive embodied reasoning for robot manipulation,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Closed loop interactive embodied reasoning for robot manipulation,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c5f9c34-de15-4842-89b6-5878d116512f · outbound

This paper cites Inductive logic programming at 30: a new introduction,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Inductive logic programming at 30: a new introduction,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d08df2ec-a440-4023-b90d-842201e0f634 · outbound

This paper cites A critical review of inductive logic programming techniques for explainable AI,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming A critical review of inductive logic programming techniques for explainable AI,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e953a4d6-a2c4-4d12-a77b-501f3c694d5b · outbound

This paper cites The birth of Prolog,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming The birth of Prolog,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3917c06-207c-44de-966a-17cf649fbc0e · outbound

This paper cites Predicate invention for bilevel planning,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Predicate invention for bilevel planning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72f4eedb-6550-4a85-8659-2cb66babbd48 · outbound

This paper cites Learning efficient abstract planning models that choose what to predict,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Learning efficient abstract planning models that choose what to predict,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2e97078-9faa-4aff-b716-7cfb008ba2a5 · outbound

This paper cites Scallop: A language for neurosymbolic programming,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Scallop: A language for neurosymbolic programming,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 033f865c-d52f-4f6e-8585-21bae0d4cfc7 · outbound

This paper cites Scallop: From probabilistic deductive databases to scalable differen- tiable reasoning,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Scallop: From probabilistic deductive databases to scalable differen- tiable reasoning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ef06148-aef4-4de8-a85f-2c726b1f34c8 · outbound

This paper cites Towards probabilistic inductive logic programming with neurosymbolic inference and relaxation,.

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming Towards probabilistic inductive logic programming with neurosymbolic inference and relaxation,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T16:45:44.659577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.