Paper Citation Record · LEDGER

Understanding the Capabilities of Large Language Models for Automated Planning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.16151.

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

pith.paper-citation-record.v1
2305.16151 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:18:40.860933Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:42:10.590289Z

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 2766615b-6dc0-41c4-8baa-f943d42e4eac · inbound

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency cites this paper.

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency Understanding the Capabilities of Large Language Models for Automated Planning

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-14T18:36:18.499617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3a243ba-92e5-4125-ae1e-47291ae54617 · inbound

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems cites this paper.

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Understanding the Capabilities of Large Language Models for Automated Planning

Reference 204

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:42:10.593500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6076de6-7838-4fe8-ad31-24a72e64d59e · inbound

Planning with Transformers: Chain of Computation and Structured Context Windows cites this paper.

Planning with Transformers: Chain of Computation and Structured Context Windows Understanding the Capabilities of Large Language Models for Automated Planning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T17:18:40.860933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2239283a-12c9-4a36-a19e-0853b853a4db · inbound

RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning cites this paper.

RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning Understanding the Capabilities of Large Language Models for Automated Planning

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T16:19:09.402294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.