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Paper Citation Record · LEDGER

Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2305.14909.

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

pith.paper-citation-record.v1
2305.14909 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:52:17.053054Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T22:49:01.033282Z

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 1c505bae-6d12-4ed9-b6e8-433d921c858c · inbound

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

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:36:18.485861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-14T18:36:18.342052Z digest=sha256:1c06290107c5b38cdb946585abcc1a23849cb385d24d4c0e63143038001ee67e

Observation a81591ca-d1fb-4c88-a772-21fbf6585169 · inbound

Cognitive Architectures for Language Agents cites this paper.

Cognitive Architectures for Language Agents Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-16T19:33:44.345947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-16T19:33:44.146134Z digest=sha256:5e9c9784156e9299ec4092467f589ba1e536a347c9d6bc18f314ae79e6dea13b

Observation 3c91e336-c045-4bdc-9148-47e34d00fe8f · inbound

Understanding the planning of LLM agents: A survey cites this paper.

Understanding the planning of LLM agents: A survey Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-13T18:12:57.676862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-13T18:12:57.568144Z digest=sha256:4ed9be9674d76c5f83b3b724cfb8a0d0778e2b7222cd20d412ee8184fd56e7ee

Observation e63b0025-8015-4d19-b74b-7368d533e89e · inbound

Query-Efficient Planning with Language Models cites this paper.

Query-Efficient Planning with Language Models Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T20:04:07.399350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:04:07.399350Z digest=sha256:5015a41c7c8c2b9fc4495cc676604dd0d3c9967530efa4fd23c39190ce9a9fdb

Observation fb6f9b55-badf-4789-8334-9ce7ec8f17f8 · inbound

Robotouille: An Asynchronous Planning Benchmark for LLM Agents cites this paper.

Robotouille: An Asynchronous Planning Benchmark for LLM Agents Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-09T00:46:44.986471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:46:44.986471Z digest=sha256:2839befebdcd4c4fc8cdc6cdee9558df771dace4c067e4ef2dbf254ebf82b3a9

Observation 4193a2af-f715-461e-90b6-acde12148f10 · inbound

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity cites this paper.

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 2022

Resolution
malformed identifier
no resolver link, observed 2026-08-08T14:11:11.412796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:11:11.412796Z digest=sha256:56159d755be9c3bb4760935af7298086b748397d889c0e869ecc6571dbe2dbcc

Observation 85b4eed8-c37a-430f-9529-b869a41d14fa · inbound

Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation cites this paper.

Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:31:24.738448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T13:30:14.462351Z digest=sha256:44c933704dcccfd0dca0de6ee189c76e7b360bd69a1f0dcdb7aedcd07e7ddfb7

Observation 31849865-019b-4c15-840e-aef11c777736 · inbound

Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation cites this paper.

Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-15T15:52:17.053054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:52:17.053054Z digest=sha256:1a49ec215d76bbfa145d0dd6f6c4dce2fc76e2b6c4c820d082b4ea7dfce6ebee

Observation 599e8cc0-c24b-4331-a2a6-6b81ffd765de · inbound

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents cites this paper.

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 52

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T19:23:54.370871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-29T04:46:11.090693Z digest=sha256:465d5466a9a5e26148d85ec3cb146bffe803a55368994c843df9bdda3606359d

Observation 82b68e5a-86e8-4fea-bc85-f8375a87086a · inbound

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents cites this paper.

Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

Reference 52

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T22:49:01.036430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-07-03T22:41:41.912274Z digest=sha256:62167d9ab34b6f90d26b6735fd4daa2a9ad9e52e6c20cdcd5d3d3a7bf77f9cbb