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Paper Citation Record · LEDGER

LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2212.04088.

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

pith.paper-citation-record.v1
2212.04088 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:47:57.785247Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

19
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5095c38e-0350-4cca-9b04-ef83f9fd95a0 · inbound

Mind2Web: Towards a Generalist Agent for the Web cites this paper.

Mind2Web: Towards a Generalist Agent for the Web LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-15T20:05:16.162458Z

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.

source=pdf_text observed=2026-05-15T20:05:15.992207Z digest=sha256:7646dab8b9c4be49f9c34bde4981f05b11e34f3ee8d28af93b73b9b239162094

Observation dc469397-ebed-4a85-925f-f800ae5c1de9 · inbound

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models cites this paper.

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-13T08:57:22.524669Z

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.

source=pdf_text observed=2026-05-13T08:57:22.299028Z digest=sha256:cbbe2ed13d833f4783b7c8b44667f48b4be8362bf0ca945bdd1d3166c9254360

Observation ffa4d2a7-64f3-44f7-a776-18cf0b577632 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 243

Resolution
verified exact
arxiv_id, observed 2026-05-19T20:28:39.094465Z

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.

source=pdf_text observed=2026-05-19T20:28:38.900026Z digest=sha256:17ee2d7ca8780f8fa5ca20c793f822b7a2fe658c83b1f243f25a291d182ed446

Observation dd619400-f4b2-42e6-8d9d-aced569e4b1d · inbound

The Rise and Potential of Large Language Model Based Agents: A Survey cites this paper.

The Rise and Potential of Large Language Model Based Agents: A Survey LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 102

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:47:51.070888Z

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.

source=pdf_text observed=2026-05-11T10:47:44.152066Z digest=sha256:d212a7d3498caeaef3915512e5cb46a4196b007a96a459e73a02a2c36bd1a795

Observation 28fd0481-a0eb-401b-9fd8-ab758bc5dda7 · inbound

GPT-Driver: Learning to Drive with GPT cites this paper.

GPT-Driver: Learning to Drive with GPT LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T15:05:31.990057Z

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.

source=pdf_text observed=2026-05-15T15:05:31.928650Z digest=sha256:c87fb6cd18e47d81ab13c560359ffc63d9597646454464f32dec23847fca8b31

Observation 1aa1f574-a83d-464d-a69e-bb76b7f42c34 · inbound

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success cites this paper.

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:35:32.824770Z

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.

source=pdf_text observed=2026-05-11T04:35:31.914360Z digest=sha256:09df61e9088d2748ecd7bb3d65eba02e85f498d2458c35b77a7bc7640b5358ab

Observation d1614318-7ab2-4fb8-ac13-24ec2269700c · inbound

CoDec: Prefix-Shared Decoding Kernel for LLMs cites this paper.

CoDec: Prefix-Shared Decoding Kernel for LLMs LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T14:47:57.785247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:47:57.785247Z digest=sha256:48004272b5b25dfdf217938122bf1d056998a41b020d094a8ec59c279ff2666f

Observation b11bbddd-215b-449a-951e-b61d403315af · inbound

Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation cites this paper.

Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T04:14:28.908967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:14:28.908967Z digest=sha256:1db70db671133b6d1246870f6f03a2aa68b5f9a277468adda33bfbe9b2f92daa

Observation e829214c-6955-4ef7-a539-c8a444a7be2b · inbound

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning cites this paper.

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-04T09:33:40.714342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T09:33:40.714342Z digest=sha256:ea67fb07882cca04792c76379a963a05be82c9b80e81a5648dd2a577d2929972

Observation 0636d6ee-ee7c-4d80-a8de-88e48fcbd883 · inbound

EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems cites this paper.

EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:41:06.954941Z

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.

source=pdf_text observed=2026-05-10T15:21:20.231759Z digest=sha256:770a95d5e000613f2af26597f6172d9a05df7c10e194dce822d463e1457dffae

Observation 8c826faf-dd44-4f17-9951-296b6d8a8a42 · inbound

PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution cites this paper.

PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 101

Resolution
unresolved
no resolver link, observed 2026-08-01T20:28:23.965044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T20:28:23.965044Z digest=sha256:5c36c80513d60232119c1ad5d01ba1a5ff66624f2cdc74748f7bcc59ef048d60

Observation 707b379c-65e8-4a20-a3c1-9ebda676227e · inbound

RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control cites this paper.

RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-01T16:35:13.351713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:35:13.351713Z digest=sha256:452d64eb31d7c70b719ca521099531ef2ad22d572591b0defc856bc65adef4c2

Observation a8271b6b-11d7-4a2c-a6be-02c97256f18e · inbound

Agentic Re-Casting using Agentic Re-Simulations cites this paper.

Agentic Re-Casting using Agentic Re-Simulations LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-01T04:29:05.229358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T04:29:05.229358Z digest=sha256:6868f20f32b164bebb032f45afbe25dbf09ea1befded95e684df9cdc6e95263d

Observation 392717c3-9d6d-4984-b7b1-7939cc74d2ac · 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 LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 237

Resolution
unresolved
no resolver link, observed 2026-08-04T19:45:35.242872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:45:35.242872Z digest=sha256:7c10240f9c1663e58936968c4f8c70ba72eb2b5955cd33f51e044c90a8d8b169