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Paper Citation Record · LEDGER

Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2211.03267.

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

pith.paper-citation-record.v1
2211.03267 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 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:05:50.685762Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:18:43.821443Z

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 102fb591-67d6-4b2b-bc21-f7c12a2f7b38 · inbound

Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few Examples cites this paper.

Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few Examples Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T05:42:03.756539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:42:03.756539Z digest=sha256:9e4e589272275f06301ee84d04ca32722b1b9f2946c67c3fa006983f5b94edc5

Observation 32e58f7d-9f50-4343-8062-0c2d39d61929 · inbound

DSADF: Thinking Fast and Slow for Decision Making cites this paper.

DSADF: Thinking Fast and Slow for Decision Making Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-15T22:05:50.685762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:05:50.685762Z digest=sha256:d1b36bed216b7bbd4ff85b4cf09ba78c7dbfb268293e3a69b75e738c45660960

Observation c232ffcf-4ea1-4dbb-b24b-a696f8fe50d5 · inbound

Efficient and Generalizable Environmental Understanding for Visual Navigation cites this paper.

Efficient and Generalizable Environmental Understanding for Visual Navigation Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T19:38:59.493625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:38:59.493625Z digest=sha256:1df6a04e030d32215b0add7c25f378233ba06efd35111f5bc41253ba443fd75b

Observation f1f7d24b-6782-46e0-bfd7-41389ec7254a · inbound

ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation cites this paper.

ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:10:26.305694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T13:09:32.132811Z digest=sha256:07fa50da30bc5057efcf695cbd8322839cf8733e8f15029b8cf33bdbe67d303a

Observation 2026526f-29fd-4498-ad29-ebd53f07af88 · inbound

RePlan-Bot: Multi-Level Replanning for Embodied Instruction Following cites this paper.

RePlan-Bot: Multi-Level Replanning for Embodied Instruction Following Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-06-29T21:23:58.809665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T21:21:30.990215Z digest=sha256:95952db59be0db0065996d973b9eaff0964d7cb5927d316cab76f76ad32afcff

Observation 3fcc50e3-5c4a-43a3-b01d-0c9b07f7d9e7 · inbound

Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation cites this paper.

Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 237

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:18:43.824762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-27T04:19:26.332718Z digest=sha256:32f72d1bab1ce256f23b5671c6e9052fd2e44e6855ca9815c57b8b1dea1cf57e

Observation 0b030c75-cc13-410b-b866-52d33f8a18f9 · inbound

Qwen-Audio-VAE Technical Report cites this paper.

Qwen-Audio-VAE Technical Report Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 244

Resolution
unresolved
no resolver link, observed 2026-07-14T03:31:19.309532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T03:31:19.309532Z digest=sha256:521afa9c2385e5f70ce8ca4bc8674665761b9c68280b05e165aa7886ba6432cb

Observation 4cfc3c25-8ebc-4d17-80c5-34f46da38fe3 · inbound

GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning cites this paper.

GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-12T00:47:48.562595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:47:48.562595Z digest=sha256:9387e2eb4300d9f9c82d55f135af876845ea162e1f9e972aa54e01e85e4a7b24