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Paper Citation Record · LEDGER

Application of LLMs to Multi-Robot Path Planning and Task Allocation

As of 7 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2507.07302.

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

pith.paper-citation-record.v1
2507.07302 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:48:17.861016Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

20 of 20 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e4d7f3dd-b69e-4a0e-9b67-5543bcd91e29 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Application of LLMs to Multi-Robot Path Planning and Task Allocation , " * write output.state after.block = add.period write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.812778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.812778Z digest=sha256:f5e870aa58b9b4b5f5c5fdcee363d3dba8b938b6ff37a6114b60485d28aadde8

Observation 77493bc3-aed9-4043-acf1-ad104c6e4e19 · outbound

This paper cites write newline.

Application of LLMs to Multi-Robot Path Planning and Task Allocation write newline

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.815901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.815901Z digest=sha256:dd4514a13801c2af376d4c38ee846822096759a94317e5db48bd2034c891405d

Observation 7a64bd95-3f08-498d-a2d6-430e667fa746 · outbound

This paper cites Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.818646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.818646Z digest=sha256:96aaf36ce2ef3ca4e679bf11180ac808a3e1aff285ae05fb1c6e4d5039b3e2f4

Observation 245e0e15-c97b-4e07-bcff-0f3d14311b93 · outbound

This paper cites V.; Christianos, F.; and Sch\"afer, L.

Application of LLMs to Multi-Robot Path Planning and Task Allocation V.; Christianos, F.; and Sch\"afer, L

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:18.032394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.821609Z digest=sha256:df3c576ed42f354459ad29e09348e4fafe602ff1a10f40cd1b6618f2d532e0ff

Observation 01cb9d14-27c9-49de-90a8-9cb70d15ebfb · outbound

This paper cites UCB Exploration via Q-Ensembles.

Application of LLMs to Multi-Robot Path Planning and Task Allocation UCB Exploration via Q-Ensembles

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.824172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.824172Z digest=sha256:1f0f408dd1299c5e793f0b649e2cbae5519d57026ed75da819f67909033890e3

Observation 4cc08db7-11d9-45d2-abbb-3d6dc4b86a2b · outbound

This paper cites an unresolved cited work.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:48:18.024359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.826995Z digest=sha256:1be8f5502caf88f66cede70bfdfd886787ad77dac479704ca7270864863aff8b

Observation 69549246-4d4f-4c4b-9981-7ba6beb04456 · outbound

This paper cites Guiding Pretraining in Reinforcement Learning with Large Language Models.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.829460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.829460Z digest=sha256:ee0c28f28b7f78ab2040fbbdf20edc5c085e7f6893da795f021d8904848e85b1

Observation 4fe27134-b04e-45e8-9654-655a59bd277b · outbound

This paper cites TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems.

Application of LLMs to Multi-Robot Path Planning and Task Allocation TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.832337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.832337Z digest=sha256:199558cd60476032b17a3cb45f875398080f5fc553f0422d3a42c1c0909adcc6

Observation aa3f1c20-bc29-467b-81d1-ee06cd52edc8 · outbound

This paper cites an unresolved cited work.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:48:18.015642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.835130Z digest=sha256:1087bb48f8a441cf8dcb729110db7e959c429fead27dea920906d04c2e2b4e0a

Observation dc315893-5824-43fa-83cd-ec954e3c8ebb · outbound

This paper cites Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.837436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.837436Z digest=sha256:129d0b775c55e41c6136aaeeb6ceab328bb8b6744ccb31bc18251fb040c1932c

Observation af8006f2-009c-4d66-9f4a-d046cf46577a · outbound

This paper cites an unresolved cited work.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:48:18.007411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.839803Z digest=sha256:90ee6160b2252b6b9ede1a4ec58df435929c18296a27f9be7466036e8ee20ba6

Observation 0bb2d081-bfcd-427c-bd56-2e4ffd958a5b · outbound

This paper cites Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:48:17.932047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.842040Z digest=sha256:05a99bb235897d791d9ba687a384675bebe385f52b4912e402fbf3fdfec58cd0

Observation d4674937-3ca0-455b-9e79-f70d2afa7f1b · outbound

This paper cites A.; and Schwing, A.

Application of LLMs to Multi-Robot Path Planning and Task Allocation A.; and Schwing, A

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:17.999265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.844601Z digest=sha256:665eb5b1899368794b4968518750008516fbd532aac4c249a71a277d036da0f5

Observation bb3689af-9d12-49ee-81be-a73de0157d67 · outbound

This paper cites an unresolved cited work.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:48:17.991759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.846758Z digest=sha256:88d71defaba1bb88c55a71f9f56642030ae8163ae34de36c9fbf8f5b1e044d2b

Observation 9ec82759-c4d1-4e63-b425-8c4b5d2cb23e · outbound

This paper cites Emergence of Grounded Compositional Language in Multi-Agent Populations.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Emergence of Grounded Compositional Language in Multi-Agent Populations

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.848779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.848779Z digest=sha256:4cac83c188ecca909740b0d55fe8276a642880396a640b40e6e9559c0e92789b

Observation 9b66142a-d4d1-4d0b-b2ef-9e2057a5e2fa · outbound

This paper cites QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Application of LLMs to Multi-Robot Path Planning and Task Allocation QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.851187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.851187Z digest=sha256:c8dba0553da98ecbb9b8575599d96416a7f95bfced22d840cc5ff8ac1925c490

Observation 5328cec8-38e2-4171-9fb3-37ad0b295641 · outbound

This paper cites an unresolved cited work.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-06T18:48:17.984095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:17.853592Z digest=sha256:973f33b2b491b0d9411ff257f4026c72c4d566dcdc3ce1a782f8de5c73ab71cc

Observation d2e22f05-25ae-4671-b40e-d5b1c81925dc · outbound

This paper cites PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change.

Application of LLMs to Multi-Robot Path Planning and Task Allocation PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.855773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.855773Z digest=sha256:5e194efd16c991f019429e4a106b47396413a25f1c7af6de1b3e5312358b38db

Observation 1e67e8ca-531e-4eb5-b3fa-69138a8e51f6 · outbound

This paper cites Large Language Models for Robotics: Opportunities, Challenges, and Perspectives.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Large Language Models for Robotics: Opportunities, Challenges, and Perspectives

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.858056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.858056Z digest=sha256:f0e2bc1ee4fdd5e0353710392a979dfe490e5be2c491e93934b2788e9c016d65

Observation f6e2855a-246c-4ab4-9b15-ec24e1f1503d · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.861016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.861016Z digest=sha256:6259f7d98b0429ce931f39d375f0b9f82e008d82e77ccd8bd97d2f053660b990

Pith citing papers

No inbound Pith citation observations are available.