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Paper Citation Record · LEDGER

Prompting Robot Teams with Natural Language

As of 7 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2509.24575.

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

pith.paper-citation-record.v1
2509.24575 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:52:25.014748Z

measured 38 of 38 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:25:39.771740Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:16:11.560912Z

Reference resolution

37 of 37 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 3b01e12c-ca0e-432b-a28c-3f2c127ddcf7 · outbound

This paper cites Long-horizon Multi-robot Rearrangement Planning for Construction Assembly,.

Prompting Robot Teams with Natural Language Long-horizon Multi-robot Rearrangement Planning for Construction Assembly,

Reference 1

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source=pdf_text observed=2026-08-04T13:52:18.633825Z digest=sha256:eadc113a3c32f436ad18c6f2e3c4af48167d6b3e948c221a989262176a3e9929

Observation 116c4842-76fd-4acf-aad3-ae56295a3ed7 · outbound

This paper cites On Collaborative Robot Teams for Environmental Monitoring: A Macro- scopic Ensemble Approach,.

Prompting Robot Teams with Natural Language On Collaborative Robot Teams for Environmental Monitoring: A Macro- scopic Ensemble Approach,

Reference 2

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source=pdf_text observed=2026-08-04T13:52:18.774742Z digest=sha256:726800f238395fc864bc77be09a92e7d41b74754e1e64d0f6953cf00d2cc271c

Observation 3f99152b-1835-47de-9890-adb7134dd49f · outbound

This paper cites Multi-robot Multi-room Exploration with Geometric Cue Extraction and Circular Decomposition,.

Prompting Robot Teams with Natural Language Multi-robot Multi-room Exploration with Geometric Cue Extraction and Circular Decomposition,

Reference 3

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source=pdf_text observed=2026-08-04T13:52:18.934742Z digest=sha256:8658387cbfa920b8fa015d7248d531bbb166b181fae692833017678a7fa95b10

Observation 37baa4ba-0c62-4516-b571-0a22bb9d1451 · outbound

This paper cites Multi-Robot Target Tracking with Sensing and Communication Danger Zones.

Prompting Robot Teams with Natural Language Multi-Robot Target Tracking with Sensing and Communication Danger Zones

Reference 4

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source=pdf_text observed=2026-08-04T13:52:19.073413Z digest=sha256:1c24c1c0b65081c731c5aa280cd8f6ba5aa6219b84043695ff306eac1607333f

Observation eac65cb0-d96a-4dd8-aacf-b9be694031e6 · outbound

This paper cites A survey of robotic language grounding: tradeoffs between symbols and embeddings,.

Prompting Robot Teams with Natural Language A survey of robotic language grounding: tradeoffs between symbols and embeddings,

Reference 5

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source=pdf_text observed=2026-08-04T13:52:19.204739Z digest=sha256:40a1928e1eabb2030398eba92d6403506f4eb6e6371a3516db9e0dda0b6ecb6a

Observation 1599fd08-4666-4dc1-9317-367b14a20297 · outbound

This paper cites Corpus-based Robotics: A Route Instruction Example,.

Prompting Robot Teams with Natural Language Corpus-based Robotics: A Route Instruction Example,

Reference 6

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source=pdf_text observed=2026-08-04T13:52:19.374745Z digest=sha256:642d2ab1e3f1f909644839f63c3fd51f1254b19ef2c352a0fc29531736a4923b

Observation f9bcd6ef-454e-42ff-8c9c-e26cd6c4857d · outbound

This paper cites Toward Understanding Natural Language Directions,.

Prompting Robot Teams with Natural Language Toward Understanding Natural Language Directions,

Reference 7

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source=pdf_text observed=2026-08-04T13:52:19.644735Z digest=sha256:a86f12e91842ed217dd3afbd900a539255f048764f6ad1c67c50705213431c14

Observation 35ba6c25-4309-4a6b-9f75-30252194c1cc · outbound

This paper cites Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation.

Prompting Robot Teams with Natural Language Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation

Reference 8

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source=pdf_text observed=2026-08-04T13:52:19.744735Z digest=sha256:b4ea3260eb21902251dcf8ce4ee6a10f49260a649aa85fa71aafe10f9ad420ab

Observation 30b77d0a-6c86-48e7-b104-3d022d0dab7d · outbound

This paper cites Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication.

Prompting Robot Teams with Natural Language Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication

Reference 9

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source=pdf_text observed=2026-08-04T13:52:19.844742Z digest=sha256:4a55e954c4e5f4e7c1758a8bbbe60b75a379b96a1b74a5d8bcad4e17b865a458

Observation bf28d41e-b17e-41c9-a2b8-17b0b852f0ab · outbound

This paper cites Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems.

Prompting Robot Teams with Natural Language Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems

Reference 10

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source=pdf_text observed=2026-08-04T13:52:20.104749Z digest=sha256:5a7c853e9562db13319c63e3109064e98e72464af876dba2212a0ee7a5a07faf

Observation f7906af8-7045-4d59-b381-854d605dcc99 · outbound

This paper cites Distilling On-device Language Models for Robot Planning with Minimal Human Intervention,.

Prompting Robot Teams with Natural Language Distilling On-device Language Models for Robot Planning with Minimal Human Intervention,

Reference 11

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source=pdf_text observed=2026-08-04T13:52:20.424739Z digest=sha256:3e08fef0c1a0b1c6612e2a067e44c03ef366326a12933aba1c5d35d574ffc46f

Observation d895a930-08fc-4163-8525-c40cfd9b7f9f · outbound

This paper cites Language-Conditioned Offline RL for Multi-Robot Navigation.

Prompting Robot Teams with Natural Language Language-Conditioned Offline RL for Multi-Robot Navigation

Reference 12

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source=pdf_text observed=2026-08-04T13:52:20.624864Z digest=sha256:21c91e502dd34da94448fe4b5d633f211f66418bb425d635ff4d6565badc0331

Observation 17540eb0-8bb0-44d0-a7c4-c381392722e5 · outbound

This paper cites Learning to Discover Abstractions for LLM Reasoning,.

Prompting Robot Teams with Natural Language Learning to Discover Abstractions for LLM Reasoning,

Reference 13

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source=pdf_text observed=2026-08-04T13:52:21.064747Z digest=sha256:6fcef0b2f5c85133446ed19dd21cee4bb8983f62f9e8b2b220a35008aa69dda5

Observation 09b927b7-6421-425e-a9a4-5f447829023e · outbound

This paper cites LATMOS: Latent Automaton Task Model from Observation Sequences.

Prompting Robot Teams with Natural Language LATMOS: Latent Automaton Task Model from Observation Sequences

Reference 14

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source=pdf_text observed=2026-08-04T13:52:21.274750Z digest=sha256:ba0380bf687dfc97655686eaf38a7f6b9be225e29ca4b7d0075a625095d50eeb

Observation dfc8db22-b285-4907-8b31-866205b111bd · outbound

This paper cites The Cambridge RoboMaster: An Agile Multi-Robot Research Platform.

Prompting Robot Teams with Natural Language The Cambridge RoboMaster: An Agile Multi-Robot Research Platform

Reference 15

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source=pdf_text observed=2026-08-04T13:52:21.464738Z digest=sha256:4a5b460cc2757638e59cb2e23483e97b43baa2b5d63a096d874e0963e0be2d14

Observation 77be61af-2bac-4b63-b873-c9aad7bd96eb · outbound

This paper cites Interpretation of Spatial Language in a Map Navigation Task,.

Prompting Robot Teams with Natural Language Interpretation of Spatial Language in a Map Navigation Task,

Reference 16

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source=pdf_text observed=2026-08-04T13:52:21.615713Z digest=sha256:723f828665da5e37efefeee1be3adaf16a67614d97946d0b9ca365a555126b89

Observation 21ac4707-7441-42d7-add0-89742f1271f2 · outbound

This paper cites Walk the talk: connecting language, knowledge, and action in route instructions,.

Prompting Robot Teams with Natural Language Walk the talk: connecting language, knowledge, and action in route instructions,

Reference 17

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source=pdf_text observed=2026-08-04T13:52:21.754744Z digest=sha256:4d43acab737d852a2f441463cdc580e40ac7207853df4d4d60003d41a0bd5983

Observation c29b0441-3864-4b81-a59a-5ada2ac18bca · outbound

This paper cites An Intelligence Architecture for Grounded Language Communication with Field Robots,.

Prompting Robot Teams with Natural Language An Intelligence Architecture for Grounded Language Communication with Field Robots,

Reference 18

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source=pdf_text observed=2026-08-04T13:52:21.851959Z digest=sha256:1371e34d01c8d511ebb9d67cbfed845ab0c8ca999623c0494303d0f21714245b

Observation 70b5b38f-cc47-4416-adf0-6ac3cad7ecd0 · outbound

This paper cites Language models are few-shot learners,.

Prompting Robot Teams with Natural Language Language models are few-shot learners,

Reference 19

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Observation 9ef9e491-725b-4e17-a248-0b14d41e8a8a · outbound

This paper cites Tidybot: Personalized Robot As- sistance with Large Language Models,.

Prompting Robot Teams with Natural Language Tidybot: Personalized Robot As- sistance with Large Language Models,

Reference 20

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source=pdf_text observed=2026-08-04T13:52:22.234742Z digest=sha256:5c496d7b8a15b75d8c7845b7ac78ffb43adf649a929755519967a5f510f22fba

Observation 7aa8380a-7407-4e08-a1cd-1b77055b3948 · outbound

This paper cites SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments.

Prompting Robot Teams with Natural Language SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments

Reference 21

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source=pdf_text observed=2026-08-04T13:52:22.411452Z digest=sha256:c66721feefd23e11b90da1d7cf4f6c841f1a2768e4fac7aeba9cd1bd067147a0

Observation 83098a56-0a92-4f42-8802-4217f9543488 · outbound

This paper cites Open X-embodiment: Robotic Learning Datasets and RT-X Models: Open x-embodiment Collaboration 0,.

Prompting Robot Teams with Natural Language Open X-embodiment: Robotic Learning Datasets and RT-X Models: Open x-embodiment Collaboration 0,

Reference 22

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source=pdf_text observed=2026-08-04T13:52:22.635712Z digest=sha256:4fbd0e05665e9680e201fcacdf911c82c8a4880d118596a066787922ad4fa7bc

Observation 2d7e7e83-e385-479b-86d7-f04f8267cf6d · outbound

This paper cites Gemini Robotics: Bringing AI into the Physical World.

Prompting Robot Teams with Natural Language Gemini Robotics: Bringing AI into the Physical World

Reference 23

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source=pdf_text observed=2026-08-04T13:52:22.794746Z digest=sha256:c3df8592391a7e7469b5b96f9edab822dd9711e6497dc35cd690f8f1b2916a4d

Observation 7ac04def-1e7d-4651-9bf5-853e151251bd · outbound

This paper cites Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models.

Prompting Robot Teams with Natural Language Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models

Reference 24

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source=pdf_text observed=2026-08-04T13:52:23.009411Z digest=sha256:3fcb2a295d587174afde15ac443672b3f0075090fca07ccf9acef04fff3d06b7

Observation ee2b662e-d402-4e43-ae49-1acc74e25a2b · outbound

This paper cites SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments.

Prompting Robot Teams with Natural Language SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments

Reference 25

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source=pdf_text observed=2026-08-04T13:52:23.151187Z digest=sha256:baa99da0a490ac1885336ca60b8c1bd5745425394cdad8f81f86331e3fe294c0

Observation 00ff3600-1cb9-4d2e-93f7-85f509776019 · outbound

This paper cites HELM: Human-Preferred Exploration with Language Models.

Prompting Robot Teams with Natural Language HELM: Human-Preferred Exploration with Language Models

Reference 26

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source=pdf_text observed=2026-08-04T13:52:23.304735Z digest=sha256:4c1f26e05380be85f9b0b2f49bbb82e30eed60c7887f8220a1e74317ce3cbeed

Observation f538087f-e9d4-4ac8-b601-f9f35ea31906 · outbound

This paper cites Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration.

Prompting Robot Teams with Natural Language Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

Reference 27

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source=pdf_text observed=2026-08-04T13:52:23.440920Z digest=sha256:b61aa56bc815726cc373e16f55f1612fff085a2705fed522462a5d95119eceae

Observation 1b58ece4-738f-437f-84fb-5f91822c8284 · outbound

This paper cites Large Language Model Guided Reinforcement Learning Based Six-Degree-of-Freedom Flight Control,.

Prompting Robot Teams with Natural Language Large Language Model Guided Reinforcement Learning Based Six-Degree-of-Freedom Flight Control,

Reference 28

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source=pdf_text observed=2026-08-04T13:52:23.633012Z digest=sha256:431985380851ed4658b312a934f6cac1ee3dcb19161b3c42bea7887f02000186

Observation 3b88f2ef-8c0a-424b-8142-d64baf09e143 · outbound

This paper cites Air-Ground Collaboration for Language-Specified Missions in Unknown Environments.

Prompting Robot Teams with Natural Language Air-Ground Collaboration for Language-Specified Missions in Unknown Environments

Reference 29

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source=pdf_text observed=2026-08-04T13:52:23.797367Z digest=sha256:174661d236dc4226caffcb74d849736afb53dd4e19a0f5415281ed53ef1b592e

Observation 21f07030-6024-4239-b486-fd44de865da7 · outbound

This paper cites ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models.

Prompting Robot Teams with Natural Language ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models

Reference 30

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source=pdf_text observed=2026-08-04T13:52:23.863167Z digest=sha256:128ff933bd7bce01e500157fa4ca4363513896df8195d03bfa9afa5c8b20fa19

Observation d1643066-f815-4de3-bcce-ea92e4c6c988 · outbound

This paper cites LUMOS: Language-Conditioned Imitation Learning with World Models.

Prompting Robot Teams with Natural Language LUMOS: Language-Conditioned Imitation Learning with World Models

Reference 31

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source=pdf_text observed=2026-08-04T13:52:24.041112Z digest=sha256:b4e9f5279e48910d7fa23b23a7d6ed3766458f20739fd6d0191a5dbe1ba7858e

Observation 7d20c536-29c4-4018-92b0-18e5ccc1e8ef · outbound

This paper cites MARLIN: Multi-Agent Reinforcement Learning Guided by Language-Based Inter-Robot Negotiation.

Prompting Robot Teams with Natural Language MARLIN: Multi-Agent Reinforcement Learning Guided by Language-Based Inter-Robot Negotiation

Reference 32

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source=pdf_text observed=2026-08-04T13:52:24.201572Z digest=sha256:c7aefc9c1a3592b0b6701375d9b1e2f6e9fcf47e4712bf20745cc942a7610b97

Observation f48f3795-591a-49a6-8e3f-bb6c36cc5bb1 · outbound

This paper cites Connecting weighted automata, tensor networks and recurrent neural networks through spectral learn- ing,.

Prompting Robot Teams with Natural Language Connecting weighted automata, tensor networks and recurrent neural networks through spectral learn- ing,

Reference 33

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source=pdf_text observed=2026-08-04T13:52:24.365487Z digest=sha256:0782e0ea40511cbb840b20c3d806a9d712001ece93f6c043f49312a3250bd88b

Observation 906d5629-2b19-4973-a8f0-512c4ba4fc02 · outbound

This paper cites Optimal Scene Graph Planning with Large Language Model Guidance,.

Prompting Robot Teams with Natural Language Optimal Scene Graph Planning with Large Language Model Guidance,

Reference 34

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source=pdf_text observed=2026-08-04T13:52:24.542177Z digest=sha256:577e48eb4575b50490847a22d27da452ccc31f42a6d47dffbac80ec16f0ab212

Observation 97b2331b-a72a-44f8-abcc-51e862c81d61 · outbound

This paper cites AutoTAMP: Autoregressive task and motion planning with llms as translators and checkers,.

Prompting Robot Teams with Natural Language AutoTAMP: Autoregressive task and motion planning with llms as translators and checkers,

Reference 35

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source=pdf_text observed=2026-08-04T13:52:24.707562Z digest=sha256:472aceba64aa72f2e2b11c2945aab54623bdc97e0e8a1a47face08ca5cdbd803

Observation 7574a889-ebc4-4b62-9796-423ce18d3f59 · outbound

This paper cites Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs.

Prompting Robot Teams with Natural Language Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs

Reference 36

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source=pdf_text observed=2026-08-04T13:52:24.838819Z digest=sha256:8e8a4a9e98fc95e2c65b315c265779665967c7936306225705f42b3e4eb22e48

Observation 04800163-95f2-40c6-8822-fafd796b8690 · outbound

This paper cites The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,.

Prompting Robot Teams with Natural Language The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,

Reference 37

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source=pdf_text observed=2026-08-04T13:52:25.014748Z digest=sha256:0cb1ce661359d20b50c600cfba3142283717398967e4f59d82c3bf45782d00c6

Pith citing papers

Observation 2b5194a9-b782-405b-88f2-92d4164699ae · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details Prompting Robot Teams with Natural Language

Reference 104

Resolution
verified exact
local_arxiv, observed 2026-08-05T15:28:39.046316Z

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=2026-08-05T15:25:39.771740Z digest=sha256:43852a84732c199b5c1e78012b111eeb063f7f19bb2bd73d791f7036583c1a2a