Paper Citation Record · LEDGER

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM

As of 23 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2505.08492.

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

pith.paper-citation-record.v1
2505.08492 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:56:57.287310Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T19:40:14.025402Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:37:24.981875Z

Reference resolution

34 of 34 outbound references displayed

verified exact4
verified fuzzy14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 23a88693-a611-410b-94d8-1f2e2eb628a4 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 5bacea19-a4ee-48d2-857f-dcd0e2d02be6 · outbound

This paper cites Transactions on Machine Learning Research (2024).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Transactions on Machine Learning Research (2024)

Reference 2

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Observation c0c09a6a-4492-4e23-8364-a79239d696a7 · outbound

This paper cites Advances in neural information processing systems33, 1877–1901 (2020).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Advances in neural information processing systems33, 1877–1901 (2020)

Reference 3

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Observation c62f883d-08ae-46b9-bfcd-5cbba036d27a · outbound

This paper cites Robotics and Autonomous Systems 109, 139–155 (2018).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Robotics and Autonomous Systems 109, 139–155 (2018)

Reference 4

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Observation 4186f358-2a6c-48a3-ac83-873fbc30487d · outbound

This paper cites Frontiers in Neu- rorobotics 18 (Jun 2024).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Frontiers in Neu- rorobotics 18 (Jun 2024)

Reference 5

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Observation 4497e1e1-3941-423e-a9b6-51ed052b49ce · outbound

This paper cites Data in Brief22, 119–117 (2019).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Data in Brief22, 119–117 (2019)

Reference 6

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Observation 74915299-8aa8-49c3-bcc3-17bb64bae878 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Evaluating Large Language Models Trained on Code

Reference 7

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This paper cites IEEE Transactions on Robotics 37(2), 567–586 (2021).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM IEEE Transactions on Robotics 37(2), 567–586 (2021)

Reference 8

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Observation 86ada017-e7b7-4625-be50-b713a6e92ec1 · outbound

This paper cites Mecha- tronics 51, 97–115 (2018).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Mecha- tronics 51, 97–115 (2018)

Reference 9

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Observation 71062560-d51d-4579-94d9-5db47dbfd191 · outbound

This paper cites Autonomous Robots 47(8), 981–997 (Aug 2023).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Autonomous Robots 47(8), 981–997 (Aug 2023)

Reference 10

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Observation 0a06c26c-819d-448a-abe2-6193275250f6 · outbound

This paper cites Neurosymbolic AI: The 3rd Wave.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Neurosymbolic AI: The 3rd Wave

Reference 11

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This paper cites In: Proc.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: Proc

Reference 12

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Observation fcb78362-4d21-4aa4-9207-6ab6454b7442 · outbound

This paper cites Robotica 42(4), 1094–1112 (Feb 2024).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Robotica 42(4), 1094–1112 (Feb 2024)

Reference 13

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Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Unresolved cited work

Reference 14

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Observation 002f6fe1-58ca-4909-a2f3-bcd72b5a268c · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Measuring Mathematical Problem Solving With the MATH Dataset

Reference 15

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Observation 50782487-c5ff-42d8-abcc-2850669b2af2 · outbound

This paper cites In: 2010 ieee/rsj international conference on intelligent robots and systems.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: 2010 ieee/rsj international conference on intelligent robots and systems

Reference 16

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Observation c2f9f0f6-d0cc-4219-8c8b-d8e1e0eb2259 · outbound

This paper cites IEEE Transactions on Human-Machine Systems49(3), 209–218 (2019).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM IEEE Transactions on Human-Machine Systems49(3), 209–218 (2019)

Reference 17

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Observation 31853e1e-5780-433f-902e-8d8664350b37 · outbound

This paper cites In: 16th IEEE International Conference on Tools with Artificial Intelligence.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: 16th IEEE International Conference on Tools with Artificial Intelligence

Reference 18

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This paper cites In: 2024 IEEE Interna- tional Conference on Robotics and Automation (ICRA).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: 2024 IEEE Interna- tional Conference on Robotics and Automation (ICRA)

Reference 19

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Observation 0f35d2ca-0e4b-48d7-8437-40aa48e00c8b · outbound

This paper cites PDDLFuse: A Tool for Generating Diverse Planning Domains.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM PDDLFuse: A Tool for Generating Diverse Planning Domains

Reference 20

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Observation 9bc86c3d-8453-4fe8-8df2-53fd5a04b061 · outbound

This paper cites In: Proceedings of the International Conference on Automated Planning and Schedul- ing.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: Proceedings of the International Conference on Automated Planning and Schedul- ing

Reference 21

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Observation 245b6d38-b9fd-4480-becf-27d9c5b9d15a · outbound

This paper cites In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=iOc57X9KM54.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=iOc57X9KM54

Reference 22

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Observation 7522dcf0-ca2f-4f27-bd89-0a169d2dcfb5 · outbound

This paper cites In: 8th International Conference on Intelligent Autonomous Systems (2004).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: 8th International Conference on Intelligent Autonomous Systems (2004)

Reference 23

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Observation 2250ee96-255c-4b6e-9a7e-584de73df537 · outbound

This paper cites Intelligent Service Robotics13, 439–457 (2020).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Intelligent Service Robotics13, 439–457 (2020)

Reference 24

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This paper cites Plansformer: Generating Symbolic Plans using Transformers.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Plansformer: Generating Symbolic Plans using Transformers

Reference 25

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This paper cites Proceedings of the International Conference on Automated Planning and Scheduling 34, 432–444 (May 2024).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Proceedings of the International Conference on Automated Planning and Scheduling 34, 432–444 (May 2024)

Reference 26

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This paper cites Proceedings of the International Conference on Automated Planning and Scheduling 34(1), 500–508 (May 2024).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Proceedings of the International Conference on Automated Planning and Scheduling 34(1), 500–508 (May 2024)

Reference 27

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This paper cites LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Reference 28

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This paper cites arXiv preprint arXiv:2503.18971 (2025).

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM arXiv preprint arXiv:2503.18971 (2025)

Reference 29

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Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Unresolved cited work

Reference 30

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 31

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This paper cites In: NeurIPS 2022 Foundation Models for Decision Making Workshop (2022), https://openreview.net/forum?id=wUU-7XTL5XO.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM In: NeurIPS 2022 Foundation Models for Decision Making Workshop (2022), https://openreview.net/forum?id=wUU-7XTL5XO

Reference 32

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This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 33

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This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 34

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Pith citing papers

Observation 8348de7a-2be3-4e04-8a9d-6acde36e93f3 · inbound

Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins cites this paper.

Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM

Reference 25

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