Paper Citation Record · LEDGER

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.09581.

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

pith.paper-citation-record.v1
2506.09581 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:47:16.451628Z

measured 38 of 38 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 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

38 of 38 outbound references displayed

verified exact1
verified fuzzy23
unresolved14
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f056c87a-eaa6-46dc-89c4-fde8a6d37d5b · outbound

This paper cites GPT-4 Technical Report.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.346418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b834b67-ceae-4858-8a3b-85377f972b77 · outbound

This paper cites Gonz´ alez-Santamarta.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Gonz´ alez-Santamarta

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.834831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 19f2a107-f8df-434a-81bd-ddd652205fe5 · outbound

This paper cites Robot operating system 2: Design, architecture, and uses in the wild.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Robot operating system 2: Design, architecture, and uses in the wild

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.824537Z

Source-reported events for the cited work

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Observation 040d2294-9bc7-4ebf-b1d7-f1fde166273d · outbound

This paper cites https://github.com/ggerganov/llama.cpp, 2023.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities https://github.com/ggerganov/llama.cpp, 2023

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.814215Z

Source-reported events for the cited work

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Observation 5be82216-0271-4a29-8824-90ba90631703 · outbound

This paper cites Deep learning with low precision by half-wave gaussian quantization.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Deep learning with low precision by half-wave gaussian quantization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.804227Z

Source-reported events for the cited work

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Observation c5b4ab9e-93ce-48c5-8aef-6871c5f386e4 · outbound

This paper cites Fixed point quantization of deep convolutional networks.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Fixed point quantization of deep convolutional networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.794375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1b3ffef-71cc-4b2a-b09b-6a9bbeeb543e · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 7

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Source-reported events for the cited work

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Observation c4fefd7f-e5af-477c-8875-c5a13c3b0edb · outbound

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Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:47:16.784950Z

Source-reported events for the cited work

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Observation 450d7c1f-8ef7-4165-853f-f5105d7e84c2 · outbound

This paper cites an unresolved cited work.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:47:16.774953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 77ed2560-9e08-402d-9886-7c3905624f29 · outbound

This paper cites Chatgpt for robotics: Design principles and model abilities.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Chatgpt for robotics: Design principles and model abilities

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.374058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bc1b5a5-02ec-41db-9445-f458284fc37e · outbound

This paper cites ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:47:16.541003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a1ba0d65-2603-4e68-8115-7916c196721b · outbound

This paper cites Progprompt: pro- gram generation for situated robot task planning using large language models.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Progprompt: pro- gram generation for situated robot task planning using large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.758941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c03abbf0-569e-43d6-9979-0f6f732a5624 · outbound

This paper cites SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.382147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3110b93-100f-4eda-a9d3-0d5dfba7cf0b · outbound

This paper cites https://github.com/Auromix/ROS-LLM, Apr 2024.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities https://github.com/Auromix/ROS-LLM, Apr 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.748955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4eb9af3d-e505-4847-ab0d-d746935f7b6b · outbound

This paper cites Rosgpt: Next-generation human-robot interaction with chatgpt and ros.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Rosgpt: Next-generation human-robot interaction with chatgpt and ros

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.739052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e4a21b23-0be9-4b14-92d8-1e7cb78ac2b0 · outbound

This paper cites Grammatical evolution.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Grammatical evolution

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.728469Z

Source-reported events for the cited work

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Observation ecef47fd-9e26-4dda-a6ac-44a85a465cf8 · outbound

This paper cites LangChain.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities LangChain

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.718510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b44f7f1b-920b-4d14-8cd4-38ec8c474d77 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32149e0e-648b-4c6e-92d8-92db4b364802 · outbound

This paper cites Llms4ol: Large language models for ontology learning, 2023.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Llms4ol: Large language models for ontology learning, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.709444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fe89a0c4-47dc-49ee-a142-9668930bf8ef · outbound

This paper cites Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.402380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d311857-4df0-40fe-9ae2-986eb21d4620 · outbound

This paper cites A comprehensive evaluation of inductive reasoning capabilities and problem solving in large language models.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities A comprehensive evaluation of inductive reasoning capabilities and problem solving in large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.699538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 20de49a8-5a98-440a-93c7-91b2f5eee495 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Training Verifiers to Solve Math Word Problems

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41b7f2bc-d0d9-4c7d-9e94-dbc79000102b · outbound

This paper cites Wino- grande: An adversarial winograd schema challenge at scale.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Wino- grande: An adversarial winograd schema challenge at scale

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.690308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eb89caf8-9d9a-4434-9472-0d9b036b8713 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Chain-of-thought prompting elicits reasoning in large language models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.413442Z

Source-reported events for the cited work

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Observation f5ca2799-f947-40f9-b153-d31a9e92437f · outbound

This paper cites Large language models are zero-shot reasoners, 2023.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Large language models are zero-shot reasoners, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.415922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afc02ab2-556d-44fa-9f56-50f8d31d5cac · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 26

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Observation 9c1a23a5-bff6-45b1-a460-12f6957f1ea7 · outbound

This paper cites Graph of Thoughts: Solving Elaborate Problems with Large Language Models.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Graph of Thoughts: Solving Elaborate Problems with Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:16.421238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d1ac0e1-e72c-4427-913a-c48290612536 · outbound

This paper cites Gonz´ alez-Santamarta, Francisco J.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Gonz´ alez-Santamarta, Francisco J

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.667965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ae207de7-45ea-4bf0-bb0f-9446d89e29a0 · outbound

This paper cites Gonz´ alez-Santamarta, Francisco J.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Gonz´ alez-Santamarta, Francisco J

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.658268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8fb39a3e-79da-4555-87ef-fac26a19a683 · outbound

This paper cites PDDL2.1: An extension to PDDL for expressing temporal planning domains.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities PDDL2.1: An extension to PDDL for expressing temporal planning domains

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.648624Z

Source-reported events for the cited work

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Observation cb46fa26-3de2-4409-b926-080f62cf4fa9 · outbound

This paper cites Gonz´ alez-Santamarta, Francisco J.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Gonz´ alez-Santamarta, Francisco J

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.638882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 059a90dd-c2cd-4c5c-843e-fc4b7bb862e8 · outbound

This paper cites Forward-chaining partial-order planning.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Forward-chaining partial-order planning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.622342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f5b8d8ea-9a1e-4258-b514-2affccc33017 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.611808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5727aafe-5dbf-4d4f-9483-0ccdb0d83eff · outbound

This paper cites Structured prompt interrogation and recursive extraction of semantics (spires): A method for populating knowledge bases using zero-shot learning.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Structured prompt interrogation and recursive extraction of semantics (spires): A method for populating knowledge bases using zero-shot learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.600952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d5a93cba-548f-417e-8bd2-3360b3056cb7 · outbound

This paper cites Towards safe and trustworthy social robots: ethical challenges and practical issues.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Towards safe and trustworthy social robots: ethical challenges and practical issues

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.591160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6db744a8-2db3-4699-8710-a141343ee5bd · outbound

This paper cites Using large language models for interpreting autonomous robots behaviors.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Using large language models for interpreting autonomous robots behaviors

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.580979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6284879c-62e6-4007-85b1-0f4f84df41c8 · outbound

This paper cites Gonz´ alez-Santamarta, ´Angel M.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Gonz´ alez-Santamarta, ´Angel M

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:47:16.570849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fb904362-6d97-4578-9f9d-41343314f291 · outbound

This paper cites an unresolved cited work.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities Unresolved cited work

Reference 2022

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.