Paper Citation Record · LEDGER

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2512.01039.

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

pith.paper-citation-record.v1
2512.01039 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:21:36.407060Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy0
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8216d598-8a57-48ed-ae48-ae1e280ca7d7 · outbound

This paper cites Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.335119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20add637-256d-4981-9a22-d17ebf81041d · outbound

This paper cites LLM Inference Serving: Survey of Recent Advances and Opportunities.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI LLM Inference Serving: Survey of Recent Advances and Opportunities

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.338737Z

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Observation f6917c7a-b1fd-4f75-bc58-246354d94e56 · outbound

This paper cites Split computing: DNN Inference Partition with Load Balancing in IoT-Edge Platform for Beyond 5G,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Split computing: DNN Inference Partition with Load Balancing in IoT-Edge Platform for Beyond 5G,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.342033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc4a42ff-8e4c-4383-9c99-e339d57c5b9e · outbound

This paper cites Split Computing: Dynamic Partitioning and Reliable Communications in IoT-Edge for 6G Vision,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Split Computing: Dynamic Partitioning and Reliable Communications in IoT-Edge for 6G Vision,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.344746Z

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Unavailable: canonical work link unavailable.

Observation ac70b13b-be07-4bf7-bcf0-08ec743d5c72 · outbound

This paper cites Kubernetes and Docker Load Balancing: State-of-the-Art Techniques and Challenges,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Kubernetes and Docker Load Balancing: State-of-the-Art Techniques and Challenges,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.347755Z

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Unavailable: canonical work link unavailable.

Observation 61eb1354-c5ce-4133-a0a0-1283f41d4443 · outbound

This paper cites Privacy-Preserving Machine Learning: Methods, Challenges and Directions.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.350529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1e0e05a-7238-479b-9caf-ea5c441c5c52 · outbound

This paper cites Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.353724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b48f33f7-bf1c-4604-b85d-b5c34c413b8f · outbound

This paper cites Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Kubernetes Scheduling: Taxonomy, Ongoing Issues and Challenges,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.356261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00f2ef55-9fe8-4264-96dc-1c6bd9358894 · outbound

This paper cites Efficient Training of Large Language Models on Distributed Infrastructures: A Survey.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Efficient Training of Large Language Models on Distributed Infrastructures: A Survey

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.358858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e43b617-f1f4-435b-9ea9-c0f8388b17c4 · outbound

This paper cites A Comprehensive Study on Quantiza- tion Techniques for Large Language Models,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI A Comprehensive Study on Quantiza- tion Techniques for Large Language Models,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.361753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d450304e-ad3b-43a8-b7dd-239e9f803329 · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI A Survey on Efficient Inference for Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.364206Z

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Observation ddfb6079-cf7d-4805-b586-f3779c248fea · outbound

This paper cites Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.367075Z

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Observation 38ca4314-a64e-4fdc-875a-5d61552b3ab3 · outbound

This paper cites Dis- tributed Inference Acceleration with Adaptive DNN Partitioning and Offloading,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Dis- tributed Inference Acceleration with Adaptive DNN Partitioning and Offloading,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.369792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d82a2e8-586a-417b-aa5e-aae6911767d5 · outbound

This paper cites Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.372302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9a1dc9c-d41d-48e3-82b1-a5021d912b48 · outbound

This paper cites EdgeShard: Efficient LLM Inference via Collaborative Edge Computing,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI EdgeShard: Efficient LLM Inference via Collaborative Edge Computing,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.375195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d76689c-782e-481a-9fca-e00056a938c1 · outbound

This paper cites QoS-Aware Edge AI Placement and Scheduling with Multiple Implementations in FaaS-Based Edge Computing,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI QoS-Aware Edge AI Placement and Scheduling with Multiple Implementations in FaaS-Based Edge Computing,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.377867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6410cd9d-fef6-41ee-8347-2c38598d79ec · outbound

This paper cites Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.380653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93df8240-a0b4-4437-a2c2-d5f4f7d698ce · outbound

This paper cites Distributing Deep Neural Networks with Containerized Partitions at the Edge,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Distributing Deep Neural Networks with Containerized Partitions at the Edge,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.383418Z

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Unavailable: canonical work link unavailable.

Observation 1afb0cd4-911c-4c29-b7e1-cfb71fe3d497 · outbound

This paper cites Learning- Aided Computation Offloading for Trusted Collaborative Mobile Edge Computing,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Learning- Aided Computation Offloading for Trusted Collaborative Mobile Edge Computing,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.385794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ea0520-2e0a-4513-8cff-b3550b4a0602 · outbound

This paper cites Optimal AI Model Splitting and Resource Allocation for Device-Edge Co-Inference in Multi-User Wireless Sensing Systems,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Optimal AI Model Splitting and Resource Allocation for Device-Edge Co-Inference in Multi-User Wireless Sensing Systems,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.388327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb3fa16-d3d5-4a07-aa06-0e565dd6ab42 · outbound

This paper cites Optimum Splitting Comput- ing for DNN Training Through Next Generation Smart Networks: A Multi-Tier Deep Reinforcement Learning Approach,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Optimum Splitting Comput- ing for DNN Training Through Next Generation Smart Networks: A Multi-Tier Deep Reinforcement Learning Approach,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.390708Z

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Observation cff130e0-866d-4c88-80dd-7ad0979bab07 · outbound

This paper cites R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models,.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.393109Z

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Observation a710f362-3967-4539-899e-653a7358a0ee · outbound

This paper cites SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.395661Z

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Observation 376262f6-7131-46cd-a47c-c6aa1471197a · outbound

This paper cites AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.398905Z

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Unavailable: canonical work link unavailable.

Observation 6db1e5f4-2a7d-4ed1-88e6-47a0a5ff337a · outbound

This paper cites SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.401798Z

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Observation fd035a8c-59be-4f59-b43c-7d34c5800149 · outbound

This paper cites Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.404378Z

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Observation 26a4e151-b4d4-48f7-a68b-8e42880f54c9 · outbound

This paper cites The Llama 3 Herd of Models.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI The Llama 3 Herd of Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.407060Z

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Pith citing papers

No inbound Pith citation observations are available.