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Paper Citation Record · LEDGER

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks

As of 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.02522.

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

pith.paper-citation-record.v1
2607.02522 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T17:31:11.711204Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

18 of 18 outbound references displayed

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  • unresolved18
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25e3b49f-187b-457e-b819-e07bd89fc4ed · outbound

This paper cites Edge artificial intelligence for 6g: Vision, enabling technologies, and applications,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Edge artificial intelligence for 6g: Vision, enabling technologies, and applications,

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:60db1db4894e84ea1038ff9947eb6631d3e951f058c3989bd0958b60db40d174

Observation 10db8f63-6080-4b45-8015-f2432ceae6ea · outbound

This paper cites Dis- tributed artificial intelligence empowered by end-edge-cloud computing: A survey,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Dis- tributed artificial intelligence empowered by end-edge-cloud computing: A survey,

Reference 2

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:f54ce7640302f90cef73635e9d4f38ca30c703a647f7d7cc080a316a4872ddf8

Observation 4d58fac6-e2f2-43ad-b1a6-5a11cd5d564b · outbound

This paper cites Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,

Reference 3

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:e67cc8399e085f9cffced13ada160175d0946c76e138253f5b45b8bdb99e224c

Observation ae5c7420-bb16-4190-b9ab-b21c02dab316 · outbound

This paper cites Distributed deep neural networks over the cloud, the edge and end devices,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Distributed deep neural networks over the cloud, the edge and end devices,

Reference 4

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:4f721a26e41e5fe53c1c6b40a3e8b498fdfa2efafcf3fc6d6ed1e21a54971b8d

Observation 3964dbc0-d43f-427e-ab79-61c5d75b1eab · outbound

This paper cites Dynamic adaptive dnn surgery for inference acceleration on the edge,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Dynamic adaptive dnn surgery for inference acceleration on the edge,

Reference 5

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:a5ed09fbf86dc96042b1448009f90c6c297acc710cc6a81542023899da918d0e

Observation 46f63038-55c8-4a96-ac61-d6f717f95291 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Efficient memory management for large language model serving with pagedattention,

Reference 6

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:27ed12b6e35a152f508579d096fd758a9b4de87d246b3aaad3986dfd1e62c271

Observation d1f55139-653c-4a6c-b5de-683934d4b724 · outbound

This paper cites Splitwise: Efficient generative llm inference using phase splitting,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Splitwise: Efficient generative llm inference using phase splitting,

Reference 7

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:04807850270cff58a0cbbaf9e1e5ed5fc67b9e3735bb57cfd20f3ea44f5d3233

Observation 949fc55f-8c86-4771-a2d2-cb57e1cd5010 · outbound

This paper cites {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving,

Reference 8

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:f13cde1921be0837106e2c2cc9d732c2f598c88a59602c84ffe97f39da52fdff

Observation c782d5ba-a83c-4f3e-b386-2756d66c2566 · outbound

This paper cites Llumnix: Dynamic scheduling for large language model serving,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Llumnix: Dynamic scheduling for large language model serving,

Reference 9

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:f96fa5879ab8739bd0328d64402e69e21b6bc450a216b845f6629b1d0a09e63d

Observation 1d1dc7a3-675a-4352-8a7e-2df827d8a85d · outbound

This paper cites {Cost-Efficient}large language model serving for multi- turn conversations with{CachedAttention},.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks {Cost-Efficient}large language model serving for multi- turn conversations with{CachedAttention},

Reference 10

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:fea29537cce60bb72bcfb6cc8345fd1c455c8038cbac766a54ef14f6ac6b5de1

Observation ed64a589-6acf-4a18-80f7-0a13abde7ec5 · outbound

This paper cites Stateful large language model serving with pensieve,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Stateful large language model serving with pensieve,

Reference 11

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:aaa65ab33ab275ece49a9ddd782bbf69dcd6ee42130352caef263b77bc11ddcc

Observation 41205033-d50e-4eaf-ada0-8746ce6fac36 · outbound

This paper cites Wdmoe: Wireless distributed mixture of experts for large language models,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Wdmoe: Wireless distributed mixture of experts for large language models,

Reference 12

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:3d908e2d3a3e1588bfdab88ad167fb7f1d29cb54292656c53736594b8da090ef

Observation ef3dc93e-6d9a-4db3-823a-61150f844071 · outbound

This paper cites Serving moe models on resource-constrained edge devices via dynamic expert swapping,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Serving moe models on resource-constrained edge devices via dynamic expert swapping,

Reference 13

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:51554990054aafdd2c567aa1336e0bd7fe4dc09993d1a0db70a04adf5df964fb

Observation c4aaa1bf-579c-4e55-a08f-32fd9b7adce9 · outbound

This paper cites Quality-of- service aware llm routing for edge computing with multiple experts,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Quality-of- service aware llm routing for edge computing with multiple experts,

Reference 14

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:eb312148b8340ae9b02165ddb68821c4ef8e5d8cb311f354ea00076f6f7bee99

Observation 6f11c718-3ff8-44b2-b0b4-fc487627045b · outbound

This paper cites HexGen: Generative Inference of Large Language Model over Heterogeneous Environment.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks HexGen: Generative Inference of Large Language Model over Heterogeneous Environment

Reference 15

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:a55c1ae073186ae28ae1668ecb2072f6a79816ef1b1042f6537ee538691a0263

Observation 94b038f2-1647-411e-9b9d-dff51436846d · outbound

This paper cites Edgeshard: Efficient llm inference via collaborative edge computing,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Edgeshard: Efficient llm inference via collaborative edge computing,

Reference 16

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:6f9439ac360e1c5d4ea076bf31f4203b637bead411e0a9c75ecd86986ce65940

Observation 8c185b62-3273-43df-bf97-f05d869d4e2b · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 17

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:fd1f1142337cf7fa050efbe23e42dec834d51185713a113d3f6c3900dee8f8e6

Observation 67bb2fb2-3092-4827-9d51-139fc5bd7668 · outbound

This paper cites Load is not what you should balance: Introducing prequal,.

Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks Load is not what you should balance: Introducing prequal,

Reference 18

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source=pdf_text observed=2026-07-12T17:31:11.711204Z digest=sha256:4d44a76b1931a5d31d62d8175fd4a4d727c64ff4708957ddaa7cf699b02505e6

Pith citing papers

No inbound Pith citation observations are available.