Paper Citation Record · LEDGER

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

As of 16 August 2026, this Paper Citation Record lists 100 of 288 outbound references and 9 inbound Pith citation observations for arXiv:2507.16731.

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

pith.paper-citation-record.v1
2507.16731 v1

Coverage vector

measured 100 of 288 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:06:48.231805Z

measured 109 of 109 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:20:59.587952Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T21:53:59.101198Z

Reference resolution

100 of 288 outbound references displayed

verified exact9
verified fuzzy0
unresolved90
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3ca209b4-721e-45b0-82d2-858871595f34 · outbound

This paper cites Online security-aware and reliability-guaranteed ai service chains provisioning in edge intelligence cloud.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Online security-aware and reliability-guaranteed ai service chains provisioning in edge intelligence cloud

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.801619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d95df77-9278-4292-b87a-501076da8b0b · outbound

This paper cites Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.806402Z

Source-reported events for the cited work

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Observation 62408183-0366-4f31-a299-92ea66d19bbb · outbound

This paper cites PrivateLoRA For Efficient Privacy Preserving LLM.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges PrivateLoRA For Efficient Privacy Preserving LLM

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.810584Z

Source-reported events for the cited work

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Observation 3c11d90d-83dc-43cf-bb2f-e94fd597f32a · outbound

This paper cites Mobile edge intelligence for large language models: A contemporary survey.IEEE Commun.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Mobile edge intelligence for large language models: A contemporary survey.IEEE Commun

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.815986Z

Source-reported events for the cited work

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Observation 56963ccd-c9fd-4710-a4f1-cc06dd8f45ce · outbound

This paper cites Multi-tier multi-node scheduling of LLM for collaborative AI computing.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Multi-tier multi-node scheduling of LLM for collaborative AI computing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.820048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7a507da-f54b-410b-a5ff-a41060532684 · outbound

This paper cites Cambricon-llm: A chiplet-based hybrid architecture for on-device inference of 70b LLM.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Cambricon-llm: A chiplet-based hybrid architecture for on-device inference of 70b LLM

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.824844Z

Source-reported events for the cited work

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Observation 8251bcb3-ca9f-4830-b3e0-14d631d1ba35 · outbound

This paper cites Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.829373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d9a3116-2abc-4dae-969c-c04b481a1658 · outbound

This paper cites Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.834205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8a59bcc-af3e-4842-bc3f-9f1a13736f8b · outbound

This paper cites DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges DC-CCL: Device-Cloud Collaborative Controlled Learning for Large Vision Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.838796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48a1e994-a5cd-4d79-bc64-793aeb41f5d1 · outbound

This paper cites Large language models (llms) inference offloading and resource allocation in cloud-edge networks: An active inference approach.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Large language models (llms) inference offloading and resource allocation in cloud-edge networks: An active inference approach

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.843074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35ed8e35-475f-4382-903d-283156d43b09 · outbound

This paper cites Crayon: Customized on-device llm via instant adapter blending and edge-server hybrid inference.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Crayon: Customized on-device llm via instant adapter blending and edge-server hybrid inference

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.847232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc860e1-309d-4dea-9d8a-324fadcb4e49 · outbound

This paper cites SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.851506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b00c00ef-973d-423d-8753-57b5903ac6f0 · outbound

This paper cites CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.855749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b49a73d2-4d9c-414d-b648-682a628ae57d · outbound

This paper cites Hybrid slm and llm for edge-cloud collaborative inference.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Hybrid slm and llm for edge-cloud collaborative inference

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.860087Z

Source-reported events for the cited work

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Observation bf8ef859-c923-4fe0-a03f-36ca2036710b · outbound

This paper cites Efficient deployment of large language model across cloud-device systems.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Efficient deployment of large language model across cloud-device systems

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.863912Z

Source-reported events for the cited work

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Observation c49c1075-66de-480e-b4f9-8b5588865c67 · outbound

This paper cites GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.867886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8436d3ec-316b-4e93-99ab-0c8ce657915c · outbound

This paper cites Large language models empowered autonomous edge ai for connected intelligence.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Large language models empowered autonomous edge ai for connected intelligence

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.871809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70db2372-76c3-471d-830d-f75024a99334 · outbound

This paper cites MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.875762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40940e70-b105-4f32-971f-dd5037d0694d · outbound

This paper cites Octo-planner: On-device Language Model for Planner-Action Agents.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Octo-planner: On-device Language Model for Planner-Action Agents

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.879783Z

Source-reported events for the cited work

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Observation 1c7ef846-9fc4-40fc-bf31-7347ed436c83 · outbound

This paper cites A survey on model compression for large language models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges A survey on model compression for large language models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.884177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f9cc99-fe6c-44de-8c0d-706648585558 · outbound

This paper cites Pushing up to the limit of memory bandwidth and capacity utilization for efficient llm decoding on embedded fpga.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Pushing up to the limit of memory bandwidth and capacity utilization for efficient llm decoding on embedded fpga

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.888303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1ad0552-5937-4a2a-821f-d52622cb3a43 · outbound

This paper cites Scaling up on-device llms via active-weight swapping between dram and flash.arXiv preprint arXiv:2504.08378, 2025.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Scaling up on-device llms via active-weight swapping between dram and flash.arXiv preprint arXiv:2504.08378, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.892060Z

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Unavailable: canonical work link unavailable.

Observation 178c80d9-2465-463c-aca5-8305a0cef15e · outbound

This paper cites Understanding llms: A comprehensive overview from training to inference.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Understanding llms: A comprehensive overview from training to inference

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.896655Z

Source-reported events for the cited work

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Observation 16778823-bc27-4b33-8aa5-949ec5360dae · outbound

This paper cites Large models for aerial edges: An edge-cloud model evolution and communication paradigm.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Large models for aerial edges: An edge-cloud model evolution and communication paradigm

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.900495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c27dfe0c-4edb-423c-8fa7-d61f33313141 · outbound

This paper cites LLMCad: Fast and Scalable On-device Large Language Model Inference.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges LLMCad: Fast and Scalable On-device Large Language Model Inference

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.908155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a39018f-15b5-41b3-98c3-800919755608 · outbound

This paper cites Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:50.749170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 228fc0f4-596e-47ef-b711-e1363ff3ce62 · outbound

This paper cites DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with Dynamic Multi-Sequence Drafting.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges DuoDecoding: Hardware-aware Heterogeneous Speculative Decoding with Dynamic Multi-Sequence Drafting

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.916285Z

Source-reported events for the cited work

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Observation 7c8c66b4-ac9b-410c-9c57-c71d8d25e634 · outbound

This paper cites LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.920673Z

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Observation 02554bec-0e0f-40e2-806a-3438b12c3541 · outbound

This paper cites Collaboration of large language models and small recommendation models for device-cloud recommendation.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Collaboration of large language models and small recommendation models for device-cloud recommendation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.924782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0eb67ef-36c8-4f21-bef5-6efc15a9d170 · outbound

This paper cites Crayon: Customized on-device LLM via instant adapter blending and edge-server hybrid inference.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Crayon: Customized on-device LLM via instant adapter blending and edge-server hybrid inference

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.928500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4aa38c5d-1619-46dd-a8c8-bbbe08456dca · outbound

This paper cites Speculative decoding with big little decoder.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Speculative decoding with big little decoder

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.932649Z

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Observation a278fdd3-a98d-4c48-a7d7-2a961e7f0de5 · outbound

This paper cites Cogenesis: A framework collaborating large and small language models for secure context-aware instruction following.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Cogenesis: A framework collaborating large and small language models for secure context-aware instruction following

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.936405Z

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Observation 01175081-3b47-4467-97b4-3550aa770503 · outbound

This paper cites Cloud-edge collaborative large model services: Challenges and solutions.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Cloud-edge collaborative large model services: Challenges and solutions

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.944565Z

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Observation 1b76a8ec-19a5-4235-8784-88635bdcd40f · outbound

This paper cites Backpropagation-free multi-modal on-device model adaptation via cloud-device collaboration.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Backpropagation-free multi-modal on-device model adaptation via cloud-device collaboration

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.948753Z

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Unavailable: canonical work link unavailable.

Observation b879e10c-fcb3-4518-83f2-40c8eacad096 · outbound

This paper cites Reward-Guided Speculative Decoding for Efficient LLM Reasoning.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Reward-Guided Speculative Decoding for Efficient LLM Reasoning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.952749Z

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Observation f2c4cb9b-af37-4482-b79d-91fddc4e4dba · outbound

This paper cites Kangaroo: Lossless self-speculative decoding for accelerating llms via double early exiting.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Kangaroo: Lossless self-speculative decoding for accelerating llms via double early exiting

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.956928Z

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Observation c5ae9cde-a63b-4669-ae61-f28a8dab7742 · outbound

This paper cites AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.960865Z

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Unavailable: canonical work link unavailable.

Observation a8a9cfb0-68f6-4bdf-af2b-f88707513c50 · outbound

This paper cites Guiding Reasoning in Small Language Models with LLM Assistance.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Guiding Reasoning in Small Language Models with LLM Assistance

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.965279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faf29311-9c3d-4e94-b749-2d00670122de · outbound

This paper cites DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:50.637879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43227bb7-20d5-4bc1-bcd8-1f5c500a2288 · outbound

This paper cites Specexec: Massively parallel speculative decoding for interactive llm inference on consumer devices.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Specexec: Massively parallel speculative decoding for interactive llm inference on consumer devices

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.973134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15299ace-83f6-4144-92f5-6ac55cade336 · outbound

This paper cites PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:50.618707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2c5dd40e-0833-4bba-a7bf-064bf0edcdb1 · outbound

This paper cites Enhancing on-device llm inference with historical cloud-based llm interactions.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Enhancing on-device llm inference with historical cloud-based llm interactions

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.981324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5288112b-71d6-42f5-8d52-f5f58a7fecc0 · outbound

This paper cites Velo: A vector database-assisted cloud-edge collaborative llm qos optimization framework.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Velo: A vector database-assisted cloud-edge collaborative llm qos optimization framework

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.985404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 231b82c3-aaba-43e0-aa98-dc4dfa9f7626 · outbound

This paper cites Fedmkt: Federated mutual knowledge transfer for large and small language models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Fedmkt: Federated mutual knowledge transfer for large and small language models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.990098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3b7c774-f4d6-4099-971a-ed1e2a27f873 · outbound

This paper cites Federated transfer learning for on-device llms efficient fine tuning optimization.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Federated transfer learning for on-device llms efficient fine tuning optimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.994722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 516181f2-23ef-4801-8793-7b391a7719f3 · outbound

This paper cites Cloud-device collaborative learning for multimodal large language models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Cloud-device collaborative learning for multimodal large language models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:47.998492Z

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Unavailable: canonical work link unavailable.

Observation 5ffdc7e0-b1f4-4cd3-9730-245080f57d3b · outbound

This paper cites Division-of-thoughts: Harnessing hybrid language model synergy for efficient on-device agents.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Division-of-thoughts: Harnessing hybrid language model synergy for efficient on-device agents

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.002387Z

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Unavailable: canonical work link unavailable.

Observation 7c498b3c-d2ee-4048-8edc-ecf2cb5ded22 · outbound

This paper cites EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.006502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 267b1906-1915-48a6-99b8-50211d008776 · outbound

This paper cites Resource allocation for stable llm training in mobile edge computing.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Resource allocation for stable llm training in mobile edge computing

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.010380Z

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Richard Yu, et al

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Adaptlink: A heterogeneity-aware adaptive framework for distributed mllm inference

Reference 53

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Hybridrag: Integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction

Reference 54

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Edge-cloud collaborative motion planning for autonomous driving with large language models

Reference 56

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Grey-box prompt optimization and fine-tuning for cloud-edge LLM agents, 2024

Reference 57

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This paper cites Small Models are Valuable Plug-ins for Large Language Models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Small Models are Valuable Plug-ins for Large Language Models

Reference 58

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Slim: Speculative decoding with hypothesis reduction

Reference 59

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Opt-tree: Speculative decoding with adaptive draft tree structure

Reference 60

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Speculative decoding via early-exiting for faster llm inference with thompson sampling control mechanism

Reference 62

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Diffusion-based cloud-edge-device collaborative learning for next poi recommendations

Reference 63

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This paper cites An edge-cloud collaboration framework for generative AI service provision with synergetic big cloud model and small edge models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges An edge-cloud collaboration framework for generative AI service provision with synergetic big cloud model and small edge models

Reference 64

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This paper cites Enhanced hybrid inference techniques for scalable on-device llm personalization and cloud integration.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Enhanced hybrid inference techniques for scalable on-device llm personalization and cloud integration

Reference 65

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Edge-llm: A collaborative framework for large language model serving in edge computing

Reference 66

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Mergenet: Knowledge migration across heterogeneous models, tasks, and modalities

Reference 67

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This paper cites Optimize incompatible parameters through compatibility-aware knowledge integration.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Optimize incompatible parameters through compatibility-aware knowledge integration

Reference 68

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This paper cites Forward once for all: Structural parameterized adaptation for efficient cloud-coordinated on-device recommendation.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Forward once for all: Structural parameterized adaptation for efficient cloud-coordinated on-device recommendation

Reference 69

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Diet: Customized slimming for incompatible networks in sequential recommendation

Reference 70

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This paper cites Edge vs cloud: How do we balance cost, latency, and quality for large language models over 5g networks? In Proc.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Edge vs cloud: How do we balance cost, latency, and quality for large language models over 5g networks? In Proc

Reference 71

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Edge-cloud collaborative computing on distributed intelligence and model optimization: A survey

Reference 72

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This paper cites Fedcfa: Alleviating simpson’s paradox in model aggregation with counterfactual federated learning.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Fedcfa: Alleviating simpson’s paradox in model aggregation with counterfactual federated learning

Reference 73

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation

Reference 74

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Intelligent model update strategy for sequential recommendation

Reference 75

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Aug-kd: Anchor-based mixup generation for out-of-domain knowledge distillation

Reference 76

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Llmco4mr: Llms-aided neural combinatorial optimization for ancient manuscript restoration from fragments with case studies on dunhuang

Reference 77

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Mpod123: One image to 3d content generation using mask-enhanced progressive outline-to-detail optimization

Reference 78

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Federated Co-tuning Framework for Large and Small Language Models

Reference 79

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics

Reference 80

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Improving In-Context Learning with Small Language Model Ensembles

Reference 82

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration

Reference 83

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Fast and Slow Generating: An Empirical Study on Large and Small Language Models Collaborative Decoding

Reference 84

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This paper cites Knowledge-decoupled synergetic learning: An MLLM based collaborative approach to few-shot multimodal dialogue intention recognition.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Knowledge-decoupled synergetic learning: An MLLM based collaborative approach to few-shot multimodal dialogue intention recognition

Reference 85

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization

Reference 86

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer

Reference 87

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This paper cites Modular pluralism: Pluralistic alignment via multi-llm collaboration.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Modular pluralism: Pluralistic alignment via multi-llm collaboration

Reference 88

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Efficient multitask learning in small language models through upside-down reinforcement learning

Reference 89

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This paper cites Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks

Reference 90

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This paper cites Slmrec: Distilling large language models into small for sequential recommendation.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Slmrec: Distilling large language models into small for sequential recommendation

Reference 91

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Automix: Automatically mixing language models

Reference 92

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This paper cites Weak-to-strong generalization: Eliciting strong capabilities with weak supervision.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Weak-to-strong generalization: Eliciting strong capabilities with weak supervision

Reference 93

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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Routerdc: Query-based router by dual contrastive learning for assembling large language models

Reference 94

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This paper cites Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors

Reference 95

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This paper cites Heterogeneous lora for federated fine-tuning of on-device foundation models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Heterogeneous lora for federated fine-tuning of on-device foundation models

Reference 96

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Observation dd95f176-65bc-4cd3-95dd-db3a870e3b8e · outbound

This paper cites Hybrid llm: Cost-efficient and quality-aware query routing.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Hybrid llm: Cost-efficient and quality-aware query routing

Reference 97

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This paper cites Data shunt: Collaboration of small and large models for lower costs and better performance.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Data shunt: Collaboration of small and large models for lower costs and better performance

Reference 98

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This paper cites Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes

Reference 99

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This paper cites Fast inference from transformers via speculative decoding.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Fast inference from transformers via speculative decoding

Reference 100

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This paper cites Ddk: Distilling domain knowledge for efficient large language models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Ddk: Distilling domain knowledge for efficient large language models

Reference 101

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Observation 4790f1c0-6132-40a0-9fb8-2ec552bf9b75 · outbound

This paper cites Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts

Reference 102

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Observation 8de31c04-962b-4f74-b597-d86c19b2e181 · outbound

This paper cites Llm-qat: Data-free quantization aware training for large language models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Llm-qat: Data-free quantization aware training for large language models

Reference 103

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Observation fb76b218-69eb-49e7-8e7d-7b12bba5b73d · outbound

This paper cites Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization

Reference 104

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Observation 15f536a6-b30d-46ab-a9cd-887b819afc65 · outbound

This paper cites An emulator for fine-tuning large language models using small language models.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges An emulator for fine-tuning large language models using small language models

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:48.231805Z

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Pith citing papers

Observation 4661de65-4c54-46ae-9ead-af3e3843e64c · inbound

Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions cites this paper.

Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 119

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:59.587952Z

Source-reported events for the cited work

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Observation ccbc9f12-93f1-4be4-84a2-4a2f3e852ef0 · inbound

Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training cites this paper.

Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:54.655669Z

Source-reported events for the cited work

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Observation 3aa744cd-79f9-4374-958a-0e7c05cdaa3a · inbound

RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation cites this paper.

RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:28:04.749869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 890f9d7c-1e51-4e65-9172-0f07ffb30613 · inbound

Administrative Decentralization in Edge-Cloud Multi-Agent for Mobile Automation cites this paper.

Administrative Decentralization in Edge-Cloud Multi-Agent for Mobile Automation Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:46:12.943447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 136e5946-975c-43d6-948e-22a5ef67db2d · inbound

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda cites this paper.

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:31:30.807812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1876146e-6691-4946-a788-3bddb672a665 · inbound

PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems cites this paper.

PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:18:37.500048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9902e717-f356-43b6-b8a9-69fa45a02cea · inbound

An Efficient and Privacy-Preserving Architecture for Cross-Institutional Collaborative RAG cites this paper.

An Efficient and Privacy-Preserving Architecture for Cross-Institutional Collaborative RAG Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:53:59.102659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a7e64c04-0db6-4448-8b9c-9944903863d4 · inbound

Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models cites this paper.

Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T06:42:15.915097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a404b17-ab33-40ef-b445-4eff6cb665a6 · inbound

PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference cites this paper.

PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-01T10:14:14.063270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.