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Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.02616.

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

pith.paper-citation-record.v1
2406.02616 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:43:56.700783Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:49:44.099745Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cd154b91-1846-4db4-a561-5132ecb2cf5b · inbound

The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks cites this paper.

The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T21:43:56.700783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:43:56.700783Z digest=sha256:cbb8bcd0f8275a3f369e13e33dd0931a2905b04db2cf0a8a0ca30c3d89190289

Observation 8d07b905-eb8c-472a-8165-348b586db6bc · inbound

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

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 203

Resolution
unresolved
no resolver link, observed 2026-08-06T15:06:48.611850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:48.611850Z digest=sha256:36e28ad4535a5db63d4c6a19fd58f80ad17614a77e7681a2637bbcebe4ca5027

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

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI cites this paper.

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.

source=pdf_text observed=2026-08-03T19:21:36.380653Z digest=sha256:54a40c687ac2a206d725135d9d99bd5587ae75ecef53e0fcb5d334daabd2a91d

Observation 9edf613d-80bf-4e22-b66e-51a1ab613673 · inbound

WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching cites this paper.

WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-16T14:12:58.630175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-16T14:12:06.034679Z digest=sha256:969f182f23184502b683c501ecbf1564c941ee5384dde125e3115effcbbabfed

Observation 84b7439d-9584-41fa-a3f6-8dca8f42c4fa · inbound

What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference cites this paper.

What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:45:20.560535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-25T04:41:56.650117Z digest=sha256:c0bbb84a0b163e092c5e4df9fd76675efafc04da39203c430df6c930f3cd44d7

Observation d0f918b8-7816-4716-9d2f-6dc5cac19928 · inbound

Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing cites this paper.

Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:49:44.101262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T09:34:00.058213Z digest=sha256:f96e11a63c99575bcf0d7c1efa5154253b3dfbf01ae1defe0e6a2d25b8f025bc

Observation 4657b477-7dbf-4877-8d33-6d4b482daae4 · inbound

Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs cites this paper.

Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-08T04:20:56.465122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T04:20:56.465122Z digest=sha256:726b59b178c0abe10a382c41b440d846bdd0806aea53dee64b6fb064cd7cd344