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Paper Citation Record · LEDGER

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading

As of 11 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2501.14205.

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

pith.paper-citation-record.v1
2501.14205 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:19:49.664592Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T18:53:34.664986Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:58:42.766816Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact2
  • verified fuzzy14
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d84a032f-a3d7-4da2-bffa-b46f63043aba · outbound

This paper cites When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 10c0479d-77f9-42de-b2dd-70ba141c59fe · outbound

This paper cites Language mod- els are few-shot learners,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Language mod- els are few-shot learners,

Reference 2

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source=pdf_text observed=2026-08-10T15:19:49.519977Z digest=sha256:46236c9df78ba89c0e458ae6b912d0c110323347d9f7b249cb1c926fb665c90d

Observation ba4d54c5-4232-4607-8a4d-db3262dcc5e0 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 3

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.523574Z digest=sha256:63f894ec117439261d73e176fa6ba1ca16e41edd3b1229cdd527d756bb124f68

Observation 07bcb665-a1fa-4fa8-bcd8-b820ba246c89 · outbound

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

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.528250Z digest=sha256:692e2e347ba9ac36a49fe952076439e7fe23ec6158c1151dc53c8ca8520ca144

Observation 7f4ee1f1-789c-46ed-873a-0ec185a40574 · outbound

This paper cites The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving

Reference 5

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verified exact
local_arxiv, observed 2026-08-10T15:19:49.892687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.532652Z digest=sha256:40c6f1a2e62e740e28a71eedb7bb3091c7b3a76859aa009cff27aa8a9761a647

Observation 324835ab-36ac-41f0-a772-95bd158246ae · outbound

This paper cites Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.536634Z digest=sha256:ae6984b6faeb043c0e02ebc51264d9d4197137f8174426c703e9d491c9f93502

Observation c30e738c-4fea-4cca-8947-4a9a28a11baa · outbound

This paper cites Retention-aware container caching for serverless edge computing,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Retention-aware container caching for serverless edge computing,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.541162Z digest=sha256:fde9431d4950e55af7f1032e6247ee781d776f8d22e8447f2d736c91bef0fca3

Observation f5daf523-da96-4009-b6ea-729c2d61fe2e · outbound

This paper cites Cache- enabled federated learning systems,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Cache- enabled federated learning systems,

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.544636Z digest=sha256:f068f8d6abd2395c031fc0f33704bce0a81582118a7661dd6a998939fc00e864

Observation be5ebe3d-bac9-4f38-9898-d668c14f884f · outbound

This paper cites Edgeadaptor: Online configuration adaption, model selection and re- source provisioning for edge dnn inference serving at scale,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Edgeadaptor: Online configuration adaption, model selection and re- source provisioning for edge dnn inference serving at scale,

Reference 9

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raw_fallback, observed 2026-08-10T15:19:50.102086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.548117Z digest=sha256:dd32329e8a091e2c1544ae42d45d105ed06cb49f015779e408b07f691da46741

Observation bafd9c87-ef0f-48d7-a912-e7242e487e42 · outbound

This paper cites Cooperative service caching and workload scheduling in mobile edge computing,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Cooperative service caching and workload scheduling in mobile edge computing,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.551751Z digest=sha256:180d4dc47537243498540495b7c1e49f3e14eb62658bb1bc624ba941e2db618f

Observation 4f9d75c6-b1d1-42e9-8a1b-d87f9a6764be · outbound

This paper cites MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool

Reference 11

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source=pdf_text observed=2026-08-10T15:19:49.555305Z digest=sha256:7a42653df2d15c296362352f4a4a393fc6a1d546a3f458c438fb97ed32cbe4ba

Observation b926fd4d-4dd1-4781-bf88-329a2c568c07 · outbound

This paper cites LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading LLM-dCache: Improving Tool-Augmented LLMs with GPT-Driven Localized Data Caching

Reference 12

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source=pdf_text observed=2026-08-10T15:19:49.560249Z digest=sha256:7e27cd6a51ca8284da7d851236c71debeb508ed27b8f1279142a7545032d61f0

Observation 5c5236eb-bcb1-4540-9a57-1cc26038a826 · outbound

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

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Chain-of-thought prompting elicits reasoning in large language models,

Reference 13

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source=pdf_text observed=2026-08-10T15:19:49.564040Z digest=sha256:669f6099e1243b1dcdaabb5c30149d1749f732c04a489fb9eb0fbb5b34ece815

Observation 705d8997-317d-498a-9e27-59e210c9deb9 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 14

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source=pdf_text observed=2026-08-10T15:19:49.567437Z digest=sha256:e44c307941d847e7b6ebc52ac302b4baa8b9b9094689b8159a9c2b531733fd3a

Observation c18bf34f-319d-459a-b120-e013e31636f6 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Proximal Policy Optimization Algorithms

Reference 15

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source=pdf_text observed=2026-08-10T15:19:49.571013Z digest=sha256:abe411198d6cb1000f6db433293eee71a9c34b84d381f4876eff8d4416da86a4

Observation 0d234bdd-393a-4d3f-971b-85005cbf12ef · outbound

This paper cites Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 16

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source=pdf_text observed=2026-08-10T15:19:49.574849Z digest=sha256:a9c4cc35807993c5a08ec912ed0fd61928d4a5199c5b3f66b6f9be676181920b

Observation dfe2d729-d1ce-43e7-80a6-73929e45fe0a · outbound

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

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

Reference 17

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Observation 3d87c17d-e2a4-4fdd-8215-d8abd3b885b2 · outbound

This paper cites PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services

Reference 18

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source=pdf_text observed=2026-08-10T15:19:49.582503Z digest=sha256:e5b297bdeb2d52a1105a349714503cd16a32fc671a4b7994b1a0d520fd359471

Observation 558a7db0-9503-4bee-8958-d1bb52c45b96 · outbound

This paper cites Titanic: Towards production federated learning with large language models,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Titanic: Towards production federated learning with large language models,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation d75add3e-0cf4-4cfb-8a7d-7cfc7c88d351 · outbound

This paper cites Generative inference of large language models in edge computing: An energy efficient approach,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Generative inference of large language models in edge computing: An energy efficient approach,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.590170Z digest=sha256:8ac8505273d81cd6cc64fff87897c655d72564834770610da946490ef581a81f

Observation ddf08fb1-3e2e-4b54-96ff-e77a2abb545e · outbound

This paper cites Two time-scale joint service caching and task offloading for uav-assisted mobile edge computing,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Two time-scale joint service caching and task offloading for uav-assisted mobile edge computing,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.593639Z digest=sha256:907344232b697f1ae5555de269e537ddc76c7a9d7f390b3e1ccd78fbc4f186ab

Observation 20ac9efd-3b09-4f68-b21c-f5e6764c2d81 · outbound

This paper cites TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading

Reference 22

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local_arxiv, observed 2026-08-10T15:19:49.790517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.596984Z digest=sha256:73f17fd2cecdac4a65e999368deeceba362719aff5a401c6f86904be7617ceda

Observation c27acdd5-8e20-49ec-aa36-8258fa3e4b5e · outbound

This paper cites A3c-based computation offloading and service caching in cloud-edge computing networks,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading A3c-based computation offloading and service caching in cloud-edge computing networks,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.600715Z digest=sha256:f9d7cf884a9e28c45b0f990ad4ae50190241aca75ed5255ddb24d2313c75bec2

Observation e482c0ba-9f64-42f9-ad97-5883a39ea08f · outbound

This paper cites Deepcache: A deep learning based framework for content caching,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Deepcache: A deep learning based framework for content caching,

Reference 24

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.604274Z digest=sha256:59f9338fcddfd4e16c862d8be9c5265222cc468dfadd0df7236ad00652de1ee4

Observation 73400278-8708-4782-9b07-d901433e05c5 · outbound

This paper cites Deep reinforcement learning-based computation offloading and distributed edge service caching for mobile edge computing,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Deep reinforcement learning-based computation offloading and distributed edge service caching for mobile edge computing,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.607789Z digest=sha256:41d04e47dc54d86b1591095cf7571720a21d1088d25e0b9e650b0d4fbb93d2c8

Observation d1b9ca53-b7dd-4cb3-ab6e-296d6691b3ad · outbound

This paper cites Neighboring- aware caching in heterogeneous edge networks by actor-attention-critic learning,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Neighboring- aware caching in heterogeneous edge networks by actor-attention-critic learning,

Reference 26

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raw_fallback, observed 2026-08-10T15:19:50.000141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.611078Z digest=sha256:08880970da4c43c52a729e01c4a225c4d9beb184ce169ad5adc293aa447a5729

Observation febaaaee-ece9-4f37-a2ca-6b25a529ef91 · outbound

This paper cites Cooperative task offloading and service caching for digital twin edge networks: A graph attention multi- agent reinforcement learning approach,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Cooperative task offloading and service caching for digital twin edge networks: A graph attention multi- agent reinforcement learning approach,

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-10T15:19:49.984386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.614544Z digest=sha256:ec4ddaf7c19e48ff1334c1857beb2b43c65fdb4059fe0afe5f0ae5f9ee6f2fd4

Observation 14ba85ae-987e-4d82-926f-cf3bb749626d · outbound

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

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Large language models (llms) inference offloading and resource allocation in cloud-edge com- puting: An active inference approach,

Reference 28

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raw_fallback, observed 2026-08-10T15:19:49.972042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.618260Z digest=sha256:73fa7c6c6fbd1186437d0794b5e12cdc9699be3363178f30b35fb1db2fb6ce5e

Observation 4b35c3fb-0782-46ce-a0cc-d39fc95bd21c · outbound

This paper cites Are transformers universal approximators of sequence-to-sequence func- tions?.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Are transformers universal approximators of sequence-to-sequence func- tions?

Reference 29

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raw_fallback, observed 2026-08-10T15:19:49.960600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.621562Z digest=sha256:dd0a9af4612134be39361b01886bae31af78c9c612ab8332531ceb41dbce7e96

Observation 39190684-d285-4a76-9702-86ce715496c3 · outbound

This paper cites Why Can Large Language Models Generate Correct Chain-of-Thoughts?.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Why Can Large Language Models Generate Correct Chain-of-Thoughts?

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.624860Z digest=sha256:342f1362df9b0c93717c14f85efe800a03dde0b5de6bc08493a84a9cda3a8d73

Observation 1138a40f-56d7-45f9-aaa3-3df80e5f49cb · outbound

This paper cites A Latent Space Theory for Emergent Abilities in Large Language Models.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.628482Z digest=sha256:6256c4905466dcfb83c0bc847989e96c73bea17c3f311b637acc9ebebea261e9

Observation 7abc262e-0c74-49b5-b4b3-2dbab7d53f38 · outbound

This paper cites Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.632041Z digest=sha256:d1ded94c53559de0a688ad03f768e6612822cdc6ee15a467e3e08d22eea4751f

Observation 6bff87d8-a937-46a8-afd5-3eac5f51219f · outbound

This paper cites Imagebind: One embedding space to bind them all,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Imagebind: One embedding space to bind them all,

Reference 33

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source=pdf_text observed=2026-08-10T15:19:49.635629Z digest=sha256:cbbdd9f10a152db594786c7f7886f9553a4d9232b5a1caca62eaa11b0144388e

Observation cb3af874-46ee-4c30-8ae5-3dc7885dc6b0 · outbound

This paper cites Solving General Arithmetic Word Problems.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Solving General Arithmetic Word Problems

Reference 34

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source=pdf_text observed=2026-08-10T15:19:49.639218Z digest=sha256:281f682010eef530eb34088063f0c050be25ea04b705d1f81ebbd5aa94f912d8

Observation 9625d1c3-274b-4f21-b68a-0c4480f8d821 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 35

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source=pdf_text observed=2026-08-10T15:19:49.643307Z digest=sha256:7226a535cb0ddcd911e9bc04075c887a92078230e61afc166f20905efa0e222e

Observation 77168914-eb45-40cd-8636-c0a7241f0c22 · outbound

This paper cites Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,

Reference 36

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source=pdf_text observed=2026-08-10T15:19:49.648085Z digest=sha256:a795ce759b55dce4e36ac9fbfd54af1c7394b0b5ea677f7bce26dd4207144b64

Observation 4b0c23f5-f14f-4c7a-9454-88602f16b0ba · outbound

This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Reference 37

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source=pdf_text observed=2026-08-10T15:19:49.651683Z digest=sha256:1d19785b5cbffda429e79650ff7b2e482a6acc25750a00a484b0c781a56a9000

Observation 81301fb8-5a9e-45f0-b363-1aa1c5868a03 · outbound

This paper cites Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models

Reference 38

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source=pdf_text observed=2026-08-10T15:19:49.656199Z digest=sha256:606cdbec8bdb4207335d91f1d0f828cb0afdd75271f86fb60dea504d8c3e1493

Observation 305919c9-70f4-428d-8177-f427c898aea5 · outbound

This paper cites Improve diverse text generation by self labeling conditional variational auto encoder,.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Improve diverse text generation by self labeling conditional variational auto encoder,

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-10T15:19:49.936143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T15:19:49.660633Z digest=sha256:0372df78d52c6017fa637f6a8407ad7d83a173d3def96ba9347972c6168691e0

Observation 7234443e-7d09-4bb8-b0cb-9494d4685cf6 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading Training Verifiers to Solve Math Word Problems

Reference 40

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source=pdf_text observed=2026-08-10T15:19:49.664592Z digest=sha256:72897b1b0d40238771c7a768bbb005db0114c5cee05224ef13b8794d6f94c54d

Pith citing papers

Observation e0444af8-1be3-4fcc-8942-70a4602b0f22 · inbound

The Price of Anarchy in Disaggregated Inference cites this paper.

The Price of Anarchy in Disaggregated Inference Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading

Reference 36

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verified exact
arxiv_id, observed 2026-07-03T16:58:42.768430Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-27T04:52:48.847661Z digest=sha256:7ee41c398228cfaaa7428cf1efb4cccd142ee5814b8ff35cc73552d6365e1803

Observation aae32842-d1be-4de0-846a-af0918afdbb6 · inbound

LMEdge: QoS-Aware LLM Inference Orchestration on Edge Clusters cites this paper.

LMEdge: QoS-Aware LLM Inference Orchestration on Edge Clusters Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading

Reference 13

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source=pdf_text observed=2026-08-01T18:53:34.664986Z digest=sha256:2cb6f2d4773be8624ffb43df0e6d6a6b59eae2cc6a01e4a0983673f428657d0b