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Paper Citation Record · LEDGER

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2607.18244.

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

pith.paper-citation-record.v1
2607.18244 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T15:18:55.255400Z

measured 26 of 26 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

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6062136a-7d93-4746-a91c-e88b669b4fbe · outbound

This paper cites Language models are few-shot learners,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Language models are few-shot learners,

Reference 1

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source=pdf_text observed=2026-08-02T15:18:53.411365Z digest=sha256:40117458d93368c7ce66fca0f54484a6244f6dd3c2e568e27af326ba3c25e6ae

Observation 4a6d267c-c00c-4f22-bcba-ac67d79c5283 · outbound

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

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Chain-of-thought prompting elicits reasoning in large language models,

Reference 2

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source=pdf_text observed=2026-08-02T15:18:53.515726Z digest=sha256:037c3856e5dc9919de4d7bdec23fb62d4ae7f12e4d1313f1aad28c67ee585953

Observation 23c3fa07-d192-4da0-af21-2655b9ed31c0 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive NLP tasks,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Retrieval- augmented generation for knowledge-intensive NLP tasks,

Reference 3

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source=pdf_text observed=2026-08-02T15:18:53.577593Z digest=sha256:7e5796b1cfa0d8f6dd6a092820f61ad7c76cc600bb1ab2f469a01d376a033a9d

Observation 31a32cf9-2b6e-4fed-80bd-f49e5b2b30bd · outbound

This paper cites Toolformer: Language models can teach themselves to use tools,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Toolformer: Language models can teach themselves to use tools,

Reference 4

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source=pdf_text observed=2026-08-02T15:18:53.676467Z digest=sha256:1cd2053c33840b95d7442bff324b428672ecb00a3bb5b3a646649ee8b8e3ac5e

Observation 608ac105-772c-4a1a-8692-f9b44846a7b9 · outbound

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

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Mobile edge intelligence for large language models: A contemporary survey,

Reference 5

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source=pdf_text observed=2026-08-02T15:18:53.776418Z digest=sha256:6491430ea5167a59717be262f7b1896cf6a0a5067ba02d785bdaf76a1c826d0b

Observation e95628d1-4b88-41e7-b0ee-7ec91796a653 · outbound

This paper cites Federated edge learning with misaligned over-the-air computation,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Federated edge learning with misaligned over-the-air computation,

Reference 6

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source=pdf_text observed=2026-08-02T15:18:53.875667Z digest=sha256:8c5f0cff95ce77a8a3d7bcf3ffc222b404be43740d58fb08aeba83a2afc9afd2

Observation 5cfd08e7-ee02-466c-ad3e-d33eea8d1769 · outbound

This paper cites Knowledge-augmented reasoning distillation for small language models in knowledge-intensive tasks,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Knowledge-augmented reasoning distillation for small language models in knowledge-intensive tasks,

Reference 7

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source=pdf_text observed=2026-08-02T15:18:53.972725Z digest=sha256:dcc3317ebd9b19ec20af2ddac1c800ab96efe4c5e5133b2d0e7396fbf738841d

Observation 6f1467f4-54d9-4b4d-8fdf-50eba45905be · outbound

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

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks DDK: Distilling domain knowledge for efficient large language models,

Reference 8

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source=pdf_text observed=2026-08-02T15:18:54.106911Z digest=sha256:98b9adf8e26f85196d6a9c1259e459610155b37b7a1396ae62efd3b261ab5989

Observation fc4c31d1-05fe-46c7-b83e-a736aea93316 · outbound

This paper cites Jiuzhang3.0: Efficiently improving mathe- matical reasoning by training small data synthesis models,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Jiuzhang3.0: Efficiently improving mathe- matical reasoning by training small data synthesis models,

Reference 9

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source=pdf_text observed=2026-08-02T15:18:54.205472Z digest=sha256:10af866d17ccf5a556186891134dbfecaacade2a6f2aff45f913052d034f1c7f

Observation 24decd7c-5953-4781-aa3b-ea453fa1b6c2 · outbound

This paper cites Demystifying small language models for edge deployment,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Demystifying small language models for edge deployment,

Reference 10

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source=pdf_text observed=2026-08-02T15:18:54.276661Z digest=sha256:a2262f0c7be1ff5a7b64f6d97945ca49f23b6fbfaeaaaeff94efcf91aafa8fa1

Observation e2956c19-7806-43b2-8b5b-d3e7558389fc · outbound

This paper cites RouteLLM: Learning to route LLMs from pref- erence data,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks RouteLLM: Learning to route LLMs from pref- erence data,

Reference 11

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Observation ec1dc573-8363-49fb-89f2-9a8e7fb9aa05 · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 12

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Observation 3e997468-e872-456a-9728-3049e8a787aa · outbound

This paper cites Hybrid SLM and LLM for edge-cloud collaborative inference,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Hybrid SLM and LLM for edge-cloud collaborative inference,

Reference 13

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source=pdf_text observed=2026-08-02T15:18:54.492845Z digest=sha256:90e21cf42a70b181d872840fce24fc35988da53fb106fc39b49c3faec3edd328

Observation 40c87724-d886-4d27-abb4-f70621715458 · outbound

This paper cites Uncertainty-aware hybrid inference with on-device small and remote large language models,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Uncertainty-aware hybrid inference with on-device small and remote large language models,

Reference 14

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source=pdf_text observed=2026-08-02T15:18:54.546029Z digest=sha256:51e1f49872c20461a052f848276b512f34c8f010f2ac54041b90a2cc2b629d41

Observation 28555106-1416-4f58-b7f3-19a2b0208d0c · outbound

This paper cites CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing

Reference 15

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source=pdf_text observed=2026-08-02T15:18:54.607262Z digest=sha256:fb7a1b0242e756af81b223a593e8fd4ad3f87944740c305fc7ac17b059dd4c0e

Observation 983a0545-2bfc-4495-9f1f-8abc91170892 · outbound

This paper cites Reward-guided speculative decoding for efficient LLM reasoning,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Reward-guided speculative decoding for efficient LLM reasoning,

Reference 16

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source=pdf_text observed=2026-08-02T15:18:54.676953Z digest=sha256:0668b5d03b9d6d6b4c2961fe4a8d9edfb61e88c1f03a7af6d641105346d7ed7f

Observation 92c081ea-80f4-4613-aebd-e5437bd77295 · outbound

This paper cites Large-Small Model Collaboration for Enhancing Edge-Deployed Small Models.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Large-Small Model Collaboration for Enhancing Edge-Deployed Small Models

Reference 17

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source=pdf_text observed=2026-08-02T15:18:54.725894Z digest=sha256:844c6596dcbff97a82bfbaff1805333f80b80d174546373bfa3694a4a6696c8b

Observation a775e885-8379-46ab-b32c-fec31f705f64 · outbound

This paper cites LLMind: Or- chestrating AI and IoT with LLM for complex task execution,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks LLMind: Or- chestrating AI and IoT with LLM for complex task execution,

Reference 18

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Observation 06f30eeb-0850-4efe-9284-d4738d474640 · outbound

This paper cites Cayley Graph Optimization for Scalable Multi-Agent Communication Topologies.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Cayley Graph Optimization for Scalable Multi-Agent Communication Topologies

Reference 19

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source=pdf_text observed=2026-08-02T15:18:54.854103Z digest=sha256:45535d5d0043ab3d93da655ab60015225b1febb0136075e58a5f5efb8f0c49e6

Observation 23951e18-1efa-4297-81f2-451b8e092725 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environ- ments,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Multi-agent actor-critic for mixed cooperative-competitive environ- ments,

Reference 20

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Observation f18bab13-7cbf-4c57-aa43-d6e4b7b3bd79 · outbound

This paper cites Multiagent cooperation and competition with deep reinforcement learning,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Multiagent cooperation and competition with deep reinforcement learning,

Reference 21

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Observation 4765349c-c8b3-4fc3-8614-05c4d934b123 · outbound

This paper cites A theory of semantic communication,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks A theory of semantic communication,

Reference 22

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source=pdf_text observed=2026-08-02T15:18:55.024162Z digest=sha256:cdd325bad3e46953d2af01c03c08abd484797d27c950eae78b9a628214618f56

Observation 4f0136b4-0226-42dc-ba88-fd25d2051414 · outbound

This paper cites Scaling Large Language Model-based Multi-Agent Collaboration.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Scaling Large Language Model-based Multi-Agent Collaboration

Reference 23

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source=pdf_text observed=2026-08-02T15:18:55.095252Z digest=sha256:31d51481310655335d0eed63777a3be7a98585906906c7e4d0e0e9c661e404e4

Observation cd22954b-3432-4df0-9099-e3718e0c63c6 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Proximal Policy Optimization Algorithms

Reference 24

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source=pdf_text observed=2026-08-02T15:18:55.142056Z digest=sha256:34e7ce61d3d22a49ccf0dd87af7306427ed55749e9f57f7f96315a8751f993ff

Observation 17282235-cc52-4696-ad24-cf9188a8c7fe · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 25

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source=pdf_text observed=2026-08-02T15:18:55.199751Z digest=sha256:fd876678e1b3fd79db53b9d1296e60ea941bb04d06f884e55f2b969f641ee287

Observation 369826f6-6f8d-43f4-938d-ada61f276ff6 · outbound

This paper cites Skywork-o1 open series,.

Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks Skywork-o1 open series,

Reference 26

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Pith citing papers

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