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Paper Citation Record · LEDGER

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN

As of 7 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 4 inbound Pith citation observations for arXiv:2507.21696.

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

pith.paper-citation-record.v1
2507.21696 v4

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:30:39.990628Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:20:59.139146Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T01:23:49.505105Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact1
  • verified fuzzy19
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f391563b-82f7-4994-afe1-b5c2dc74ac18 · outbound

This paper cites Transition technologies towards 6g networks.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Transition technologies towards 6g networks

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-06T12:30:40.492585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.917694Z digest=sha256:550a4b740658be09bbdf05d71569c67095b3095e347d9073d5e9d97b877ca203

Observation e42e854c-f8b1-47a2-b805-ff8d2d514ca3 · outbound

This paper cites Oran-map: A hybrid approach to mobility-aware power optimisation in open radio access networks (oran).

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Oran-map: A hybrid approach to mobility-aware power optimisation in open radio access networks (oran)

Reference 2

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raw_fallback, observed 2026-08-06T12:30:40.485539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.920853Z digest=sha256:8a0942a8ce8ee190c0d0bc280219b19f9f7fb9e0f8c7742fca14a332999a1d1c

Observation 238e33b7-016d-403d-93a2-f0447d1e88f2 · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 3

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no resolver link, observed 2026-08-06T12:30:39.923932Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T12:30:39.923932Z digest=sha256:62ffec8dda7987d24b16eca1f6b50d1f9e4cc8acfb3608647470cd6239709cf9

Observation ee6a414e-d480-4cef-86a3-49f1a13b1e2a · outbound

This paper cites Llm-driven agentic ai approach to enhanced o-ran resilience in next-generation networks, May 2025.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Llm-driven agentic ai approach to enhanced o-ran resilience in next-generation networks, May 2025

Reference 4

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raw_fallback, observed 2026-08-06T12:30:40.478248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.926854Z digest=sha256:066ca5d37c2a14873a4831a9bbae98604afb0b14dfab45f938f64155979f5eb8

Observation 5caca143-129c-48e6-bde7-b0835de67397 · outbound

This paper cites PersonaGym: Evaluating Persona Agents and LLMs.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN PersonaGym: Evaluating Persona Agents and LLMs

Reference 5

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no resolver link, observed 2026-08-06T12:30:39.929548Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T12:30:39.929548Z digest=sha256:f606e789734f1c360183f3edae4549995488f826dd4b2d80b526dbb4f7693382

Observation 3c14a9fa-c4c3-4ffe-8301-c7cbd3ed3198 · outbound

This paper cites ChatHaruhi: Reviving Anime Character in Reality via Large Language Model.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN ChatHaruhi: Reviving Anime Character in Reality via Large Language Model

Reference 6

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T12:30:39.932289Z digest=sha256:ba29755bcc7882c878f4f61cdbe498b1e2a60f360bdb83186597e638d8dd8b09

Observation 94ae557a-b676-4afa-8b18-7ba81ddfa065 · outbound

This paper cites Better Zero-Shot Reasoning with Role-Play Prompting.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Better Zero-Shot Reasoning with Role-Play Prompting

Reference 7

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source=pdf_text observed=2026-08-06T12:30:39.935329Z digest=sha256:2f11270f903e451eaaa1e657e91caf771feae2a4f7b49e92a030fa0c181ef409

Observation 23fe2058-792e-4e80-bdcc-0e369a37b1a6 · outbound

This paper cites Softbank corp.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Softbank corp

Reference 8

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raw_fallback, observed 2026-08-06T12:30:40.470457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.938030Z digest=sha256:913485aaf0597eb951ae00c81ed51b9f8529a74bce2dc8969cba2fc017d5d28c

Observation 96a7546e-86be-4292-b453-a3c0d6de210e · outbound

This paper cites Nvidia ai aerial launches to optimize wireless networks, deliver new generative ai experiences on one platform.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Nvidia ai aerial launches to optimize wireless networks, deliver new generative ai experiences on one platform

Reference 9

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raw_fallback, observed 2026-08-06T12:30:40.463338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.940588Z digest=sha256:63c95ad5c687252634b509b922bfd5cc7686296d69cfb0399906f0db6211ae94

Observation 092198e0-1352-44d9-8433-070820056031 · outbound

This paper cites Evaluating generative ai for telecom.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Evaluating generative ai for telecom

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.943007Z digest=sha256:fab194eaa4861727e575e6c3f1bc94f4205647a9723b54cc6ca439c354dc805e

Observation 64ff1af3-7297-4439-9910-8c7214c314f1 · outbound

This paper cites Deploy ai-ran at cell sites with nvidia arc-compact.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Deploy ai-ran at cell sites with nvidia arc-compact

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.945392Z digest=sha256:8f6cf48e6b4fcaeff83a7d1a5139a7cb972cab6b481244cee213ba7f6f1c0410

Observation 81d871da-0d45-4dd6-b3d4-418e0a4c11e4 · outbound

This paper cites Joint admission control and resource provisioning for urllc traffic in o-ran: A constrained multi- agent reinforcement learning approach, May 2025.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Joint admission control and resource provisioning for urllc traffic in o-ran: A constrained multi- agent reinforcement learning approach, May 2025

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.947687Z digest=sha256:fcd610a0e77e238607306b0613be87bba7bcea92c2aaf7f3112fb387dd5c27da

Observation 08c97512-c742-47f2-9c30-88980a06ef6f · outbound

This paper cites Explainable ai in 6g o-ran: A tutorial and survey on architecture, use cases, challenges, and future research.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Explainable ai in 6g o-ran: A tutorial and survey on architecture, use cases, challenges, and future research

Reference 13

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raw_fallback, observed 2026-08-06T12:30:40.434277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.950403Z digest=sha256:802c4db5e836ce985303f8f3f785ad125305cda00883708d742ad973931b3860

Observation 54a45e1e-c13c-43dd-86f1-1808bf9e6e5b · outbound

This paper cites FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers

Reference 14

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local_arxiv, observed 2026-08-06T12:30:40.317976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.952676Z digest=sha256:e227c08a9210e423eb3fbbecf8a82ff144a3f2d437c89a21e10b91b743d4dca5

Observation 93515012-64ed-4494-940a-046c27959dbc · outbound

This paper cites Alympics: Llm agents meet game theory.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Alympics: Llm agents meet game theory

Reference 15

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raw_fallback, observed 2026-08-06T12:30:40.426344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.955389Z digest=sha256:10bb45f6d711622db1f0349ef2372178db01780fe140eca77dd9b7b86e48c9b9

Observation bcf4d2af-5ec5-44a0-8e26-7743d8ec65f3 · outbound

This paper cites TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge

Reference 16

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T12:30:39.957816Z digest=sha256:d62997f4ab2318d3c1070fba31439e1dc46a092e544a4bea7585e028c6265adb

Observation c237ae26-c389-4a95-863a-b0cd88442a78 · outbound

This paper cites Advanced architectures integrated with agentic ai for next-generation wireless networks.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Advanced architectures integrated with agentic ai for next-generation wireless networks

Reference 17

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source=pdf_text observed=2026-08-06T12:30:39.960572Z digest=sha256:2591ea8aa416d26d72bf283f66d5c7852020af6518391927a2c5c21ee83cb6a9

Observation 31566e6e-be2c-49fb-9631-feb76d533484 · outbound

This paper cites The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms

Reference 18

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no resolver link, observed 2026-08-06T12:30:39.963291Z

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source=pdf_text observed=2026-08-06T12:30:39.963291Z digest=sha256:f870035f231b6bf5ff2c4325d66c7403bdc72abdbadc8e7c116a60f1b04f64bd

Observation 9561de62-4b4c-454a-8a7c-dfca391392c1 · outbound

This paper cites Llm-based policy generation for intent-based management of applications.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Llm-based policy generation for intent-based management of applications

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-06T12:30:40.419127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.965892Z digest=sha256:ad8e6ae493244ea2905158fe1ffead6e30e0b90a0ab45dc8647d7f4270c6c74e

Observation 098e7ec8-9da6-4c7d-816d-a71c958df219 · outbound

This paper cites What do llms need to synthesize correct router configurations? In Proceedings of the 22nd ACM Workshop on Hot Topics in Networks , pages 189–195, 2023.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN What do llms need to synthesize correct router configurations? In Proceedings of the 22nd ACM Workshop on Hot Topics in Networks , pages 189–195, 2023

Reference 20

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raw_fallback, observed 2026-08-06T12:30:40.411654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.968212Z digest=sha256:b3f786699257ab1214c693c2b05fa0a52e9f54b151ff4cb258df5579d33b6e4f

Observation ae791450-971f-480e-b54d-f3088c3ae928 · outbound

This paper cites Toward reproducing network research results using large language models.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Toward reproducing network research results using large language models

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.970927Z digest=sha256:dcb398c00c821eee71bf22b7e34e67a731ac6ea6252268e4bab76b32097509e0

Observation 467b3dea-1ad4-46a1-87c6-9297f82036ab · outbound

This paper cites Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence

Reference 22

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source=pdf_text observed=2026-08-06T12:30:39.973326Z digest=sha256:e016cab326473561f9b89d125276b4147ef2b92b58e66870adff0384bfed9639

Observation 61a75cbb-ee2a-4deb-9485-ccdbb7e86547 · outbound

This paper cites Edgefm: Leveraging foundation model for open-set learning on the edge.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Edgefm: Leveraging foundation model for open-set learning on the edge

Reference 23

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raw_fallback, observed 2026-08-06T12:30:40.396267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.976006Z digest=sha256:23be640e911d1e4dbf6aa2126098a9354d07aacc4ac80e388cfce26e199e72a4

Observation aa6cdb45-454b-4820-a9c3-9def87f41ace · outbound

This paper cites Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification

Reference 24

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raw_fallback, observed 2026-08-06T12:30:40.388059Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.978374Z digest=sha256:90255a9992a71f09ad96549b3786c7ca60686969da15380c5116aacd68c80563

Observation 158fe600-ef6b-423d-b6c2-3bc5a8a134f2 · outbound

This paper cites Reward Design with Language Models.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Reward Design with Language Models

Reference 25

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source=pdf_text observed=2026-08-06T12:30:39.980782Z digest=sha256:f4695b2abe0c7ca9d986236d5fcb874115e0c977712072869f8c2b83e842002c

Observation 937dce6a-a013-4647-9c9a-bcadbe7fe286 · outbound

This paper cites Diagnosing infeasible optimization problems using large language models.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Diagnosing infeasible optimization problems using large language models

Reference 26

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raw_fallback, observed 2026-08-06T12:30:40.380056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.983325Z digest=sha256:ee2b5fe8a2361e325efbbca44da466d7289bdb66d04617a47d03e3698847cdfb

Observation 1b8ae0db-3548-42da-bda2-7804bcd63136 · outbound

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

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Large language models empowered autonomous edge ai for connected intelligence

Reference 27

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raw_fallback, observed 2026-08-06T12:30:40.372184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.985637Z digest=sha256:e18a0542fb8485f21d8a84f550e83137f887f6110c20dbb7a91a789f19f78e48

Observation 18a097ec-9a91-439d-a7e9-4787d6974554 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 28

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raw_fallback, observed 2026-08-06T12:30:40.364257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.988019Z digest=sha256:b03b160fbbc72fed6614fc2e204bf8cd613c3095b24a6836d97ab8b854497af8

Observation f586b3be-fe30-45e3-a597-eae6beaad537 · outbound

This paper cites Mobile- llama: Instruction fine-tuning open-source llm for network analysis in 5g networks.

Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN Mobile- llama: Instruction fine-tuning open-source llm for network analysis in 5g networks

Reference 29

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raw_fallback, observed 2026-08-06T12:30:40.356342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T12:30:39.990628Z digest=sha256:6fe14c34a395b9eb5128693ffe4fbf2fb0f062ff5d0dfec7fdac09535b1237f2

Pith citing papers

Observation 163f1d8d-fc60-4952-932d-e9b1d469ef6d · 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 Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN

Reference 46

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no resolver link, observed 2026-08-05T16:20:59.139146Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:20:59.139146Z digest=sha256:95c96d6309d55ac15ac54920a68d18d05268c1ddee8c8028191bb71fd13b69cb

Observation 521ce2b1-9303-49b0-b777-fc26bcce037e · inbound

Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance cites this paper.

Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN

Reference 10

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arxiv_id, observed 2026-05-16T22:43:37.802322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T22:43:20.760265Z digest=sha256:60114fd208b795ba75d2f64d57add43a0ce84274dade7c1db109c5601ac43fb5

Observation 68f9dfa0-dc1f-4a65-bbdf-02639badb5df · inbound

Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows cites this paper.

Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN

Reference 13

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arxiv_id, observed 2026-05-17T01:23:49.507787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-17T01:21:33.692839Z digest=sha256:44a622d52b6c30e9985daca2dee5d1155b877a8b52e09e22226a341a5e6e29f6

Observation 8fb3a77f-f4cc-42c1-8d5a-f834bbbb8c8f · inbound

Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models cites this paper.

Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models Edge Agentic AI Framework for Autonomous Network Optimisation in O-RAN

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:01:43.536920Z digest=sha256:256dd632c645790d96e6424c1c1c9b8b4f638014fba2671f2418369da8e2395d