Paper Citation Record · LEDGER

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

As of 22 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2511.02748.

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

pith.paper-citation-record.v1
2511.02748 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:10:26.600611Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:51:17.661480Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T20:17:22.183742Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy0
unresolved47
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6881a5a4-92b4-45dc-bcdf-5400817846b3 · outbound

This paper cites World Models.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning World Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:21.348246Z

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Observation ab8aec98-91f9-4a32-a4d4-bdf7f22a32e9 · outbound

This paper cites World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:21.409025Z

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Observation 7a01f472-5cfa-4d21-9f2e-477c379f02f7 · outbound

This paper cites Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:21.518779Z

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Observation 415a4728-ede6-4838-ae64-f09298e66585 · outbound

This paper cites Autonomous networks: Exploring the evolution from level 0 to level 5,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Autonomous networks: Exploring the evolution from level 0 to level 5,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:21.683568Z

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Observation a5f2c95c-a7f7-4e99-86ba-8487ce6b9554 · outbound

This paper cites LLM-xApp: A Large Language Model Empowered Radio Resource Management xApp for 5G O-RAN,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning LLM-xApp: A Large Language Model Empowered Radio Resource Management xApp for 5G O-RAN,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:21.843982Z

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Observation 2c4465d4-ded7-4fa4-8bc0-75879b92f438 · outbound

This paper cites LLM-hRIC: LLM- empowered Hierarchical RAN Intelligent Control for O-RAN,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning LLM-hRIC: LLM- empowered Hierarchical RAN Intelligent Control for O-RAN,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:22.009052Z

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Observation f418bf23-5d88-4377-9e2c-5ccfa6c86689 · outbound

This paper cites Intent-Based Network for RAN Management with Large Language Models.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Intent-Based Network for RAN Management with Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:22.170468Z

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Observation 157f1519-3d28-4053-af71-8acaba8cf888 · outbound

This paper cites Language Models Need Inductive Biases to Count Inductively,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Language Models Need Inductive Biases to Count Inductively,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:22.273047Z

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Observation 4c9123fc-62bc-4a05-903b-a94d0e081368 · outbound

This paper cites Scheduled Sampling for Sequence Prediction with Re- current Neural Networks,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Scheduled Sampling for Sequence Prediction with Re- current Neural Networks,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:22.356767Z

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Observation df86f9e6-95c6-457f-9afe-78364164cca5 · outbound

This paper cites Professor Forcing: A New Algorithm for Training Recurrent Networks,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Professor Forcing: A New Algorithm for Training Recurrent Networks,

Reference 10

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Observation 5a59b05c-76eb-4164-a0f1-26d9377cc2f6 · outbound

This paper cites Off-Policy Deep Reinforcement Learning without Exploration,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Off-Policy Deep Reinforcement Learning without Exploration,

Reference 11

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Observation 7cdda349-b85c-44c1-9ae2-781348b2b399 · outbound

This paper cites Time Series Foundation Models: Benchmarking Challenges and Requirements,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Time Series Foundation Models: Benchmarking Challenges and Requirements,

Reference 12

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Observation 846690d1-dd7d-4c11-a99f-62db5484493f · outbound

This paper cites Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting

Reference 13

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Observation 7f49861a-5592-473c-ae89-f1743c6abfb2 · outbound

This paper cites Counterfactual Dynamics Forecasting — A New Setting of Spatio-Temporal Forecasting,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Counterfactual Dynamics Forecasting — A New Setting of Spatio-Temporal Forecasting,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:22.897008Z

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Observation 2d9cf9ff-48c9-4ae5-b729-7e4dc8f6ba9f · outbound

This paper cites Understanding Physical Dynamics with Counterfactual World Modeling.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Understanding Physical Dynamics with Counterfactual World Modeling

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.022356Z

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Observation 80c5af2c-a485-4dfd-934d-d3d8c56efd2f · outbound

This paper cites Simplified State Space Layers for Sequence Modeling,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Simplified State Space Layers for Sequence Modeling,

Reference 16

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Observation 952e02b8-e083-4302-beb6-8d462dd3687c · outbound

This paper cites Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Rivaling Transformers: Multi-Scale Structured State-Space Mixtures for Agentic 6G O-RAN

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.222833Z

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Observation d230f6b6-5e4f-40eb-9198-7fd60c230058 · outbound

This paper cites Stochastic Backpropaga- tion and Approximate Inference in Deep Generative Models,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Stochastic Backpropaga- tion and Approximate Inference in Deep Generative Models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.315915Z

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Observation 57df26e5-b194-4c6b-9d03-684fc31c067d · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 19

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Observation 4bba7b0f-7f27-4a61-8a1d-34c04bd08272 · outbound

This paper cites an unresolved cited work.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.496340Z

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Observation bc7d25cb-5045-4fe4-bc15-59733c3b4744 · outbound

This paper cites The Cross-Entropy Method for Combinatorial and Continuous Optimization,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning The Cross-Entropy Method for Combinatorial and Continuous Optimization,

Reference 21

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Observation 3c925287-ef55-4873-80a9-4e9c6be2023a · outbound

This paper cites Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.841161Z

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Observation 579ab157-1afa-4f9a-a842-e74a5307fc7a · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning RWKV: Reinventing RNNs for the Transformer Era

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.964420Z

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Observation eac01e81-08af-4dbe-b484-d056824fa147 · outbound

This paper cites Rethinking Attention with Performers,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Rethinking Attention with Performers,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:24.041086Z

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Observation 7ede2026-a87c-44dc-a0ea-cefb72fa9606 · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Retentive Network: A Successor to Transformer for Large Language Models

Reference 25

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Observation f935be5a-4462-4499-bec6-c9ccea94091c · outbound

This paper cites Chronos: Learning the Language of Time Series,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Chronos: Learning the Language of Time Series,

Reference 26

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Observation 606c9c6a-c8da-41a3-85c1-e4021351c3c3 · outbound

This paper cites HiPPO: Recurrent Memory with Optimal Polynomial Projections,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning HiPPO: Recurrent Memory with Optimal Polynomial Projections,

Reference 27

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Observation 561c8212-c6f7-4bfb-bfcf-1c028f18a660 · outbound

This paper cites Dreamer: Reinforcement Learning with World Models,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Dreamer: Reinforcement Learning with World Models,

Reference 28

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Observation c113cfef-4767-42ef-83d6-f4f1ab20684f · outbound

This paper cites Mastering Atari with Discrete World Models,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Mastering Atari with Discrete World Models,

Reference 29

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Observation 08359413-a3ad-49cc-a363-5d9aaa685abf · outbound

This paper cites Dyna, an Integrated Architecture for Learning, Planning, and Reacting,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Dyna, an Integrated Architecture for Learning, Planning, and Reacting,

Reference 30

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Observation 9ea1ff79-4dc5-4e79-aff6-1a92b3ddb57a · outbound

This paper cites Generalized Kullback-Leibler Divergence Loss.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Generalized Kullback-Leibler Divergence Loss

Reference 31

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Observation f2a4859b-cc96-411f-a12c-e885fd7b9b83 · outbound

This paper cites (2023) O-RAN Architecture Overview.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning (2023) O-RAN Architecture Overview

Reference 32

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Observation 2f744ce2-acb7-4281-9975-5dcaf5730004 · outbound

This paper cites ETSI TS 104 040 V4.0.0: Publicly Available Specification (PAS); E2 interface: RAN Function-specific Service Models (O-RAN E2SMs),.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning ETSI TS 104 040 V4.0.0: Publicly Available Specification (PAS); E2 interface: RAN Function-specific Service Models (O-RAN E2SMs),

Reference 33

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Observation 352c1340-0ec4-4d90-8a66-216de67fd74b · outbound

This paper cites Squeeze-and-Excitation Networks,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Squeeze-and-Excitation Networks,

Reference 34

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Observation 58b1d052-05a7-4a9d-91f4-99067840cf06 · outbound

This paper cites Language Modeling with Gated Convolutional Networks,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Language Modeling with Gated Convolutional Networks,

Reference 35

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Observation 1d5c39c6-b17a-4a85-ac4a-847fd2c0f987 · outbound

This paper cites Autoregressive Models: What Are They Good For?.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Autoregressive Models: What Are They Good For?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:25.304242Z

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Observation a1b14ca5-91af-4cd8-94ed-b121194fea89 · outbound

This paper cites an unresolved cited work.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Unresolved cited work

Reference 37

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Observation b712fb72-7a1e-40fb-bb7c-8615bd46471d · outbound

This paper cites Deep Learning for Time Series Forecasting: Tutorial and Survey,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Deep Learning for Time Series Forecasting: Tutorial and Survey,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:25.559763Z

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Observation 4c9e3ca6-26ab-445b-ad3c-301cb8c2e1cc · outbound

This paper cites DeepAR: Probabilistic forecasting with autoregressive recurrent networks,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning DeepAR: Probabilistic forecasting with autoregressive recurrent networks,

Reference 39

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Observation 84e82a12-4c40-49c9-8fc9-b2ba3df2dbce · outbound

This paper cites an unresolved cited work.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Unresolved cited work

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:25.779082Z

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Observation 0d03b3be-5cd3-4a01-996e-73fa2b56f4fa · outbound

This paper cites Preservation of Common Quadratic Lyapunov Functions and Pad ´e Approximations,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Preservation of Common Quadratic Lyapunov Functions and Pad ´e Approximations,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:25.922675Z

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Observation aa865c27-94ca-46b6-bf75-c2cb6bec9c6a · outbound

This paper cites On Pad ´e Approximations, Quadratic Stability and Discretization of Switched Linear Systems,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning On Pad ´e Approximations, Quadratic Stability and Discretization of Switched Linear Systems,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:26.084339Z

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Unavailable: canonical work link unavailable.

Observation a0fb5104-cde4-48ee-8865-b5c436f49b76 · outbound

This paper cites Auto-Encoding Variational Bayes,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Auto-Encoding Variational Bayes,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:26.306817Z

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Observation 11c1362c-a6a1-4480-a6d0-94facb265e23 · outbound

This paper cites an unresolved cited work.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Unresolved cited work

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:26.422423Z

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Observation 4e44748c-169b-4a31-80af-0a9ea8345d38 · outbound

This paper cites An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:26.498793Z

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Observation 6105f0d6-eb4b-4391-bab2-f2107c8c39bd · outbound

This paper cites Learning Low-Dimensional Representation for O-RAN Testing via Transformer- ESN,.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Learning Low-Dimensional Representation for O-RAN Testing via Transformer- ESN,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:26.600611Z

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Observation 8f67093b-bca1-481b-b1ec-5ae0778e4393 · outbound

This paper cites Available: https://sites.engineering.ucsb.edu/ ∼jbraw/mpc/ MPC-book-2nd-edition-1st-printing.pdf.

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning Available: https://sites.engineering.ucsb.edu/ ∼jbraw/mpc/ MPC-book-2nd-edition-1st-printing.pdf

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T00:10:23.592125Z

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Pith citing papers

Observation 7c875a2e-acff-40c1-96f5-9bf3c8b22d49 · inbound

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk cites this paper.

LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-08T02:03:46.870871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dce4948f-3367-4c2a-aac6-961abb279744 · inbound

LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G cites this paper.

LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T09:51:17.661480Z

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Observation e6049fdb-2d43-43d6-a704-691927581ea1 · inbound

Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks cites this paper.

Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T20:17:22.184991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.