Paper Citation Record · LEDGER

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction

As of 5 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2605.09260.

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

pith.paper-citation-record.v1
2605.09260 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:52:07.910558Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:39:19.253172Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact4
verified fuzzy35
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1da33987-085e-40e7-b73e-cbec55e35859 · outbound

This paper cites Data traffic prediction for 5G and beyond: Emerging trends, challenges, and future directions: A scoping review.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Data traffic prediction for 5G and beyond: Emerging trends, challenges, and future directions: A scoping review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.570860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ed6b52c7-8533-4d13-8b33-8e1530d2c7e7 · outbound

This paper cites A vision of 6G wireless systems: Applications, trends, technologies, and open research problems.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction A vision of 6G wireless systems: Applications, trends, technologies, and open research problems

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.628304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5467d934-65a2-4f79-87ba-62ac862c2b45 · outbound

This paper cites From large AI models to agentic AI: A tutorial on future intelligent communications.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction From large AI models to agentic AI: A tutorial on future intelligent communications

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.555266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9a93af50-25b1-4949-ac79-ab45cc139921 · outbound

This paper cites A survey on modern deep neural network for traffic prediction: Trends, methods and challenges.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction A survey on modern deep neural network for traffic prediction: Trends, methods and challenges

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.586498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 946117db-47cd-429a-9c45-5dad427060b4 · outbound

This paper cites Deep learning on traffic prediction: Methods, analysis, and future directions.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Deep learning on traffic prediction: Methods, analysis, and future directions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.582560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0ec5ab97-18b7-4730-91da-feb87d1a305c · outbound

This paper cites Rl meets multi-link operation in ieee 802.11be: Multi-headed recurrent soft-actor critic-based traffic allocation.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Rl meets multi-link operation in ieee 802.11be: Multi-headed recurrent soft-actor critic-based traffic allocation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.547046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6e2c16c2-1e4e-4fd7-bc66-6cde287c7a78 · outbound

This paper cites Language models are few-shot learners.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Language models are few-shot learners

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.591165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b616973b-f3ae-4f87-bd6f-7ed3990df50f · outbound

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

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Chain-of-thought prompting elicits reasoning in large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.563367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3fa1f6b8-d7b1-49e0-ae42-095f75a39170 · outbound

This paper cites Performance analysis of network traffic predictors in the cloud.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Performance analysis of network traffic predictors in the cloud

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.578691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b5163788-f955-4cbc-b193-7439e248ac57 · outbound

This paper cites Network traffic prediction method based on autoregressive integrated moving average and adaptive volterra filter.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Network traffic prediction method based on autoregressive integrated moving average and adaptive volterra filter

Reference 10

Resolution: verified exact; doi, observed 2026-05-12T04:56:22.987824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3f1cd4e9-502d-440a-b114-6e6705567964 · outbound

This paper cites Mobile traffic prediction from raw data using LSTM networks.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Mobile traffic prediction from raw data using LSTM networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.539902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6d5588f7-7332-4091-85b6-943746ebaf7c · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Adaptive graph convolutional recurrent network for traffic forecasting

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.532336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fd72fe16-436b-45ce-996a-c4eef0c88858 · outbound

This paper cites SDGNet: A handover-aware spa- tiotemporal graph neural network for mobile traffic forecasting.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction SDGNet: A handover-aware spa- tiotemporal graph neural network for mobile traffic forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.654624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5d1d95eb-8180-4d4b-a304-0473d2636c16 · outbound

This paper cites Attention is all you need.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Attention is all you need

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.527830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 40219d84-7d04-42e7-9b12-4840d37c911e · outbound

This paper cites Mobile network traffic prediction using temporal fusion transformer.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Mobile network traffic prediction using temporal fusion transformer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.645293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8c978ad1-7b4b-411f-b1fd-7b9037039c33 · outbound

This paper cites Citywide mobile traffic forecasting using spatial-temporal downsampling transformer neural networks.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Citywide mobile traffic forecasting using spatial-temporal downsampling transformer neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.535963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7e82af45-b2b3-462b-88e1-b47f913de673 · outbound

This paper cites STTF: A spatiotemporal transformer framework for multi-task mobile network prediction.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction STTF: A spatiotemporal transformer framework for multi-task mobile network prediction

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.543437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c1f679e4-94d3-42bc-876d-946289fff353 · outbound

This paper cites A spatial- temporal transformer network for city-level cellular traffic analysis and prediction.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction A spatial- temporal transformer network for city-level cellular traffic analysis and prediction

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.550916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation de9506b6-1988-4dcc-a740-c1a7f052d376 · outbound

This paper cites Large language model (LLM) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Large language model (LLM) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.574527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ac6382ae-af5b-4ea7-8691-5d0b9026163d · outbound

This paper cites Large language models in wireless application design: In-context learning-enhanced automatic network intrusion detection.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Large language models in wireless application design: In-context learning-enhanced automatic network intrusion detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.662882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4eb3712f-42bb-4d9d-b88e-6081339e28fc · outbound

This paper cites Mobile traffic prediction using LLMs with efficient in-context demonstration selection.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Mobile traffic prediction using LLMs with efficient in-context demonstration selection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.667449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 16757ac7-a5de-4cd3-90bb-c07b226537a2 · outbound

This paper cites Self-refined generative foundation models for wireless traffic prediction.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Self-refined generative foundation models for wireless traffic prediction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.649558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 49e05e92-74fb-417b-bf69-f38c76737f55 · outbound

This paper cites LLM-based intent processing and network optimization using attention-based hierarchical reinforcement learning.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction LLM-based intent processing and network optimization using attention-based hierarchical reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.632161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 628a2b44-9a6b-4f04-8a8f-66b21529750b · outbound

This paper cites Tempo: Prompt-based generative pre-trained transformer for time series forecasting.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Tempo: Prompt-based generative pre-trained transformer for time series forecasting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.636625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 35fe12d0-8100-4c3b-a73c-ce9abdbb3070 · outbound

This paper cites LLM4TS: Align- ing pre-trained LLMs as data-efficient time-series forecasters.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction LLM4TS: Align- ing pre-trained LLMs as data-efficient time-series forecasters

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.641166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0920acfb-70d5-4aa0-a8ea-1845178bcd84 · outbound

This paper cites Reasoning AI performance degradation in 6G networks with large language models.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Reasoning AI performance degradation in 6G networks with large language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.658599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ae69836d-e1ac-4f3f-8877-49f73b0655e3 · outbound

This paper cites Chain-of-Thought for Large Language Model-empowered Wireless Communications.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Chain-of-Thought for Large Language Model-empowered Wireless Communications

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:26.263450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7057a5c7-e4d2-4bd1-9c78-4d8290c1547f · outbound

This paper cites Large language models are zero-shot reasoners.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Large language models are zero-shot reasoners

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.620513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 05fe7a34-67a1-4d95-9507-e4972c5fe581 · outbound

This paper cites A survey on in-context learning.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction A survey on in-context learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.616655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9d88dacd-0e5c-467b-9d40-e814c145eca7 · outbound

This paper cites Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.559385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 26b051f7-57ee-4879-a702-f53671375d62 · outbound

This paper cites Beyond throughput, the next generation: A 5G dataset with channel and context metrics.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Beyond throughput, the next generation: A 5G dataset with channel and context metrics

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.608522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation db156dd2-4a0e-4e0c-9d73-162643c688e9 · outbound

This paper cites Realtime mobile bandwidth and handoff predictions in 4G/5G networks.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Realtime mobile bandwidth and handoff predictions in 4G/5G networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.612211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 638aabfc-8f33-4071-b530-cb0de6205dc1 · outbound

This paper cites Openai o3 and o4-mini system card.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Openai o3 and o4-mini system card

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.624537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7a1b09e6-1b92-43e2-a3fd-49c87f3ed961 · outbound

This paper cites Permutation entropy: A natural complexity measure for time series.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Permutation entropy: A natural complexity measure for time series

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.604199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3b64ec46-1300-4d08-a703-eee5c23b1965 · outbound

This paper cites Ministral 3 3b.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Ministral 3 3b

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.566980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b7a5ef31-ae5e-4cb4-9d09-9f0598cd134f · outbound

This paper cites Qwen3 Technical Report.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Qwen3 Technical Report

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-12T05:51:26.281293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 79a9a8d6-b714-494b-bb18-01b84c5dda6c · outbound

This paper cites Phi-4-reasoning Technical Report.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Phi-4-reasoning Technical Report

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-17T03:40:25.972991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d4f32367-6032-41f6-828b-710bdb9a0062 · outbound

This paper cites LLM-inference-bench: Inference benchmark- ing of large language models on AI accelerators.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction LLM-inference-bench: Inference benchmark- ing of large language models on AI accelerators

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.595229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4b2f3397-ef89-41de-ba5e-d6208c91d5d8 · outbound

This paper cites Latency-aware joint task offloading and energy control for cooperative mobile edge computing.

Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction Latency-aware joint task offloading and energy control for cooperative mobile edge computing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T13:11:36.599873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 730d064d-5f9f-4627-a224-e76d8d3cf360 · inbound

PERA: A Perceive-Reason-Act Interface Bridging Sensing, Cognitive Reasoning, and Trustworthy Agentic Response for 6G cites this paper.

PERA: A Perceive-Reason-Act Interface Bridging Sensing, Cognitive Reasoning, and Trustworthy Agentic Response for 6G Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T19:39:19.253172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.