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Paper Citation Record · LEDGER

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.06623.

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2607.06623 v2

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measured 35 of 35 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

35 of 35 outbound references displayed

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Outbound references

Observation b3dfe519-8675-49ee-b707-f44dedca0721 · outbound

This paper cites A survey on deep learning for data-driven soft sensors,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting A survey on deep learning for data-driven soft sensors,

Reference 1

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Observation 10815532-52b7-4c35-9ae5-71705e6c73f4 · outbound

This paper cites LLM-driven human-AI collaborative decision support system for complex industrial processes: A case study in metallurgy,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting LLM-driven human-AI collaborative decision support system for complex industrial processes: A case study in metallurgy,

Reference 2

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Observation 3cad75c7-ab8c-43e2-bc35-5bba60d63c4d · outbound

This paper cites Large language models are zero-shot time series forecasters,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Large language models are zero-shot time series forecasters,

Reference 3

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Observation 1fb7c45c-23f6-458a-ac91-36d7aaec8caa · outbound

This paper cites Time-LLM: Time series forecasting by reprogramming large language models,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Time-LLM: Time series forecasting by reprogramming large language models,

Reference 4

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Observation 75446d1e-c1f0-4c7f-a8ea-298e66296a13 · outbound

This paper cites AutoTimes: Autoregressive time series forecasters via large language models,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting AutoTimes: Autoregressive time series forecasters via large language models,

Reference 5

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Observation 4e74e7f0-b30b-4179-98fe-7eeafafed3ad · outbound

This paper cites Nonlinear dynamic soft sensor modeling with supervised long short-term memory network,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Nonlinear dynamic soft sensor modeling with supervised long short-term memory network,

Reference 6

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Observation 2d9148da-4f54-433c-9e07-5ac4c763a471 · outbound

This paper cites Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes,

Reference 7

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Observation d53c6073-06f5-4af4-80ed-bb7bcc576943 · outbound

This paper cites Hybrid grid search and Bayesian optimization-based random forest regression for predicting material compression pressure in manufacturing processes,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Hybrid grid search and Bayesian optimization-based random forest regression for predicting material compression pressure in manufacturing processes,

Reference 8

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Observation d22f95d8-2784-4290-86b3-9d61ee35e3cc · outbound

This paper cites Deep learning framework for collaborative variable time delay estimation and uncertainty quantifi- cation in industrial quality prediction,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Deep learning framework for collaborative variable time delay estimation and uncertainty quantifi- cation in industrial quality prediction,

Reference 9

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Observation c7cc411a-1cf1-4d1a-8493-1176b6ff857f · outbound

This paper cites From complexity to clarity: Structural process knowledge-informed neural network for alumina concentration distribution prediction,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting From complexity to clarity: Structural process knowledge-informed neural network for alumina concentration distribution prediction,

Reference 10

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Observation e61cd9b1-f7dc-4ce2-bfc5-2c513a046f09 · outbound

This paper cites Performance-driven distillation and confident pseudo labeling for semi-supervised industrial soft-sensor application,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Performance-driven distillation and confident pseudo labeling for semi-supervised industrial soft-sensor application,

Reference 11

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Observation c373ac4f-ec2a-46da-b7e5-a3dbed9b37e9 · outbound

This paper cites PromptCast: A new prompt-based learning paradigm for time series forecasting,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting PromptCast: A new prompt-based learning paradigm for time series forecasting,

Reference 12

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Observation 514066da-b8f8-4f23-9029-eca5f2ee13c4 · outbound

This paper cites One fits all: Power general time series analysis by pretrained LM,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting One fits all: Power general time series analysis by pretrained LM,

Reference 13

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Observation 5d785da9-ed9c-4d95-b072-297fe7ad3284 · outbound

This paper cites TimeCMA: Towards LLM-empowered multivariate time se- ries forecasting via cross-modality alignment,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting TimeCMA: Towards LLM-empowered multivariate time se- ries forecasting via cross-modality alignment,

Reference 14

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This paper cites TEST: Text prototype aligned embedding to activate LLM’s ability for time series,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting TEST: Text prototype aligned embedding to activate LLM’s ability for time series,

Reference 15

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Observation f0762b79-679b-4a18-870d-1fd66d71d24f · outbound

This paper cites Zero-shot capillary segmentation in dermoscopy images via SAM2: A case study on oral mucosa,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Zero-shot capillary segmentation in dermoscopy images via SAM2: A case study on oral mucosa,

Reference 16

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Observation 4a9fc6e9-ba92-4573-bea3-73652b45343b · outbound

This paper cites Are language models actually useful for time series forecasting?.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Are language models actually useful for time series forecasting?

Reference 17

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Observation b5acd745-d900-4242-a827-2b96a7b0c951 · outbound

This paper cites A domain knowledge- guided industrial large model framework: A case study in battery health estimation and recycling,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting A domain knowledge- guided industrial large model framework: A case study in battery health estimation and recycling,

Reference 18

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Observation 89aaeb6c-bd6f-47a7-8582-a8c35df3e2d4 · outbound

This paper cites Socially aware load forecasting utilizing large language models,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Socially aware load forecasting utilizing large language models,

Reference 19

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Observation 527cacb9-a4f1-4a75-8ddd-d46b84ebf6a0 · outbound

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LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting A 2RA-NSMTSllm: Adversarially aligning retrieval-augmented LLMs for nonstationary multivariate time series forecasting,

Reference 20

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Observation 23e4eafa-6a9d-40a6-855a-ef67a402391b · outbound

This paper cites Causality-aware LLM-enhanced graph representation learning for adaptive power system control,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Causality-aware LLM-enhanced graph representation learning for adaptive power system control,

Reference 21

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Observation 7046ce8d-95a7-4f4e-8fd1-5b719ddfba5b · outbound

This paper cites Zero-shot fault diagnosis via LLM-guided complexity-aware fuzzy boundary learning,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Zero-shot fault diagnosis via LLM-guided complexity-aware fuzzy boundary learning,

Reference 22

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Observation b1e7ebec-cfae-47b7-8cda-d991a685266f · outbound

This paper cites Joint knowledge graph and large language model for fault diagnosis and its application in aviation assembly,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Joint knowledge graph and large language model for fault diagnosis and its application in aviation assembly,

Reference 23

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This paper cites Learning phrase representations using RNN encoder–decoder for statistical machine translation,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Learning phrase representations using RNN encoder–decoder for statistical machine translation,

Reference 24

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This paper cites Long short-term memory,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Long short-term memory,

Reference 25

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This paper cites Attention is all you need,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Attention is all you need,

Reference 26

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Observation 7f768ab5-c8a7-4a52-8e83-8dc7ff12ee83 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 27

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LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Mamba: Linear-time sequence modeling with selective state spaces,

Reference 28

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This paper cites iTransformer: Inverted transformers are effective for time series fore- casting,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting iTransformer: Inverted transformers are effective for time series fore- casting,

Reference 29

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LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting A time series is worth 64 words: Long-term forecasting with transformers,

Reference 30

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Observation a908fbf3-8a55-4852-a96e-c4033b3f77de · outbound

This paper cites ModernTCN: A modern pure convolution struc- ture for general time series analysis,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting ModernTCN: A modern pure convolution struc- ture for general time series analysis,

Reference 31

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Observation ebaed3f9-49ff-40a3-a1ac-120ad2af738c · outbound

This paper cites A meta-contrastive learn- ing hybrid model for adaptive temperature trend prediction in variable ladle preheating,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting A meta-contrastive learn- ing hybrid model for adaptive temperature trend prediction in variable ladle preheating,

Reference 32

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This paper cites Expert-augmented dual-stage reinforcement learning for coordinated optimization of the thickening-dewatering process,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Expert-augmented dual-stage reinforcement learning for coordinated optimization of the thickening-dewatering process,

Reference 33

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Observation 3827597d-b863-4e2b-b629-8b82f272874b · outbound

This paper cites The development of an industrial-scale fed-batch fermentation simulation,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting The development of an industrial-scale fed-batch fermentation simulation,

Reference 34

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This paper cites Additional Tennessee Eastman Process simulation data for anomaly detection evaluation,.

LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting Additional Tennessee Eastman Process simulation data for anomaly detection evaluation,

Reference 35

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