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Paper Citation Record · LEDGER

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

As of 21 August 2026, this Paper Citation Record lists 100 of 138 outbound references and 3 inbound Pith citation observations for arXiv:2506.24124.

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

pith.paper-citation-record.v1
2506.24124 v2

Coverage vector

measured 100 of 138 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:29:05.314870Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T01:29:58.474810Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 138 outbound references displayed

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External citation measurements

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 12aad24f-4c1e-406c-b27d-1b7eb409f167 · outbound

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Chronos: Learning the Language of Time Series

Reference 1

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Observation e4672016-a5af-41c7-88cd-de48d2772c31 · outbound

This paper cites Layer Normalization.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Layer Normalization

Reference 2

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Observation ff9ee70f-bbe6-4161-929c-375b1165a6cf · outbound

This paper cites Privacy preserving generative feature transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Privacy preserving generative feature transformation

Reference 3

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Observation b2d385e1-2244-432c-b160-6c549fbb885a · outbound

This paper cites Gorec: a generative cold-start recommendation framework.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Gorec: a generative cold-start recommendation framework

Reference 4

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Observation ad68d57d-d7fb-4549-8bea-0376225aa81f · outbound

This paper cites Multimodality invariant learning for multimedia-based new item recommendation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Multimodality invariant learning for multimedia-based new item recommendation

Reference 5

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Observation 0889fec0-b78d-4e49-a1a1-f61225b3fb93 · outbound

This paper cites Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage

Reference 6

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Observation 3ea3dc0a-3a32-4928-83f7-f4879170aa3c · outbound

This paper cites Deep learning and time series-to-image encoding for finan- cial forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Deep learning and time series-to-image encoding for finan- cial forecasting

Reference 7

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Observation 7620ff6f-c6f1-484d-b88d-a3495ed39952 · outbound

This paper cites Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation

Reference 8

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Observation cb1d611f-4fea-4843-b258-3e9ee3ccafad · outbound

This paper cites Control charts in financial applications: An overview.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Control charts in financial applications: An overview

Reference 9

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Observation a5f2d7b9-7b50-4880-bcd4-d23756c7c686 · outbound

This paper cites Language models are few-shot learners.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Language models are few-shot learners

Reference 10

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Observation 48333e52-ec0f-4a8a-9809-899cd7f2d88f · outbound

This paper cites Time series forecasting for healthcare diagnosis and prognostics with the focus on cardiovascular diseases.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time series forecasting for healthcare diagnosis and prognostics with the focus on cardiovascular diseases

Reference 11

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Observation c9cbc451-dea2-4878-a69d-1f5034779d70 · outbound

This paper cites From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals

Reference 12

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Observation 3aea84ee-749e-4558-82b7-d8de20d84d55 · outbound

This paper cites Lightts: Lightweight time series classification with adaptive ensemble distillation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Lightts: Lightweight time series classification with adaptive ensemble distillation

Reference 13

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Observation 2ce06a8f-22ac-4381-9d57-6b96aaa98ed3 · outbound

This paper cites LocVTP: Video-Text Pre-training for Temporal Localization.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LocVTP: Video-Text Pre-training for Temporal Localization

Reference 14

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Observation fa67aee7-565e-495a-9fe2-7ae42c9ee75e · outbound

This paper cites Nhits: Neural hierarchical interpolation for time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Nhits: Neural hierarchical interpolation for time series forecasting

Reference 15

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Observation 66be7787-6e16-469f-84fd-7187b163156b · outbound

This paper cites Multi- model approach for stock price prediction and trading recommendations.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Multi- model approach for stock price prediction and trading recommendations

Reference 16

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Observation f4199341-2d8e-45ab-b958-1587667f075a · outbound

This paper cites Financial time series forecasting with multi-modality graph neural network.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Financial time series forecasting with multi-modality graph neural network

Reference 17

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Observation 965b7b11-eece-4c84-b22e-5c464ee84d34 · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 18

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Observation 1498ee0f-abdc-460f-b88e-4a487efcc00d · outbound

This paper cites TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling

Reference 19

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Observation 6605f5ff-d14f-44bc-a0d4-b2498d82caf4 · outbound

This paper cites Weakly supervised video representation learning with unaligned text for sequential videos.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Weakly supervised video representation learning with unaligned text for sequential videos

Reference 20

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Observation 8558c4ef-66fc-4f02-83bf-3fe25524dd33 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 21

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Observation a551c215-e2d7-4598-8764-5cd2377f00f4 · outbound

This paper cites LANISTR: Multimodal Learning from Structured and Unstructured Data.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LANISTR: Multimodal Learning from Structured and Unstructured Data

Reference 22

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Observation f8e67e57-8267-40b8-941d-9cf3c8b00189 · outbound

This paper cites Unsupervised scalable repre- sentation learning for multivariate time series.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Unsupervised scalable repre- sentation learning for multivariate time series

Reference 23

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Observation d3b0f1d2-bdd1-4d87-9c78-c8024e18cb72 · outbound

This paper cites Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

Reference 24

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Observation 2402a515-8451-4756-a82f-890d49f7306e · outbound

This paper cites Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation

Reference 25

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Observation a3dec871-0c2a-4322-a69f-a47baacbfa58 · outbound

This paper cites Evolutionary large language model for automated feature transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Evolutionary large language model for automated feature transformation

Reference 26

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Observation dc277e5c-1e83-4425-a8bb-106ec7f42b40 · outbound

This paper cites Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming

Reference 27

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 357861e3-64f2-4712-ad87-6460106ed578 · outbound

This paper cites Neuro-symbolic embedding for short and effective feature selection via autoregressive generation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Neuro-symbolic embedding for short and effective feature selection via autoregressive generation

Reference 28

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Observation ff69d990-fea8-4712-a214-5936477b5c6f · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives MOMENT: A Family of Open Time-series Foundation Models

Reference 29

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Observation 3a5dc60a-625c-468c-8e54-5c264a936fc2 · outbound

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

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Large language models are zero-shot time series forecasters

Reference 30

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Observation dd212c19-eb43-465f-94af-a2fd243970d1 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Efficiently Modeling Long Sequences with Structured State Spaces

Reference 31

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Observation e0554412-4d71-469c-ac49-7eb65ca4e259 · outbound

This paper cites Audioclip: Extending clip to image, text and audio.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Audioclip: Extending clip to image, text and audio

Reference 32

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Observation 93236bc6-6cba-4335-ab44-958f13b2417b · outbound

This paper cites Temporal alignment networks for long- term video.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Temporal alignment networks for long- term video

Reference 33

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Observation 745fe211-ea90-42fa-b86a-b6f6ad5b3695 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 34

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Observation 81dba392-922a-4938-a472-5a701c5f2bcd · outbound

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Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Deep residual learning for image recognition

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Observation 3035ca14-c920-40dc-a34f-541a7b0f77b4 · outbound

This paper cites Double correction framework for denoising recommendation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Double correction framework for denoising recommendation

Reference 36

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Observation 4a538c54-e9e8-492e-98d0-c9320fdadd15 · outbound

This paper cites Long short-term memory.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long short-term memory

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Observation 23724760-8b4d-4a06-956c-f6d1a145cea9 · outbound

This paper cites Transrac: Encoding multi-scale temporal correlation with transformers for repetitive action counting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Transrac: Encoding multi-scale temporal correlation with transformers for repetitive action counting

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Observation df2c1207-45b9-465a-8f84-55e5febb6d0e · outbound

This paper cites Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting

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Observation 6c5fca9c-7645-485b-84fc-7d85708a9101 · outbound

This paper cites Gpt4mts: Prompt-based large language model for multimodal time-series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Gpt4mts: Prompt-based large language model for multimodal time-series forecasting

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Observation 09c60e33-9d1b-4142-b086-c2ad391f4f9a · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

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Observation ebcc2524-0b47-4275-a195-439642c21a63 · outbound

This paper cites Position: What can large language models tell us about time series analysis.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Position: What can large language models tell us about time series analysis

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Observation cc426686-a8be-4e6d-ad4b-72fc1a8a1171 · outbound

This paper cites Ai in healthcare: time-series forecasting using statistical, neural, and ensemble architectures.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Ai in healthcare: time-series forecasting using statistical, neural, and ensemble architectures

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Observation cd0a5b4b-5790-4b3b-b2a2-47b0999c8f43 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Bert: Pre-training of deep bidirectional transformers for language understanding

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Observation 0549d233-f528-47fe-ab19-7c8a69a14404 · outbound

This paper cites Reformer: The Efficient Transformer.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Reformer: The Efficient Transformer

Reference 46

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Observation 60be177e-e1d4-4a3b-853f-55cb20291c3e · outbound

This paper cites LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models

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Observation 85681163-5902-4ad8-9c2e-222e9aa2137e · outbound

This paper cites Sehf: A summary- enhanced hierarchical framework for financial report sentiment analysis.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Sehf: A summary- enhanced hierarchical framework for financial report sentiment analysis

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Observation c696f405-ae9b-4d0e-9541-322d02010e7f · outbound

This paper cites Sade: A speaker- aware dual encoding model based on diagbert for medical triage and pre-diagnosis.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Sade: A speaker- aware dual encoding model based on diagbert for medical triage and pre-diagnosis

Reference 49

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Observation 24431474-6f97-4885-a0ff-67268a44f9be · outbound

This paper cites Frozen language model helps ecg zero-shot learning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Frozen language model helps ecg zero-shot learning

Reference 50

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Observation b130c929-f07e-4c8a-830d-9c9e01f1e9e1 · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models

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Observation af2f22b6-21ba-46ab-afb4-c370dc8eafbd · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives VideoChat: Chat-Centric Video Understanding

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Observation aa420b5e-e238-4180-8fc4-61b8cf5e5151 · outbound

This paper cites Clip-event: Connecting text and images with event structures.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Clip-event: Connecting text and images with event structures

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Observation 6b59d7f7-01f5-4599-8fef-2b37ccd6bad2 · outbound

This paper cites Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting

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Observation b6de7484-019b-4e50-b757-498817c3b545 · outbound

This paper cites Deep learning models for time series forecasting: a review.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Deep learning models for time series forecasting: a review

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Observation 31a0e8ad-e176-495f-b3e0-71089a6034eb · outbound

This paper cites Forecasting with time series imaging.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Forecasting with time series imaging

Reference 56

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Observation da2b637c-03c0-41c8-ad19-7c9321028db5 · outbound

This paper cites Time series as images: Vision transformer for irregularly sampled time series.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Time series as images: Vision transformer for irregularly sampled time series

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Observation dd3481ad-3e57-44a6-9dc9-478e0c4fa6f2 · outbound

This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

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Observation 71d8c771-4547-453e-bd3c-b0c49dc6d522 · outbound

This paper cites SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting

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Observation df36f1cf-1232-4907-9b6b-d5a3efe2a2e0 · outbound

This paper cites Pth and the regulation of mesenchymal cells within the bone marrow niche.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Pth and the regulation of mesenchymal cells within the bone marrow niche

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Observation 47fb0c6e-05a6-4874-82ff-1185c8628f69 · outbound

This paper cites Visual instruction tuning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Visual instruction tuning

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Observation d66ff5d9-1eb6-493b-9437-89ff3428ee81 · outbound

This paper cites LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting

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Observation 4a0ec73e-067e-414a-b936-e089c56b433a · outbound

This paper cites Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression

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Observation f079f970-d6c2-4b8b-81fa-36e8c19e0858 · outbound

This paper cites Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling

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Observation a5eabf00-0e70-4447-9965-89d2782fd179 · outbound

This paper cites Scinet: Time series modeling and forecasting with sample convolution and interaction.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Scinet: Time series modeling and forecasting with sample convolution and interaction

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Observation 9a76fdb9-5d29-4675-9ce3-80e629141c7b · outbound

This paper cites Focal: Contrastive learning for multimodal time- series sensing signals in factorized orthogonal latent space.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Focal: Contrastive learning for multimodal time- series sensing signals in factorized orthogonal latent space

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Observation d2e4bba7-044d-42cc-acda-4ee820ac12ae · outbound

This paper cites Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting

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Observation 4b2c9914-8523-4ba1-8411-c6237ef1f62e · outbound

This paper cites Unitime: A language-empowered unified model for cross-domain time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Unitime: A language-empowered unified model for cross-domain time series forecasting

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Observation 4c9ab9be-20b4-46a3-8beb-cd07e9c6b4b0 · outbound

This paper cites Non-stationary transformers: Ex- ploring the stationarity in time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Non-stationary transformers: Ex- ploring the stationarity in time series forecasting

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Observation be74caf6-ba53-476d-8b93-c2718966e95a · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

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Observation aacc89a5-f6e3-4e5e-adac-90cc3a3a59ec · outbound

This paper cites AutoTimes: Autoregressive Time Series Forecasters via Large Language Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

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Observation 78bc09ea-079c-4151-883d-5eca9fe8ee5a · outbound

This paper cites Timer: Generative pre-trained transformers are large time series models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Timer: Generative pre-trained transformers are large time series models

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Observation 6315744b-487e-448f-8e59-6245bfa6ae04 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Swin transformer: Hierarchical vision transformer using shifted windows

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Observation b23abbb3-d2c6-4d74-ac0e-27b48ca99517 · outbound

This paper cites A cnn-bilstm-am method for stock price prediction.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A cnn-bilstm-am method for stock price prediction

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Observation f575aaf1-cb54-4661-a09f-f84009812730 · outbound

This paper cites Howto100m: Learning a text-video embedding by watching hundred million narrated video clips.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Howto100m: Learning a text-video embedding by watching hundred million narrated video clips

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Observation 5e4334fc-5e37-4b23-87fa-3f56cd0bbb56 · outbound

This paper cites Expanding language-image pretrained models for general video recognition.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Expanding language-image pretrained models for general video recognition

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Observation a5c6dda5-0abd-44f3-a870-11a65f1f7a81 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

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Observation e5c409ef-0a7d-4e52-9790-1bf5715c70ae · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Representation Learning with Contrastive Predictive Coding

Reference 78

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source=pdf_text observed=2026-08-06T21:29:02.114667Z digest=sha256:f1c81718a0a1e0df40277574d6db5333367e10587dfb8409d978a767ed3abbed

Observation 66d8dbab-91c0-4d5c-af8e-9b3c745ff5cd · outbound

This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 79

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Observation 2ee961b2-2bc9-4391-af25-348f23a172a0 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Pytorch: An imperative style, high-performance deep learning library

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Observation a9d6c929-8b36-4fb5-b390-bda00216289a · outbound

This paper cites Learning transferable visual models from natural language supervision.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Learning transferable visual models from natural language supervision

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Observation f67a7772-c1b6-4031-ba0c-3ced2c7c61a6 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 82

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source=pdf_text observed=2026-08-06T21:29:02.586917Z digest=sha256:1d1f9edaccf3b3ca43e896bb5d2e2d18d44fdabc0abfff6e03e0200d34bf33ca

Observation f8810c08-9755-4108-a7ad-296623248da6 · outbound

This paper cites Automatic diagnosis of the 12-lead ecg using a deep neural network.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Automatic diagnosis of the 12-lead ecg using a deep neural network

Reference 83

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Observation bc625d19-e4bd-4c4d-8895-a6d879bf533a · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives High-resolution image synthesis with latent diffusion models

Reference 84

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source=pdf_text observed=2026-08-06T21:29:02.918008Z digest=sha256:bffb28628f3e9262e5dfa498b7cb53cfbb117fc9d2e095e330e02ce00a7d5d7a

Observation 1a3a896a-5155-47dd-9c0e-f3188e70785a · outbound

This paper cites A review of deep learning techniques for forecasting energy use in buildings.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A review of deep learning techniques for forecasting energy use in buildings

Reference 85

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source=pdf_text observed=2026-08-06T21:29:03.084368Z digest=sha256:87c9c436d70e2a0013914292ee93d8e9791561ea5169d5c9aad6f235eda65862

Observation b31f76a3-7a37-41bb-85ed-3265589db840 · outbound

This paper cites Image- based time series forecasting: A deep convolutional neural network approach.Neural Networks, 157:39–53, 2023.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Image- based time series forecasting: A deep convolutional neural network approach.Neural Networks, 157:39–53, 2023

Reference 86

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source=pdf_text observed=2026-08-06T21:29:03.220999Z digest=sha256:01b927942f72b467dd87ae844da4b1365ae3a20c3d58d92fed8db407f9f87741

Observation 37aee5d3-dbca-499e-a0be-57664450a828 · outbound

This paper cites Dust: Dual swin transformer for multi- modal video and time-series modeling.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Dust: Dual swin transformer for multi- modal video and time-series modeling

Reference 87

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source=pdf_text observed=2026-08-06T21:29:03.332985Z digest=sha256:43c30c4c314a85d2d12e1deb7156800cd7f69eeee5ae712a38a2f4dd5b96f240

Observation 2e281257-1145-46ff-a2bd-75c1dd0387fb · outbound

This paper cites Learning Video Representations using Contrastive Bidirectional Transformer.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Learning Video Representations using Contrastive Bidirectional Transformer

Reference 88

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source=pdf_text observed=2026-08-06T21:29:03.461180Z digest=sha256:3a52bf654f3b5b507b4a0b7e510e4278784eb885bc6366b52bcda8de03cfedb2

Observation 2dc11e7f-c895-4848-b551-e8f1cec888c3 · outbound

This paper cites Videobert: A joint model for video and language representation learning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Videobert: A joint model for video and language representation learning

Reference 89

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T21:29:03.627353Z digest=sha256:32bf0b04d232723fea4c5a776113366adf14d2fa2d462f2a41247f2935628ad7

Observation abda9d6e-5505-4bd9-8d7f-2c4a62896087 · outbound

This paper cites TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

Reference 90

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source=pdf_text observed=2026-08-06T21:29:03.796086Z digest=sha256:6d597b28643abdbc1f48dfd8c7308587e590d6baccc4d9db028e59bee725516b

Observation 5c8101c3-3abd-4b70-8500-7ed013e5dc5e · outbound

This paper cites Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning

Reference 91

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Observation 7b2ee3d7-90fb-4978-ac46-bbd58fe0f971 · outbound

This paper cites Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

Reference 92

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T21:29:04.042255Z digest=sha256:f339545a2297614215eb140be15c1c012eb9b9561f37c6f8841b8ed74ae349da

Observation a3147d8f-832b-427e-b3f3-8afbb9bde3d3 · outbound

This paper cites Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

Reference 93

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T21:29:04.148304Z digest=sha256:26a53df68c521c60d8cffee80fde21396df6a571feb8578cabc8da289e7e491f

Observation e831c829-605f-4219-991a-3d502d35445b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives LLaMA: Open and Efficient Foundation Language Models

Reference 94

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Observation 167d606e-03eb-457b-952c-9774ae0356d7 · outbound

This paper cites Attention is all you need.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Attention is all you need

Reference 95

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Observation a2726999-8d48-4c29-a8c9-2386cdd8ecf8 · outbound

This paper cites Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Reference 96

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source=pdf_text observed=2026-08-06T21:29:04.583770Z digest=sha256:41d3553099c91817ce5ffd5cbef18aa2bdf64e312343a0c000c2c7b4d43c687a

Observation 66a86caf-e597-4e13-b649-582afd9a3f95 · outbound

This paper cites Micn: Multi-scale local and global context modeling for long-term series forecasting.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Micn: Multi-scale local and global context modeling for long-term series forecasting

Reference 97

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source=pdf_text observed=2026-08-06T21:29:04.698553Z digest=sha256:acdcb5ae345116775166bb83fd6bcdeda52d63b5c458ca7110356a3f054b3888

Observation 8e26c3b6-34a2-465d-8a82-efc4eac27530 · outbound

This paper cites Long-short temporal contrastive learning of video transformers.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Long-short temporal contrastive learning of video transformers

Reference 98

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verified fuzzy
raw_fallback, observed 2026-08-06T21:29:18.967420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 5e0caaa7-7186-43ea-a47c-e988f75f6f33 · outbound

This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives ActionCLIP: A New Paradigm for Video Action Recognition

Reference 99

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source=pdf_text observed=2026-08-06T21:29:05.004317Z digest=sha256:02e0fe5c7328e570299586e74a598501041b0b0cefe9622f3a577a98f1fe4480

Observation 2d9357f1-380d-45bb-b419-1bd210e8b1f3 · outbound

This paper cites A hierarchal bert structure for native speaker writing detection.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives A hierarchal bert structure for native speaker writing detection

Reference 100

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raw_fallback, observed 2026-08-06T21:29:18.704941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T21:29:05.163304Z digest=sha256:94047908ec715fa7141ccf6d963c1240147ebf7eaf52823ccab595e8e478c615

Observation 2ec5269b-6731-4f8a-b4c0-58c71406f8cb · outbound

This paper cites Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models.

Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models

Reference 101

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local_arxiv, observed 2026-08-06T21:29:12.093386Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T21:29:05.314870Z digest=sha256:5e8f30e5192e8d1e035b8a8fdd0f6c6b6e001d3d497a28e3e8842329f69bb302

Pith citing papers

Observation afd912c9-50f1-41cc-b294-7df8c4ad6a5e · inbound

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy cites this paper.

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Reference 9

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arxiv_id, observed 2026-06-29T22:24:00.656941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 5c954d86-2670-4dce-bdaf-02a390bd3724 · inbound

Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering cites this paper.

Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Reference 24

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arxiv_id, observed 2026-07-04T01:09:18.666026Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation 7be0cf6d-ed19-4a25-aa4a-7bcbce33dec8 · inbound

Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA cites this paper.

Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

Reference 46

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verified exact
arxiv_id, observed 2026-06-30T01:34:09.349309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-30T01:29:58.474810Z digest=sha256:9aafe3ce748f5ecad4318b8cd4ebc2d764231f391cd1f21aac4b7a27dc0569f8