Paper Citation Record · LEDGER

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

As of 14 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 3 inbound Pith citation observations for arXiv:2507.02939.

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

pith.paper-citation-record.v1
2507.02939 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:12:15.188243Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:03.958199Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:53:58.799012Z

Reference resolution

54 of 54 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a93a09e9-c696-475b-bc5f-6532495e7891 · outbound

This paper cites Dissecting the high-frequency bias in convolutional neural networks.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Dissecting the high-frequency bias in convolutional neural networks

Reference 1

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Observation 30af083b-77e7-455f-9314-54c877ceeacf · outbound

This paper cites Accurate medium-range global weather forecasting with 3d neural networks.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Accurate medium-range global weather forecasting with 3d neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:24.569687Z

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Observation 00665334-ab05-4c4e-8bd4-e597f7165c59 · outbound

This paper cites Choose a transformer: Fourier or galerkin.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Choose a transformer: Fourier or galerkin

Reference 3

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Observation c3fd8285-fc12-48fe-bca5-aad85edc4fb0 · outbound

This paper cites Towards Understanding the Spectral Bias of Deep Learning.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Towards Understanding the Spectral Bias of Deep Learning

Reference 4

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Observation 3e4f781e-2df8-4359-9e7f-3862ae220937 · outbound

This paper cites Selective frequency network for image restoration.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Selective frequency network for image restoration

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:23.980323Z

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Observation 7145666e-da41-4eaf-aff2-30a9d29b38fa · outbound

This paper cites Deep learning for spatio-temporal modeling: dynamic traffic flows and high frequency trading.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Deep learning for spatio-temporal modeling: dynamic traffic flows and high frequency trading

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:23.654926Z

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Observation 30280df8-558c-4180-b5d4-d42b44aa0193 · outbound

This paper cites Agree to disagree: Adap- tive ensemble knowledge distillation in gradient space.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Agree to disagree: Adap- tive ensemble knowledge distillation in gradient space

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:23.363758Z

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Observation 7900bc27-ecbb-4f89-a334-eb229f0612de · outbound

This paper cites Neuralom: Neural ocean model for subseasonal-to-seasonal simulation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Neuralom: Neural ocean model for subseasonal-to-seasonal simulation

Reference 8

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Observation 7e6b8763-d86d-4e6f-88c7-080d8f1834fa · outbound

This paper cites Simvp: Simpler yet better video prediction.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Simvp: Simpler yet better video prediction

Reference 9

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Observation ffaa8a49-a162-44da-a2ac-73fe7576efee · outbound

This paper cites Deep residual learning for image recognition.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Deep residual learning for image recognition

Reference 10

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Observation 30b3f107-22ca-411b-969b-4cb3ec5b3695 · outbound

This paper cites Knowledge transfer via distillation of activation bound- aries formed by hidden neurons.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Knowledge transfer via distillation of activation bound- aries formed by hidden neurons

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:22.906781Z

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Observation cf6ee84c-f5a7-4b1b-b33c-761ed5a9ad49 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Distilling the Knowledge in a Neural Network

Reference 12

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Observation e299eb70-5630-47b0-9448-6813d587d082 · outbound

This paper cites Knowledge distillation on spatial-temporal graph convolu- tional network for traffic prediction.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Knowledge distillation on spatial-temporal graph convolu- tional network for traffic prediction

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:22.531421Z

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Observation 7d9260ea-a33a-43dc-ac34-0f1d824a93b3 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Neural tangent kernel: Convergence and generalization in neural networks

Reference 14

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Observation daa6915d-7a2d-4d4c-847b-77345da798da · outbound

This paper cites Show, attend and distill: Knowledge distillation via attention-based fea- ture matching.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Show, attend and distill: Knowledge distillation via attention-based fea- ture matching

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:21.720467Z

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Observation a54b0da8-91a8-4f10-a160-15fa1e1c57c0 · outbound

This paper cites Graph neural network for traf- fic forecasting: A survey.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Graph neural network for traf- fic forecasting: A survey

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:21.376641Z

Source-reported events for the cited work

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Observation 52e380b9-bf4d-4e22-80ad-04e2fc11c014 · outbound

This paper cites Frequency-guided masking for enhanced vision self- supervised learning.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Frequency-guided masking for enhanced vision self- supervised learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:21.051688Z

Source-reported events for the cited work

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Observation e28fc793-a8d2-41fe-a3e2-c3a2955ee03f · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Fourier Neural Operator for Parametric Partial Differential Equations

Reference 18

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Observation 762132a2-c651-4ece-a2f3-46261705a1d3 · outbound

This paper cites Curriculum temperature for knowledge distillation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Curriculum temperature for knowledge distillation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:20.778281Z

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Observation f1e1c8e7-a322-4cc0-844a-0007403c296a · outbound

This paper cites Promptkd: Unsupervised prompt distillation for vision-language models.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Promptkd: Unsupervised prompt distillation for vision-language models

Reference 20

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Observation f1cc23de-9f5f-4d36-8228-46199eb38e5c · outbound

This paper cites Conditional local convolution for spatio- temporal meteorological forecasting.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Conditional local convolution for spatio- temporal meteorological forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:20.553979Z

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Observation 1b00c711-35ba-42da-bc25-cf97ba07e52b · outbound

This paper cites Investigating and Explaining the Frequency Bias in Image Classification.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Investigating and Explaining the Frequency Bias in Image Classification

Reference 22

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Observation 389d0cba-546b-4ea2-9163-baa9e687a28f · outbound

This paper cites Machine learn- ing for geographically differentiated climate change mitiga- tion in urban areas.Sustainable Cities and Society, 64:102526,.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Machine learn- ing for geographically differentiated climate change mitiga- tion in urban areas.Sustainable Cities and Society, 64:102526,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:20.354976Z

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Observation 9fcb4769-c391-46d2-b2bd-37d87a4149fc · outbound

This paper cites Urban traffic prediction from spatio- temporal data using deep meta learning.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Urban traffic prediction from spatio- temporal data using deep meta learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:20.145830Z

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Observation dd66d163-2f4f-417e-804b-e63a0360b37e · outbound

This paper cites How Do Vision Transformers Work?.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting How Do Vision Transformers Work?

Reference 25

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Observation 2c73d1f3-7428-4ec5-bbb8-c5eb0fc05211 · outbound

This paper cites Frequency attention for 9 knowledge distillation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Frequency attention for 9 knowledge distillation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:19.855381Z

Source-reported events for the cited work

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Observation f449d5c3-d419-4eff-a3f9-f10a4c2fc128 · outbound

This paper cites Do vision trans- formers see like convolutional neural networks? Advances in neural information processing systems, 34:12116–12128,.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Do vision trans- formers see like convolutional neural networks? Advances in neural information processing systems, 34:12116–12128,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:19.600976Z

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Observation f29571e3-76a3-4878-a0b9-0672eef8bcd0 · outbound

This paper cites On the spectral bias of neural networks.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting On the spectral bias of neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:19.439824Z

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Observation a2d45512-34cf-46ef-b4a3-af67d63e3012 · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting FitNets: Hints for Thin Deep Nets

Reference 29

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Observation 8b3f8868-3138-4e92-89ca-3b4a5522fde1 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmenta- tion.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting U- net: Convolutional networks for biomedical image segmenta- tion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:19.283023Z

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Observation 64a563c8-b4ce-4770-b5b2-c067472b9ea0 · outbound

This paper cites Understanding depthwise separable convolu- tions and the efficiency of mobilenets.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Understanding depthwise separable convolu- tions and the efficiency of mobilenets

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:19.154292Z

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Observation 05c40e01-8f5d-4ebe-9762-5b5e9a7039aa · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation now- casting, 2015.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Convolutional lstm network: A machine learning approach for precipitation now- casting, 2015

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:19.020432Z

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Observation 7742bf13-8b83-4cd9-b9c0-49048979fe0e · outbound

This paper cites Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation

Reference 33

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Observation 8cf012ae-bbc4-49f2-abc4-5b6398ea3657 · outbound

This paper cites Estimating low-frequency variabil- ity and trends in atmospheric temperature using era-interim.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Estimating low-frequency variabil- ity and trends in atmospheric temperature using era-interim

Reference 34

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Observation 994bfa9f-a9b4-4746-b65a-382454678575 · outbound

This paper cites Mlp- mixer: An all-mlp architecture for vision.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Mlp- mixer: An all-mlp architecture for vision

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:18.785760Z

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Observation 144fedae-89d2-4de7-a4bd-623dce007d37 · outbound

This paper cites High- frequency component helps explain the generalization of con- volutional neural networks.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting High- frequency component helps explain the generalization of con- volutional neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:18.644479Z

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Observation c0cd3089-6380-4b9d-82d9-5f283ca5ffd5 · outbound

This paper cites Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:18.514611Z

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Observation 26a5643a-1496-4894-b88a-d9bfb35cbf3c · outbound

This paper cites Earthfarsser: Versatile spatio-temporal dynamical systems modeling in one model.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Earthfarsser: Versatile spatio-temporal dynamical systems modeling in one model

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:18.354466Z

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Observation c1b7a255-e2d6-4181-b409-3570c510c466 · outbound

This paper cites Pure: Prompt evolution with graph ode for out-of-distribution fluid dynamics model- ing.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Pure: Prompt evolution with graph ode for out-of-distribution fluid dynamics model- ing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:18.220633Z

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Observation fc5b7de9-22fa-4196-802c-5ab5924dd9cc · outbound

This paper cites Neural manifold operators for learning the evolu- tion of physical dynamics.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Neural manifold operators for learning the evolu- tion of physical dynamics

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:18.096262Z

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Observation 280a1ab4-f2f4-469c-b056-48332ffad0c5 · outbound

This paper cites Pastnet: Introducing physical inductive biases for spatio-temporal video prediction.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Pastnet: Introducing physical inductive biases for spatio-temporal video prediction

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:17.949536Z

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Observation 01403d84-d34b-4d98-88eb-c7fb405a9496 · outbound

This paper cites Turb-l1: Achieving long-term turbulence tracing by tackling spectral bias.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Turb-l1: Achieving long-term turbulence tracing by tackling spectral bias

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T22:12:13.923323Z

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Observation 534761a4-919b-424d-b6bf-7ab1f873c69e · outbound

This paper cites Advanced long-term earth system fore- casting by learning the small-scale nature.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Advanced long-term earth system fore- casting by learning the small-scale nature

Reference 43

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Observation 4e57772b-e360-4ee8-afb9-9374a33daf9b · outbound

This paper cites Hierarchical self-supervised augmented knowledge dis- tillation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Hierarchical self-supervised augmented knowledge dis- tillation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:17.750415Z

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Observation b64958a4-865b-4e38-b9a0-52d775b86bbf · outbound

This paper cites Mutual contrastive learning for visual representation learning.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Mutual contrastive learning for visual representation learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:17.580639Z

Source-reported events for the cited work

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Observation 2fe13420-777e-429f-87fd-a426e5af6d1c · outbound

This paper cites Cross-image relational knowl- edge distillation for semantic segmentation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Cross-image relational knowl- edge distillation for semantic segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:17.384678Z

Source-reported events for the cited work

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Observation c79bf352-2eb1-46eb-85e2-eea258fbee18 · outbound

This paper cites Online knowledge distillation via mutual contrastive learning for visual recognition.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Online knowledge distillation via mutual contrastive learning for visual recognition

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:17.220878Z

Source-reported events for the cited work

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Observation 7a092a16-95bc-46ad-977d-c97b583de112 · outbound

This paper cites Clip-kd: An empirical study of clip model distillation.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Clip-kd: An empirical study of clip model distillation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:17.036657Z

Source-reported events for the cited work

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Observation 19a6adaf-96f0-48e6-9569-ff9894f5fe8d · outbound

This paper cites Multi-teacher knowledge distillation with reinforcement learning for visual recognition.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Multi-teacher knowledge distillation with reinforcement learning for visual recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:16.850643Z

Source-reported events for the cited work

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Observation a558acd1-ba80-4ac6-b033-40e956f379ca · outbound

This paper cites Decoupling dark 10 knowledge via block-wise logit distillation for feature-level alignment.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Decoupling dark 10 knowledge via block-wise logit distillation for feature-level alignment

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:16.716227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e41b6550-a9b0-41e5-94c8-2c3e92cd1ce2 · outbound

This paper cites Ginar+: A robust end-to-end framework for multivariate time series forecasting with missing values.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Ginar+: A robust end-to-end framework for multivariate time series forecasting with missing values

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:16.581857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1a2ffb7f-04ce-4ed2-9bb6-7ec663236b63 · outbound

This paper cites Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:12:15.422066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2405ed1-6ce5-4b49-8495-fd174b766048 · outbound

This paper cites Skilful nowcasting of extreme precipitation with nowcastnet.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Skilful nowcasting of extreme precipitation with nowcastnet

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:16.424593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 50000c44-ccfe-4d15-8a9b-79320086a3d4 · outbound

This paper cites Freekd: Knowledge distillation via semantic frequency prompt.

Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting Freekd: Knowledge distillation via semantic frequency prompt

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:12:16.238911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation c43f5b67-54d2-4462-9466-983feaec7834 · inbound

SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving cites this paper.

SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:03.958199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d78428e-731e-4fd5-889d-69dbbbd4231f · inbound

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting cites this paper.

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:53:58.866339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 12f132c2-74d2-408b-987b-bd6ad727fc01 · inbound

When Should Active RAG Retrieve? A Budget-Aware Evaluation of Utility, Calibration, and Cost cites this paper.

When Should Active RAG Retrieve? A Budget-Aware Evaluation of Utility, Calibration, and Cost Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T23:18:24.458968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.