Paper Citation Record · LEDGER

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

As of 10 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 3 inbound Pith citation observations for arXiv:2604.13453.

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

pith.paper-citation-record.v1
2604.13453 v1

Coverage vector

measured 82 of 82 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T14:20:21.472989Z

measured 85 of 85 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T19:10:42.461337Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T04:37:37.134195Z

Reference resolution

82 of 82 outbound references displayed

verified exact13
verified fuzzy66
unresolved0
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88067a12-89c1-4e75-912c-37b0bf634ba5 · outbound

This paper cites Bayesian critique-tune-based reinforcement learning with adaptive pressure for multi-intersection traffic signal control.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Bayesian critique-tune-based reinforcement learning with adaptive pressure for multi-intersection traffic signal control

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.135564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b8b13eb-a3b4-41c3-86b4-1f8f95ed0585 · outbound

This paper cites Spatiotemporal multi-view continual dictionary learning with graph diffusion.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Spatiotemporal multi-view continual dictionary learning with graph diffusion

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.133473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c881dfa6-171d-4cb7-805e-254998034082 · outbound

This paper cites Multi-resolution context augmentation and dual channel attention for 3d lane detection.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Multi-resolution context augmentation and dual channel attention for 3d lane detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.126435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03cc77ff-2117-488b-a7d4-63e7c4701a5d · outbound

This paper cites Tur- boreg: Turboclique for robust and efficient point cloud registration.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Tur- boreg: Turboclique for robust and efficient point cloud registration

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.137486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d8c318bd-2261-4230-8725-adb2b40e0c63 · outbound

This paper cites Spatiotemporal align- ment for remote sensing image recovery via terrain-aware diffusion.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Spatiotemporal align- ment for remote sensing image recovery via terrain-aware diffusion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.147383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cf54650-8be4-4007-9031-c48ac86eaf41 · outbound

This paper cites Difflow3d: Toward robust uncertainty-aware scene flow estimation with iterative diffusion-based refinement.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Difflow3d: Toward robust uncertainty-aware scene flow estimation with iterative diffusion-based refinement

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.117424Z

Source-reported events for the cited work

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Observation 660311d9-8b62-44ae-a71c-d574ebddb4dc · outbound

This paper cites Dvlo: Deep visual-lidar odometry with local-to-global feature fusion and bi- directional structure alignment.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Dvlo: Deep visual-lidar odometry with local-to-global feature fusion and bi- directional structure alignment

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.123595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2dfbdbec-c5bd-4c2b-81e5-bb088c53919d · outbound

This paper cites Stg- avatar: Animatable human avatars via spacetime gaussian.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Stg- avatar: Animatable human avatars via spacetime gaussian

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.114969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bae4a027-b9e9-43e3-9d80-c21f3ede6ae1 · outbound

This paper cites Adagar: Adaptive gabor representation for dynamic scene reconstruction.arXiv preprint arXiv:2601.00796.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Adagar: Adaptive gabor representation for dynamic scene reconstruction.arXiv preprint arXiv:2601.00796

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:20:29.485652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe8be797-8c22-4bf6-9ddd-d0f786fa48c0 · outbound

This paper cites Learn from global correlations: Enhancing evolutionary algorithm via spectral gnn.arXiv preprint arXiv:2412.17629.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Learn from global correlations: Enhancing evolutionary algorithm via spectral gnn.arXiv preprint arXiv:2412.17629

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e3dd087-ae28-4c58-adc3-557cc9d35ce1 · outbound

This paper cites A stable technical feature with gru-cnn-ga fusion.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction A stable technical feature with gru-cnn-ga fusion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.121002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88f9592e-70ac-4129-bd01-d79a307e8331 · outbound

This paper cites Research and practice of advertisement recommendation algorithm based on graph neural network.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Research and practice of advertisement recommendation algorithm based on graph neural network

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.144735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39e0024a-8213-4868-84bb-e2f9ac323075 · outbound

This paper cites Efficient cold-start recommendation via bpe token-level embedding initialization with llm.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Efficient cold-start recommendation via bpe token-level embedding initialization with llm

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.164748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bc157984-8681-499f-be43-eb103e5f73f1 · outbound

This paper cites Priordrive: Enhancing online hd mapping with unified vector priors.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Priordrive: Enhancing online hd mapping with unified vector priors

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.084894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b96b758b-4b95-45c5-accd-62a8083728cf · outbound

This paper cites MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-10T14:20:29.474708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b3bf689-4a4f-435c-be2f-5ce800df66e6 · outbound

This paper cites Decentralized graph-based multi-agent reinforcement learning using reward machines.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Decentralized graph-based multi-agent reinforcement learning using reward machines

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.071543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14ed10a4-c256-4815-9e65-1271d8fa921b · outbound

This paper cites FineState-Bench: A Comprehensive Benchmark for Fine-Grained State Control in GUI Agents.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction FineState-Bench: A Comprehensive Benchmark for Fine-Grained State Control in GUI Agents

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T14:20:29.497042Z

Source-reported events for the cited work

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Observation ad6099d9-a095-417c-a3c6-16ede5f127e5 · outbound

This paper cites Intent: Invariance and discrimination-aware noise mitigation for robust composed image retrieval.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Intent: Invariance and discrimination-aware noise mitigation for robust composed image retrieval

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.095150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 91960a04-bbb1-49d4-9ca3-b458c020e66a · outbound

This paper cites Hud: Hierar- chical uncertainty-aware disambiguation network for composed video retrieval.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Hud: Hierar- chical uncertainty-aware disambiguation network for composed video retrieval

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.104455Z

Source-reported events for the cited work

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Observation 14764666-a23e-4c33-aadd-da69f69f9eff · outbound

This paper cites Refine: Composed video retrieval via shared and differential semantics enhance- ment.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Refine: Composed video retrieval via shared and differential semantics enhance- ment

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.106519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa53be21-4c3c-4769-8882-67ec8652fe6e · outbound

This paper cites Tri- subspaces disentanglement for multimodal sentiment analysis.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Tri- subspaces disentanglement for multimodal sentiment analysis

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.160293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cfc44d55-1c65-4aa6-85bf-415f2c5975c3 · outbound

This paper cites Cotextor: Training- free modular multilingual text editing via layered disentanglement and depth-aware fusion.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Cotextor: Training- free modular multilingual text editing via layered disentanglement and depth-aware fusion

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.149945Z

Source-reported events for the cited work

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Observation 61c9e3cf-d717-4f03-be74-191f18df0cd2 · outbound

This paper cites DynamicNER: A dynamic, multilingual, and fine-grained dataset for LLM-based named entity recognition.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction DynamicNER: A dynamic, multilingual, and fine-grained dataset for LLM-based named entity recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.128550Z

Source-reported events for the cited work

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Observation 1fb18fd6-ca75-48b0-abbb-7b72eaf80286 · outbound

This paper cites Codes: A context-efficient framework for enhancing small language models via domain-specific adaptation and model ensembling.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Codes: A context-efficient framework for enhancing small language models via domain-specific adaptation and model ensembling

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:20:28.642573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1c3ec4ab-64c8-42c0-a1e4-22d2d2ff1218 · outbound

This paper cites Parameter-efficient and student-friendly knowledge distillation.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Parameter-efficient and student-friendly knowledge distillation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.130511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation caac9975-1e7b-4180-a596-b0ceb5f93188 · outbound

This paper cites The Accessibility and Inaccessibility of Urban Public Charging Stations.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction The Accessibility and Inaccessibility of Urban Public Charging Stations

Reference 26

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Source-reported events for the cited work

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Observation bd50ec8f-794e-425d-af0d-7bd70293dea7 · outbound

This paper cites Reg- former: an efficient projection-aware transformer network for large-scale point cloud registration.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Reg- former: an efficient projection-aware transformer network for large-scale point cloud registration

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.139659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 842d82f3-b450-4671-aea3-64053c5a86f1 · outbound

This paper cites AutoNeural: Co-Designing Vision-Language Models for NPU Inference.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction AutoNeural: Co-Designing Vision-Language Models for NPU Inference

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:32.885266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64a68c60-cefe-4914-bca4-99c61a38a28d · outbound

This paper cites REA-RL: Reflection-aware online reinforcement learning for efficient reasoning.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction REA-RL: Reflection-aware online reinforcement learning for efficient reasoning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.152663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b86aeade-4266-4805-a3fb-50f9402c8bcc · outbound

This paper cites Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-21T02:04:11.502809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b45e23e3-5123-4458-8d83-a764179b0d49 · outbound

This paper cites Efficient partitioning vision transformer on edge devices for distributed inference.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Efficient partitioning vision transformer on edge devices for distributed inference

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.142066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad1d0e61-bf37-44f8-b6dd-2a8f13ebe526 · outbound

This paper cites Fedlpa: One- shot federated learning with layer-wise posterior aggregation.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Fedlpa: One- shot federated learning with layer-wise posterior aggregation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.169523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86343c57-7e41-418c-9ceb-f5538e11fea9 · outbound

This paper cites One-shot Federated Learning Methods: A Practical Guide.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction One-shot Federated Learning Methods: A Practical Guide

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:20:29.465694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1d4a0c21-acbb-4e6b-ab37-616da83e2110 · outbound

This paper cites Fastpillars: A deployment-friendly pillar-based 3d detector.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Fastpillars: A deployment-friendly pillar-based 3d detector

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.082214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa611084-d7b2-4555-bd79-41c11e5aacdd · outbound

This paper cites GSQ- tuning: Group-shared exponents integer in fully quantized training for LLMs on-device fine-tuning.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction GSQ- tuning: Group-shared exponents integer in fully quantized training for LLMs on-device fine-tuning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.108472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b2ea701-104d-47e4-8dab-674a58a51ce0 · outbound

This paper cites Yolov8- dds: A lightweight model based on pruning and distillation for early detection of root mold in barley seedling.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Yolov8- dds: A lightweight model based on pruning and distillation for early detection of root mold in barley seedling

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.075081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36e9fe23-f0d4-40c3-ae88-eee028ff8f5d · outbound

This paper cites Filter-and-refine: A MLLM based cascade system for industrial-scale video content moderation.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Filter-and-refine: A MLLM based cascade system for industrial-scale video content moderation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.065717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f336f62a-0335-4939-a84d-3dd93dec191b · outbound

This paper cites ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-27T02:04:33.896984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 366ec035-2d42-48bf-b370-35f3e6ff350c · outbound

This paper cites An efficient solution method for solving convex separable quadratic optimization problems.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction An efficient solution method for solving convex separable quadratic optimization problems

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:20:29.507919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a1f71477-c989-44dd-9836-3fca8c566e47 · outbound

This paper cites A semidefinite relaxation based global algorithm for two-level graph partition problem.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction A semidefinite relaxation based global algorithm for two-level graph partition problem

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.062934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7a86b7bc-dcc2-4d86-9825-20b437821a2c · outbound

This paper cites Comptrack: Information bottleneck-guided low-rank dynamic token compression for point cloud tracking.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Comptrack: Information bottleneck-guided low-rank dynamic token compression for point cloud tracking

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.068511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d938b5a7-05d0-42dc-92e5-3b11c0c51186 · outbound

This paper cites Regime-dependent volatility dynamics: Evidence from time-series analysis.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Regime-dependent volatility dynamics: Evidence from time-series analysis

Reference 42

Resolution: verified exact; doi, observed 2026-05-10T14:20:28.627048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f38ff298-6024-4043-aa52-31bbdfa30613 · outbound

This paper cites & Wang, Z.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction & Wang, Z

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T14:20:29.502459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation db3a6bf6-75af-4cce-b297-cff15944b162 · outbound

This paper cites Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-10T14:20:29.513034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 78c2dfc2-62cf-4b97-aef2-801171f8000b · outbound

This paper cites NoiseBox: Towards More Efficient and Effective Learning with Noisy Labels.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction NoiseBox: Towards More Efficient and Effective Learning with Noisy Labels

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.079021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb48fd7b-4526-416e-8e8a-11be3c99097d · outbound

This paper cites PROSAC: Provably safe certification for machine learning models under adversarial attacks.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction PROSAC: Provably safe certification for machine learning models under adversarial attacks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.087356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1d7658d-df6f-4ba5-b1c2-411179be5bc3 · outbound

This paper cites Noisy but valid: Robust statistical evaluation of LLMs with imperfect judges.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Noisy but valid: Robust statistical evaluation of LLMs with imperfect judges

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.092761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d43a9a17-d442-4ed2-9af9-c2efcc22a326 · outbound

This paper cites Dynamic neural fortresses: An adaptive shield for model extraction defense.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Dynamic neural fortresses: An adaptive shield for model extraction defense

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.110632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 69f74a2f-324d-433a-b290-7d9d73686a47 · outbound

This paper cites Agentauditor: Human-level safety and security evaluation for llm agents.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Agentauditor: Human-level safety and security evaluation for llm agents

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.060458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14138235-9343-46bc-82e2-135de25075b1 · outbound

This paper cites From voice to safety: Language ai powered pilot-atc communication understanding for airport surface movement collision risk assessment.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction From voice to safety: Language ai powered pilot-atc communication understanding for airport surface movement collision risk assessment

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.051139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4dec599e-f1cb-46f4-86d9-1dbf059fbbb4 · outbound

This paper cites Reinforcement learning driven integrated detection and mitigation of uav gps spoofing attacks.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Reinforcement learning driven integrated detection and mitigation of uav gps spoofing attacks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.049060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11564863-9fca-410e-83a6-9248bce83895 · outbound

This paper cites A fully data-driven approach for realistic traffic signal control using offline reinforcement learning.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction A fully data-driven approach for realistic traffic signal control using offline reinforcement learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.055954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88b2927a-de10-4997-928d-01627a75ca60 · outbound

This paper cites Ccma: A framework for cascading cooperative multi-agent in autonomous driving merging using large language models.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Ccma: A framework for cascading cooperative multi-agent in autonomous driving merging using large language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.042468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d5a8678-099a-4c7b-9abb-51cad1e536b3 · outbound

This paper cites Towards cleaner heating production in rural areas: Identifying optimal regional renewable systems with a case in ningxia, china.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Towards cleaner heating production in rural areas: Identifying optimal regional renewable systems with a case in ningxia, china

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.044942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f18922e-ccc4-4666-a018-5f853b25136a · outbound

This paper cites Reasoning-enhanced domain-adaptive pretraining of mul- timodal large language models for short video content governance.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Reasoning-enhanced domain-adaptive pretraining of mul- timodal large language models for short video content governance

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.047076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d7ee01fc-c2ff-490d-ac11-852fc5131ad4 · outbound

This paper cites When rules fall short: Agent-driven discovery of emerging content issues in short video platforms.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction When rules fall short: Agent-driven discovery of emerging content issues in short video platforms

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.053683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f1b03ef-9fc7-4c66-a2d4-6f1fa30bd2a6 · outbound

This paper cites FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-15T19:19:43.280111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7a8524df-b5ca-4bc4-a850-7e138f2af6d4 · outbound

This paper cites Janusvln: Decoupling semantics and spatiality with dual implicit memory for vision-language navigation.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Janusvln: Decoupling semantics and spatiality with dual implicit memory for vision-language navigation

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:20:29.480221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a831e9dd-4446-4eec-9cbc-d339d7f9bd79 · outbound

This paper cites Forecasting freeway traffic flow for intelligent transporta- tion systems application.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Forecasting freeway traffic flow for intelligent transporta- tion systems application

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.097657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41a51b3e-714e-44da-a4f3-41d14be2881f · outbound

This paper cites Short-term traffic flow prediction using seasonal ARIMA model with limited input data.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Short-term traffic flow prediction using seasonal ARIMA model with limited input data

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.100028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9370203b-998b-4bf8-8c9a-97f27a81f4f6 · outbound

This paper cites Predicting short-term traffic flow in urban based on multivariate linear regression model.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Predicting short-term traffic flow in urban based on multivariate linear regression model

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.028974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8477d2a5-47e5-4696-b678-936fd6090031 · outbound

This paper cites Travel-time prediction with support vector regression.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Travel-time prediction with support vector regression

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.031551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 985a9bfe-3a9d-442f-bd76-549d2be5c372 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.026642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 957d31da-f5b2-4992-8b50-3ec3a3b36215 · outbound

This paper cites Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.024398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e8a28e2-add1-47cf-8473-66c4c5713a06 · outbound

This paper cites Graph wavenet for deep spatial-temporal graph modeling.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Graph wavenet for deep spatial-temporal graph modeling

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.038468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30794a34-60f3-4ada-afcb-a579ecbb620f · outbound

This paper cites Spatial-temporal fusion graph neural networks for traffic flow forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Spatial-temporal fusion graph neural networks for traffic flow forecasting

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.022325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 40f5f358-06a8-4578-90cc-c3d64dc4f3e1 · outbound

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

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Adaptive graph convolutional recurrent network for traffic forecasting

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.036113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32e3f686-5626-4599-baf2-9152312c3628 · outbound

This paper cites Attention is all you need.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Attention is all you need

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.040436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93ca8d50-d582-4e0a-89eb-614aec6bade2 · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Gman: A graph multi-attention network for traffic prediction

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.020105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0adb340e-6d63-46b4-96b8-5d404bcf43e8 · outbound

This paper cites Towards spatio- temporal aware traffic time series forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Towards spatio- temporal aware traffic time series forecasting

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.102114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6fff7743-145e-43d2-93dd-fd686a260834 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction A time series is worth 64 words: Long-term forecasting with transformers

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.058068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b75b0c8a-e929-4b64-bf46-0f47c7eaf967 · outbound

This paper cites itrans- former: Inverted transformers are effective for time series forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction itrans- former: Inverted transformers are effective for time series forecasting

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.112786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71851621-7cae-4662-898d-0f73a3a3773a · outbound

This paper cites Adaptive context length optimization with low-frequency truncation for multi-agent reinforcement learning.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Adaptive context length optimization with low-frequency truncation for multi-agent reinforcement learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.155253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25cafe3c-0b9a-4904-ba78-593e907b4092 · outbound

This paper cites Hippo: Recurrent memory with optimal polynomial projections.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Hippo: Recurrent memory with optimal polynomial projections

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.011017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b99366b-f5e3-4a40-8a61-2315671f5a6d · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Efficiently modeling long sequences with structured state spaces

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.013719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4f1997f9-cc01-49e9-8a6a-c00eac615a7e · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Mamba: Linear-time sequence modeling with selective state spaces

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.015880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cffaf44d-b40e-4bb8-b565-b90dcc442d42 · outbound

This paper cites U-mamba: Enhancing long-range depen- dency for biomedical image segmentation.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction U-mamba: Enhancing long-range depen- dency for biomedical image segmentation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.018130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 79ad143b-5532-44a6-b892-d693003093bd · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Vision mamba: Efficient visual representation learning with bidirectional state space model

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.033879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 48ab970d-2265-4283-a215-e159d085dbf5 · outbound

This paper cites Videomamba: State space model for efficient video understanding.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Videomamba: State space model for efficient video understanding

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.157944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76e4af41-de7f-45c7-a35b-ee8a74f5220d · outbound

This paper cites Graph neural controlled differential equations for traffic forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Graph neural controlled differential equations for traffic forecasting

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.162437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4f36aaf9-e03c-43ba-8a56-39e9badae0fe · outbound

This paper cites Mcst-mamba: Multivariate mamba-based model for traffic prediction.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction Mcst-mamba: Multivariate mamba-based model for traffic prediction

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.166821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c74ee859-7f76-4467-8e80-6538f2615179 · outbound

This paper cites A mamba foundation model for time series forecasting.

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction A mamba foundation model for time series forecasting

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T14:42:43.089630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation aac75274-646a-43a9-8289-e57eed2cee30 · inbound

LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support cites this paper.

LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:16:36.145050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 584eae95-4d1b-46d1-a83f-0a3397482ae4 · inbound

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction cites this paper.

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-06-28T19:12:34.546452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09bc3db0-8462-4410-993e-2c9f2e1e58a9 · inbound

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite cites this paper.

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:37:37.135826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.