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Paper Citation Record · LEDGER

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

As of 10 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2606.29347.

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

pith.paper-citation-record.v1
2606.29347 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T08:16:47.629826Z

measured 33 of 33 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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  • verified fuzzy28
  • unresolved1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3f3bb20-ed04-4014-8130-19bfe1eff985 · outbound

This paper cites Efficient capital markets: A review of theory and empirical work.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Efficient capital markets: A review of theory and empirical work

Reference 1

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Observation 9beea0ad-191d-451a-acc4-958bbf9fbf9e · outbound

This paper cites Attention is all you need,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Attention is all you need,

Reference 2

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

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Observation cf1c7636-9141-438e-8498-0829dd304b56 · outbound

This paper cites Temporal fusion transformers for interpretable multi- horizon time series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Temporal fusion transformers for interpretable multi- horizon time series forecasting,

Reference 3

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Observation fc2375c6-3675-4825-a459-c5b23868fe21 · outbound

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

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Informer: Beyond efficient transformer for long se- quence time-series forecasting,

Reference 4

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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.

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Observation 67c4c27b-e0a4-4c61-94fd-22c73ea1c1c1 · outbound

This paper cites Autoformer: Decomposition transformers with auto- correlation for long-term series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Autoformer: Decomposition transformers with auto- correlation for long-term series forecasting,

Reference 5

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Source-reported events for the cited work

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Observation eb639db8-bea2-44f7-a777-e28f95c60fbc · outbound

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Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Unresolved cited work

Reference 6

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Observation d76af6aa-22bf-47b6-b0d8-62a7e1ea67c1 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Optuna: A next-generation hyperparameter optimization framework,

Reference 7

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Observation e116ac87-8bc7-4b82-95f4-4c34be1f82fb · outbound

This paper cites Long short-term memory.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Long short-term memory

Reference 8

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Source-reported events for the cited work

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Observation 6dee2e69-e77b-463e-a2db-d06cbad34d3e · outbound

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

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Learning phrase representations using RNN encoder- decoder for statistical machine translation,

Reference 9

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Source-reported events for the cited work

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Observation 7ee33f3c-2bce-4b66-900b-680e765daf01 · outbound

This paper cites XGBoost: A scalable tree boosting system,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction XGBoost: A scalable tree boosting system,

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.

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Observation 977dc8f4-0bdb-4bc7-bd9b-3ae4d3417f73 · outbound

This paper cites LightGBM: A highly efficient gradient boosting decision tree,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction LightGBM: A highly efficient gradient boosting decision tree,

Reference 11

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Observation da95fd53-ab75-4595-b683-83e49277aa41 · outbound

This paper cites CatBoost: unbiased boosting with categorical features,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction CatBoost: unbiased boosting with categorical features,

Reference 12

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Source-reported events for the cited work

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Observation 0bce3db0-aa1b-4f1f-a00e-3bffb2a10386 · outbound

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

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction A time series is worth 64 words: Long-term forecasting with Transformers,

Reference 13

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Observation db8d0661-92ba-4db8-8d13-7ec22cea9eb8 · outbound

This paper cites FEDformer: Frequency enhanced decomposed trans- former for long-term series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction FEDformer: Frequency enhanced decomposed trans- former for long-term series forecasting,

Reference 14

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Observation 7c9f4b58-95b3-44b4-96b6-7d99657d892a · outbound

This paper cites TimesNet: Temporal 2D-variation modeling for general time series analysis,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction TimesNet: Temporal 2D-variation modeling for general time series analysis,

Reference 15

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Source-reported events for the cited work

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Observation 36370966-d77a-44d8-8fb7-93ff91128e29 · outbound

This paper cites Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,

Reference 16

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Observation b9e7b050-21a5-49e0-9b3e-6dcc22201a7e · outbound

This paper cites iTransformer: Inverted Transformers are effective for time series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction iTransformer: Inverted Transformers are effective for time series forecasting,

Reference 17

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Source-reported events for the cited work

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Observation aa663d9a-ebd3-4bdd-a216-b55a88895a8f · outbound

This paper cites ETSformer: Exponential Smoothing Transformers for Time-series Forecasting.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Reference 18

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Observation 636b16bb-82d0-4c7e-9911-40dbf819250e · outbound

This paper cites Are Transformers effective for time series forecasting?.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Are Transformers effective for time series forecasting?

Reference 19

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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.

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Observation 33c00194-233a-4bdd-8923-f39d50a8bbee · outbound

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

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction N-BEATS: Neural basis expansion analysis for interpretable time series forecasting,

Reference 20

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

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Observation c378d1a4-54cf-4854-8b2c-b57437c25edf · outbound

This paper cites N-HiTS: Neural hierarchical interpolation for multi- horizon time series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction N-HiTS: Neural hierarchical interpolation for multi- horizon time series forecasting,

Reference 21

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

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Observation 96b391b1-0ed0-464a-9717-b914d2f70ae7 · outbound

This paper cites TiDE: Time-series dense encoder,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction TiDE: Time-series dense encoder,

Reference 22

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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.

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Observation 7bbc4dfb-fd6b-4d83-84f3-9099f42077e8 · outbound

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

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction SCINet: Time series modeling and forecasting with sample convolution and interaction,

Reference 23

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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.

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Observation 8a992f47-2422-446e-a70e-32d8e2dd3939 · outbound

This paper cites MICN: Multi-scale local and global context network for long-term series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction MICN: Multi-scale local and global context network for long-term series forecasting,

Reference 24

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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.

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Observation d08e566e-9338-464a-b318-b311d68ff1d7 · outbound

This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 25

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local_arxiv, observed 2026-06-30T08:24:26.586989Z

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.

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Observation 70fb7e49-e1e9-4b66-a1a3-da171fc542e4 · outbound

This paper cites AlphaStock: A deep reinforcement learning framework for stock portfolio optimization,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction AlphaStock: A deep reinforcement learning framework for stock portfolio optimization,

Reference 26

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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.

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Observation 3a131448-1908-4cb6-8680-1a53e667ca38 · outbound

This paper cites DeepLOB: Deep convolutional neural networks for limit order books,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction DeepLOB: Deep convolutional neural networks for limit order books,

Reference 27

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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.

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Observation baed3943-29b3-435e-b545-b0dc14cfa402 · outbound

This paper cites Axiomatic attribution for deep networks,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Axiomatic attribution for deep networks,

Reference 28

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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.

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Observation eeefbace-b812-46dd-a50f-edaf71aed955 · outbound

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

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction PyTorch: An imperative style, high-performance deep learning library,

Reference 29

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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.

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Observation 6cc78323-1fc8-42b3-962b-0fd286345bdc · outbound

This paper cites Scikit-learn: Machine learning in Python.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Scikit-learn: Machine learning in Python

Reference 30

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raw_fallback, observed 2026-07-10T22:27:41.659321Z

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.

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Observation 94b9e4c3-afda-482c-91cc-51084373c132 · outbound

This paper cites Transformers in Time Series: A Survey.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction Transformers in Time Series: A Survey

Reference 31

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arxiv_id, observed 2026-06-30T08:24:26.584463Z

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.

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Observation eadd7e58-9147-415c-9482-2195db701755 · outbound

This paper cites PatchMixer: A lightweight patch-mixing architecture for time series forecasting,.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction PatchMixer: A lightweight patch-mixing architecture for time series forecasting,

Reference 32

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raw_fallback, observed 2026-07-10T22:27:41.989895Z

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.

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Observation f63ace8c-269c-4f59-84aa-51671cc33639 · outbound

This paper cites A stochastic model of discussion.

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction A stochastic model of discussion

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:24:26.592984Z

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.

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Pith citing papers

No inbound Pith citation observations are available.