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Paper Citation Record · LEDGER

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music

As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2607.14537.

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

pith.paper-citation-record.v1
2607.14537 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:52:43.212465Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:52:40.712471Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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  • unresolved34
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 3fa39840-3961-43e3-a373-51cd70eccff1 · outbound

This paper cites MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music

Reference 1

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Observation 75bc6ce2-f1f8-49f3-ba8d-677810f402a2 · outbound

This paper cites Multi-Level Dropout.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Multi-Level Dropout

Reference 2

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Observation b95b2490-57e8-4cc3-972a-9a333aebd4c5 · outbound

This paper cites an unresolved cited work.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Unresolved cited work

Reference 3

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Observation 7d65d4b3-1867-4667-8e84-28734260d52a · outbound

This paper cites an unresolved cited work.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Unresolved cited work

Reference 4

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Observation 14fbeed0-4187-43e4-87e6-c3aa566d0f00 · outbound

This paper cites Thanks to Vincent Lostanlen for the suggestion of the Haar wavelet baseline.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Thanks to Vincent Lostanlen for the suggestion of the Haar wavelet baseline

Reference 5

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Observation d4b316c0-7982-44b7-b16c-cbe021b2800a · outbound

This paper cites an unresolved cited work.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Unresolved cited work

Reference 6

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Observation 39df41d3-e7c8-4b18-8ead-651b99976abb · outbound

This paper cites Lerdahl and R.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Lerdahl and R

Reference 7

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Observation 50bcada1-0973-4f1f-9e93-cfc02066128c · outbound

This paper cites CMI-Bench: A comprehensive benchmark for evaluating music instruction following,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music CMI-Bench: A comprehensive benchmark for evaluating music instruction following,

Reference 8

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Observation 771f3617-0037-426c-a03b-c361d999f8d1 · outbound

This paper cites Polyffusion: A diffu- sion model for polyphonic score generation with internal and external controls,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Polyffusion: A diffu- sion model for polyphonic score generation with internal and external controls,

Reference 9

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Observation 96f6f601-084d-4f0b-9cb3-589d53204e97 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music DINOv2: Learning Robust Visual Features without Supervision

Reference 10

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Observation 1c78510a-d1ac-47e6-9aa1-329a27a957da · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Self-supervised learning from images with a joint-embedding predictive architecture,

Reference 11

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Observation 1e0a0565-98a8-429f-95d8-83ad34d4e5dd · outbound

This paper cites Diffusion Transformers with Representation Autoencoders.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Diffusion Transformers with Representation Autoencoders

Reference 12

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Observation 5ef4ed1d-cde9-44d7-bfe5-66c36f288bd7 · outbound

This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 13

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Observation 29e3e19f-401d-47cf-8f6b-aebb6f1026bd · outbound

This paper cites Emerging properties in self- supervised vision transformers,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Emerging properties in self- supervised vision transformers,

Reference 14

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Observation 91e175a9-21f8-4ab3-9311-7dde7ae5763d · outbound

This paper cites Masked modeling duo: Learning representations by encouraging both networks to model the input,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Masked modeling duo: Learning representations by encouraging both networks to model the input,

Reference 15

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Observation a3842049-b348-4b85-a135-114e99534aa8 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 16

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Observation fc9d5c98-b04e-4f52-baf1-ee3c9b653927 · outbound

This paper cites PESTO: Pitch estimation with self-supervised transposition-equivariant objective,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music PESTO: Pitch estimation with self-supervised transposition-equivariant objective,

Reference 17

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Observation 1aec51c0-dbf2-4d31-9c40-cb326d58915a · outbound

This paper cites Equivariant self-supervision for musical tempo estimation,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Equivariant self-supervision for musical tempo estimation,

Reference 18

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Observation feb42e93-bf68-4793-bd2c-1ba24f40f1fc · outbound

This paper cites STONE: Self-supervised tonality estimator,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music STONE: Self-supervised tonality estimator,

Reference 19

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Observation 641d2545-39a9-4629-9a57-96c87a55b41a · outbound

This paper cites Toward Fully Self-Supervised Multi-Pitch Estimation.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Toward Fully Self-Supervised Multi-Pitch Estimation

Reference 20

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Observation ec5a174c-4aca-4cd4-b8f8-5499e22a514b · outbound

This paper cites MusicBERT: Symbolic music understanding with large-scale pre-training,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music MusicBERT: Symbolic music understanding with large-scale pre-training,

Reference 21

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Observation 56816d53-98ab-47c9-b4b3-ecfbf5a5d01d · outbound

This paper cites MidiBERT-Piano: Large-scale pre-training for symbolic mu- sic classification tasks,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music MidiBERT-Piano: Large-scale pre-training for symbolic mu- sic classification tasks,

Reference 22

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Observation 3fb6f24a-9bf5-459e-84f8-050c641f1abd · outbound

This paper cites MuseBERT: Pre-training music repre- sentation for music understanding and controllable generation,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music MuseBERT: Pre-training music repre- sentation for music understanding and controllable generation,

Reference 23

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Observation 60ceb9bc-a43e-4a1d-a1d1-e8b6a3db9508 · outbound

This paper cites POP909: A Pop-song Dataset for Music Arrangement Generation.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music POP909: A Pop-song Dataset for Music Arrangement Generation

Reference 24

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Observation 9c696266-0819-4297-8eda-414a2a47e1af · outbound

This paper cites Learning-based methods for comparing sequences, with applications to audio-to-MIDI alignment and matching,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Learning-based methods for comparing sequences, with applications to audio-to-MIDI alignment and matching,

Reference 25

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Observation 0913c3a2-8f0c-4907-b9ac-d5278c0c2602 · outbound

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

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 26

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Observation dd5c211e-632a-4458-8980-3348d351b272 · outbound

This paper cites Swin transformer V2: Scaling up capacity and resolution,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Swin transformer V2: Scaling up capacity and resolution,

Reference 27

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Observation bf195eac-2a4a-45a3-a642-9bcb54505338 · outbound

This paper cites MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation

Reference 28

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Observation c1dd8008-2c85-4e2c-97b2-6e87609b03f3 · outbound

This paper cites Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm

Reference 29

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Observation 12b3eab7-47ac-45db-85e2-b98185d64ac8 · outbound

This paper cites Flow matching for generative modeling,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Flow matching for generative modeling,

Reference 30

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Observation b451ec46-16b4-4739-ab56-ec513128d2ca · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Improving and generalizing flow-based generative models with minibatch optimal transport,

Reference 31

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Observation 9d15d1d9-318a-4ae1-a99f-40ca071cea23 · outbound

This paper cites Classifier-free diffusion guidance,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Classifier-free diffusion guidance,

Reference 32

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Observation 0f5002f7-132f-410f-bb39-efa7556d1dc0 · outbound

This paper cites EMOPIA: A multi-modal pop piano dataset for emo- tion recognition and emotion-based music generation,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music EMOPIA: A multi-modal pop piano dataset for emo- tion recognition and emotion-based music generation,

Reference 33

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Observation 08f58d55-d2ca-4df4-ab93-88342e119122 · outbound

This paper cites Unsupervised deep haar scattering on graphs,.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music Unsupervised deep haar scattering on graphs,

Reference 34

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Pith citing papers

Observation 3fa39840-3961-43e3-a373-51cd70eccff1 · inbound

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music cites this paper.

MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music

Reference 1

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