Paper Citation Record · LEDGER

Taming Audio VAEs via Target-KL Regularization

As of 4 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2605.17085.

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

pith.paper-citation-record.v1
2605.17085 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T14:53:15.718359Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-20T14:53:23.198073Z

Reference resolution

49 of 49 outbound references displayed

verified exact17
verified fuzzy25
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External citation measurements

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Outbound references

Observation 2801ff57-1e74-4225-b0f4-afd3a9f4636a · outbound

This paper cites an unresolved cited work.

Taming Audio VAEs via Target-KL Regularization Unresolved cited work

Reference 1

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Taming Audio VAEs via Target-KL Regularization Unresolved cited work

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Taming Audio VAEs via Target-KL Regularization Unresolved cited work

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Taming Audio VAEs via Target-KL Regularization Unresolved cited work

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This paper cites Taming Audio VAEs via Target-KL Regularization.

Taming Audio VAEs via Target-KL Regularization Taming Audio VAEs via Target-KL Regularization

Reference 5

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This paper cites Model architecture Our model is built on the same framework of neural audio codec models, except we replace the quantization bottleneck with a gaus- sian regularization.

Taming Audio VAEs via Target-KL Regularization Model architecture Our model is built on the same framework of neural audio codec models, except we replace the quantization bottleneck with a gaus- sian regularization

Reference 6

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Taming Audio VAEs via Target-KL Regularization Unresolved cited work

Reference 7

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This paper cites This allows for direct comparison to discrete neural audio codecs and enables systematic study of the rate-distortion trade-off for continuous audio compres- sion models.

Taming Audio VAEs via Target-KL Regularization This allows for direct comparison to discrete neural audio codecs and enables systematic study of the rate-distortion trade-off for continuous audio compres- sion models

Reference 8

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This paper cites Neural discrete representation learning.

Taming Audio VAEs via Target-KL Regularization Neural discrete representation learning

Reference 9

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Observation 1ed826fb-88ba-4124-8c89-3ed2c5b9db55 · outbound

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

Taming Audio VAEs via Target-KL Regularization High-resolution image synthesis with latent diffusion models

Reference 10

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Observation 92576b4d-5b78-447a-ad0d-bee40344e3ca · outbound

This paper cites Audiolm: a language modeling approach to audio generation.

Taming Audio VAEs via Target-KL Regularization Audiolm: a language modeling approach to audio generation

Reference 11

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This paper cites Stable audio open.

Taming Audio VAEs via Target-KL Regularization Stable audio open

Reference 12

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This paper cites Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.

Taming Audio VAEs via Target-KL Regularization Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 13

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This paper cites VampNet: Music Generation via Masked Acoustic Token Modeling.

Taming Audio VAEs via Target-KL Regularization VampNet: Music Generation via Masked Acoustic Token Modeling

Reference 14

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Observation b8583be2-aef0-4be5-98d7-8b637f38461b · outbound

This paper cites MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer.

Taming Audio VAEs via Target-KL Regularization MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer

Reference 15

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Observation 76880868-1f65-483d-a52e-60e57fb1c33d · outbound

This paper cites SoundStorm: Efficient Parallel Audio Generation.

Taming Audio VAEs via Target-KL Regularization SoundStorm: Efficient Parallel Audio Generation

Reference 16

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Observation 7d7aca55-741a-471d-add7-dd127d086ea2 · outbound

This paper cites Auto-Encoding Variational Bayes.

Taming Audio VAEs via Target-KL Regularization Auto-Encoding Variational Bayes

Reference 17

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This paper cites Denoising dif- fusion probabilistic models.

Taming Audio VAEs via Target-KL Regularization Denoising dif- fusion probabilistic models

Reference 18

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This paper cites Au- dioLDM: Text-to-audio generation with latent diffusion mod- els.

Taming Audio VAEs via Target-KL Regularization Au- dioLDM: Text-to-audio generation with latent diffusion mod- els

Reference 19

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This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

Taming Audio VAEs via Target-KL Regularization Scaling rectified flow transformers for high-resolution image synthesis

Reference 20

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This paper cites Soundstream: An end-to- end neural audio codec.

Taming Audio VAEs via Target-KL Regularization Soundstream: An end-to- end neural audio codec

Reference 21

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This paper cites High-fidelity audio compression with improved rvqgan.

Taming Audio VAEs via Target-KL Regularization High-fidelity audio compression with improved rvqgan

Reference 22

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This paper cites In- terpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection.

Taming Audio VAEs via Target-KL Regularization In- terpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection

Reference 23

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This paper cites Practical Lossless Compression with Latent Variables using Bits Back Coding.

Taming Audio VAEs via Target-KL Regularization Practical Lossless Compression with Latent Variables using Bits Back Coding

Reference 24

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This paper cites Fixing a Broken ELBO.

Taming Audio VAEs via Target-KL Regularization Fixing a Broken ELBO

Reference 25

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This paper cites Improved variational in- ference with inverse autoregressive flow.

Taming Audio VAEs via Target-KL Regularization Improved variational in- ference with inverse autoregressive flow

Reference 26

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This paper cites An introduction to variational autoencoders.

Taming Audio VAEs via Target-KL Regularization An introduction to variational autoencoders

Reference 27

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This paper cites BigVGAN: A Universal Neural Vocoder with Large-Scale Training.

Taming Audio VAEs via Target-KL Regularization BigVGAN: A Universal Neural Vocoder with Large-Scale Training

Reference 28

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This paper cites Moshi: a speech-text foundation model for real-time dialogue.

Taming Audio VAEs via Target-KL Regularization Moshi: a speech-text foundation model for real-time dialogue

Reference 29

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This paper cites Audio set: An ontology and human-labeled dataset for audio events.

Taming Audio VAEs via Target-KL Regularization Audio set: An ontology and human-labeled dataset for audio events

Reference 30

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This paper cites SpectroStream: A Versatile Neural Codec for General Audio.

Taming Audio VAEs via Target-KL Regularization SpectroStream: A Versatile Neural Codec for General Audio

Reference 31

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Taming Audio VAEs via Target-KL Regularization High Fidelity Neural Audio Compression

Reference 32

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Observation a1ea9b96-7ea7-4f91-a008-fa96c690987a · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Taming Audio VAEs via Target-KL Regularization Progressive Distillation for Fast Sampling of Diffusion Models

Reference 33

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This paper cites sim- ple diffusion: End-to-end diffusion for high resolution im- ages.

Taming Audio VAEs via Target-KL Regularization sim- ple diffusion: End-to-end diffusion for high resolution im- ages

Reference 34

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This paper cites Simple-tts: End-to-end text-to-speech synthesis with latent diffusion.

Taming Audio VAEs via Target-KL Regularization Simple-tts: End-to-end text-to-speech synthesis with latent diffusion

Reference 35

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This paper cites Scalable diffusion models with transformers.

Taming Audio VAEs via Target-KL Regularization Scalable diffusion models with transformers

Reference 36

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This paper cites Ditto-tts: Efficient and scalable zero-shot text-to-speech with diffusion transformer.

Taming Audio VAEs via Target-KL Regularization Ditto-tts: Efficient and scalable zero-shot text-to-speech with diffusion transformer

Reference 37

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This paper cites Autoregressive Diffusion Transformer for Text-to-Speech Synthesis.

Taming Audio VAEs via Target-KL Regularization Autoregressive Diffusion Transformer for Text-to-Speech Synthesis

Reference 38

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This paper cites Byt5: Towards a token-free future with pre-trained byte-to- byte models.

Taming Audio VAEs via Target-KL Regularization Byt5: Towards a token-free future with pre-trained byte-to- byte models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T14:53:23.521781Z

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

Taming Audio VAEs via Target-KL Regularization Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T14:53:23.536255Z

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Observation 6c3d5fca-e480-47fb-8de3-1a6bfa14f44e · outbound

This paper cites Phonemizer: Text to phones transcription for multiple languages in python.

Taming Audio VAEs via Target-KL Regularization Phonemizer: Text to phones transcription for multiple languages in python

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T14:53:23.541832Z

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Observation 3c53ee44-f5df-41db-84f7-c92726aefef5 · outbound

This paper cites Emilia: A large-scale, extensive, multilin- gual, and diverse dataset for speech generation.

Taming Audio VAEs via Target-KL Regularization Emilia: A large-scale, extensive, multilin- gual, and diverse dataset for speech generation

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:53:23.227927Z

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Observation 3e4bfec1-d8dd-4d70-8845-cb42683db895 · outbound

This paper cites SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation.

Taming Audio VAEs via Target-KL Regularization SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:53:23.240224Z

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Observation 4492068a-99c8-41aa-b65c-f503dcfe9086 · outbound

This paper cites Sketch2sound: Controllable audio generation via time-varying signals and sonic imitations.

Taming Audio VAEs via Target-KL Regularization Sketch2sound: Controllable audio generation via time-varying signals and sonic imitations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T14:53:23.503422Z

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Observation 2deef7fe-17c5-41e7-86e9-fd1420012cc4 · outbound

This paper cites FLAM: Frame-wise language-audio model- ing.

Taming Audio VAEs via Target-KL Regularization FLAM: Frame-wise language-audio model- ing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T14:53:23.516502Z

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Observation f9db439e-e46e-4f1f-ace9-3ae10376f717 · outbound

This paper cites Scaling instruction- finetuned language models.

Taming Audio VAEs via Target-KL Regularization Scaling instruction- finetuned language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T14:53:23.549390Z

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Observation 59e97aa2-3ac4-4d1f-b1a7-bde25cad6181 · outbound

This paper cites Seed-TTS: A Family of High-Quality Versatile Speech Generation Models.

Taming Audio VAEs via Target-KL Regularization Seed-TTS: A Family of High-Quality Versatile Speech Generation Models

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-20T14:53:23.190532Z

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Observation 76bbbf21-7af8-4adf-9c7d-07e30845c50b · outbound

This paper cites Finite Scalar Quantization: VQ-VAE Made Simple.

Taming Audio VAEs via Target-KL Regularization Finite Scalar Quantization: VQ-VAE Made Simple

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-20T14:53:23.196540Z

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Observation 6d3b93c9-7adf-4e9d-ae65-8fa20a0d5050 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Taming Audio VAEs via Target-KL Regularization Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-20T14:53:23.202963Z

Source-reported events for the cited work

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Pith citing papers

Observation 08e5bc35-139f-4b17-88df-1dfb9801d680 · inbound