Pith. sign in

Paper Citation Record · LEDGER

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.22010.

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

pith.paper-citation-record.v1
2607.22010 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:08:27.749037Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved40
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8346fc9b-b14b-4b4d-93f0-06c831f2b3e3 · outbound

This paper cites Generalizable speech deepfake detection via meta-learned lora,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Generalizable speech deepfake detection via meta-learned lora,

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:23.967824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:23.967824Z digest=sha256:1d520461b4a96f81aa11b76e4fb39a6426b33722989d3c7bfbc285da672c9e8b

Observation f88dc750-8531-4f21-aea5-34bf052fad41 · outbound

This paper cites Does Audio Deepfake Detection Generalize?.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Does Audio Deepfake Detection Generalize?

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.060046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.060046Z digest=sha256:46e1e9395eacfb17a5cf3007e5ce1b16309227c75d425c2bc113fa09e8bf4640

Observation 2f9c2144-4169-4d6b-a46f-1e4a6dfee18b · outbound

This paper cites Domain generalization: A survey,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Domain generalization: A survey,

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.133941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.133941Z digest=sha256:e3294d1569107dee1c6f4934325afbece164b04fdcbea7ffcf1792e9888b1c43

Observation 3d7b300c-3cc4-4b6b-bd01-30151fa781cc · outbound

This paper cites Learning to learn: Introduction and overview,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Learning to learn: Introduction and overview,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.293198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.293198Z digest=sha256:480d40dc3fb8b687a134c3a71e30e577d35acb32ad4ac130eedef328113dbed0

Observation ce61daa1-c317-4b7b-a63e-1b4ad58f3dc2 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.379902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.379902Z digest=sha256:eba6155666db5fd0ba65736f37025eef5c92363b356cd04657f95da669bcd388

Observation b4c300ac-de7e-4823-b79e-3f253582e8bd · outbound

This paper cites Meta-learning approaches for improving detection of unseen speech deepfakes,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Meta-learning approaches for improving detection of unseen speech deepfakes,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.529221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.529221Z digest=sha256:9a32462fa72d5e01041b9a2f1dac51c7024203802795f42740a1b9d7ba370e2f

Observation e285f081-c2df-4feb-83f0-58f86b1ee394 · outbound

This paper cites Learning to generalize: Meta-learning for domain generalization,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Learning to generalize: Meta-learning for domain generalization,

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.650899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.650899Z digest=sha256:429285aee51d7155747779cf7322d99c97555355907f5764b95e590f625b6678

Observation b3ef6647-6c5c-4b5f-844c-4b41848441eb · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection LoRA: Low-rank adaptation of large language models,

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.738477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.738477Z digest=sha256:0590ad876e25443772ce3978f4d2a5591a4dec844801672022cd55e7a0322013

Observation 16e0fa0b-511f-4208-a4b7-4c664a4a7cba · outbound

This paper cites Principles of risk minimization for learning theory,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Principles of risk minimization for learning theory,

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.898456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.898456Z digest=sha256:7a9c1f92beafbc54e728e0b97d18d6406bffd0801a080c03ffaa3bddec224599

Observation 1536d5b0-b041-4764-afb5-d8b34f2d4a53 · outbound

This paper cites New insights and perspectives on the natural gradient method,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection New insights and perspectives on the natural gradient method,

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.069472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.069472Z digest=sha256:12fefd46f385bdfa8a039c4027c0cf9caf3896d7146cebdc248a28d548d02bcd

Observation 9a5b4c76-0847-46d5-a56d-670c94f55d09 · outbound

This paper cites The effective rank: A measure of effective dimensionality,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection The effective rank: A measure of effective dimensionality,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.182149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.182149Z digest=sha256:1b0ba428a30c41d0e0326c61010f0d7a23511a60407ead78217a34b466cdfd0f

Observation 05ea43e6-3fc9-4bd6-80f6-05b7d5100719 · outbound

This paper cites Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.272051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.272051Z digest=sha256:5e68bd4d7740936bc477dba0bb747a39ed8839e983a2efbe82a4432aba9e3169

Observation fb77a02d-28a2-46c5-a953-210d744a9701 · outbound

This paper cites Intrinsic dimensionality explains the effectiveness of language model fine-tuning,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Intrinsic dimensionality explains the effectiveness of language model fine-tuning,

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.346042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.346042Z digest=sha256:da02eb61f42f8eb99734716ee42701b2765d49b5bb8d23bd6d02f02bd373ee29

Observation e5e3cef7-152c-4229-b687-4c3d1b903347 · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Adaptive budget allocation for parameter-efficient fine-tuning,

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.428912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.428912Z digest=sha256:2abeb417e153779f65dc64bbb8f45cbf6dcff26472ccea6f2a0125d94641d783

Observation 2ed24da5-bff7-4a00-b8bf-d2697566462c · outbound

This paper cites Pissa: Principal singular values and singular vectors adaptation of large language models,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Pissa: Principal singular values and singular vectors adaptation of large language models,

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.513294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.513294Z digest=sha256:6b3b4f1838d1775fae5fa4277638f650e587d293fd3fa34cf56b00abb57387d2

Observation b8025ffe-e2cb-4eb0-9ee2-bca45df5845c · outbound

This paper cites Lora vs full fine-tuning: An illusion of equivalence,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Lora vs full fine-tuning: An illusion of equivalence,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.606813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.606813Z digest=sha256:7105071d9ecd2993fa6d6ee49992b1ab8af5c67f75cf1326e3db77d3ac2e29b2

Observation b10510d0-2bcb-4e8b-a537-e3eef8cc1ff4 · outbound

This paper cites Asymmetry in low-rank adapters of foundation models,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Asymmetry in low-rank adapters of foundation models,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.668247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.668247Z digest=sha256:375ea1f5c12bf1b65ba404c44ceedfb7b70303c48a7397c48c275865c165d7bf

Observation 8e4cae20-cd6b-4b34-92ad-aa2b500648b6 · outbound

This paper cites LoRA Learns Less and Forgets Less.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection LoRA Learns Less and Forgets Less

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.705441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.705441Z digest=sha256:7a7b270ebc2235c4bdc7d49b24be22d7588b3e14e3dd6c80c282f256638c312d

Observation c32bdcd8-71e2-4f3c-8759-8a077f3e5d3f · outbound

This paper cites Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.824474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.824474Z digest=sha256:5ad20723023230f685300d3cf8b31f4aab0516725827c247e54a68adb64d6228

Observation 6462b643-34b3-4ec0-83ed-d8dea7331b61 · outbound

This paper cites Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Learning in the Fisher Subspace: A Guided Initialization for LoRA Fine-Tuning

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:25.933242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:25.933242Z digest=sha256:ced432c453b956e5fd256bf41ae4945d44945c057cd9deee5fd6e2df03d72583

Observation d0abc3be-3f8d-44ea-b550-8b23de4fde28 · outbound

This paper cites Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.040122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.040122Z digest=sha256:9004b0f7f2de7e3e5d394d9f93b4c28eb37cb65a14bda24c40f87713de89df3f

Observation ded19160-1a95-431c-a6bb-f541df0b1284 · outbound

This paper cites Limitations of the empirical fisher approximation for natural gradient descent,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Limitations of the empirical fisher approximation for natural gradient descent,

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.151391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.151391Z digest=sha256:440f3333afcd6a789fdd302e1428abc54ab456eac2c162def5d17642ccd1ebe3

Observation 257afeff-9ef3-4e83-9998-882f971ed813 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.274328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.274328Z digest=sha256:14fc4c989abc6fa4d14d7f124452153f35404175be6fe478aa0ce2dd2c5203f1

Observation 93025460-b42f-4eae-b99f-af03492b1318 · outbound

This paper cites Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks,

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.354782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.354782Z digest=sha256:f33ebecb7d26d900af70f77f242c0de7001460c8f37a44b27be499d860068754

Observation 444e57f1-0efa-41be-999d-bddec79102b2 · outbound

This paper cites New insights and perspectives on the natural gradient method.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection New insights and perspectives on the natural gradient method

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.493783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.493783Z digest=sha256:8e84d3b35f7f07aad011e6467e12faa598b5f8ae3b8ded2115a5ccc5924c34ec

Observation a9c9eb30-7aca-4f4a-b17a-1b1f2f43efa1 · outbound

This paper cites Optimizing neural networks with kronecker-factored approximate curvature,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Optimizing neural networks with kronecker-factored approximate curvature,

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.598802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.598802Z digest=sha256:d87aac0f04af90862d66f4b456129e0fff2e30dd54e3bf6b8e1c40d613b8e192

Observation f6f42f2b-c267-49ee-a1ae-0f09c55cce96 · outbound

This paper cites An Improved Empirical Fisher Approximation for Natural Gradient Descent.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection An Improved Empirical Fisher Approximation for Natural Gradient Descent

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.702744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.702744Z digest=sha256:987810a2f4d35f873915c4ddb5f0e7f22a65b89f311f0a89e1966597546f5743

Observation eafc7ca4-6d4b-4f54-bf51-97cb4ff09402 · outbound

This paper cites An investigation into neural net optimization via hessian eigenvalue density,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection An investigation into neural net optimization via hessian eigenvalue density,

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.783478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.783478Z digest=sha256:b40e46a5025688679f6767b62e6998d20666189f4d6f4ce249b703e7f97d9670

Observation 9dae94ce-8d3c-4e07-a173-a9a11a67bd9d · outbound

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

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Neural tangent kernel: Convergence and generalization in neural networks,

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.870135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.870135Z digest=sha256:296ce83bfce65f0af16f24b76935b148b851e0541bde04da6dc122551efaf584

Observation 6c32635c-384c-4f86-af7e-ebeee338c32e · outbound

This paper cites RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.040474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.040474Z digest=sha256:3b40155d1e24b57daff6153d2f6e13f9c381e46021676e47cc3e3deca319d651

Observation d5a9fd55-a141-4132-94ce-b54f29e706d5 · outbound

This paper cites Spectral alignment of stochastic gradient descent for high-dimensional classification tasks.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Spectral alignment of stochastic gradient descent for high-dimensional classification tasks

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.109643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.109643Z digest=sha256:b1df7f0b5ef8002b1c535553cfdff20be4c941a7d2390665fa91192606c26770

Observation c3dbec53-cd41-4b8b-92cc-8e62eb210573 · outbound

This paper cites ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection,

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.184551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.184551Z digest=sha256:fcb8dffa31078d8a7a5ce340e2d8dae1086628c692fdee1b2b3ab2c38f4b5ea6

Observation e83b3de9-4a11-401f-b740-0bdada43e83a · outbound

This paper cites ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.261924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.261924Z digest=sha256:bac5cc9d12a0c7fc29461b407b03cf54fe97e86ee020f7f2f2d50a6ea42b96f7

Observation 36297aa2-ee53-45c5-8c8a-808525cd3d3c · outbound

This paper cites ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.367744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.367744Z digest=sha256:adefc473a930f7e80075501dafac7474ed241c34c8b1747c74629c1d8a9d26ea

Observation cb90ba01-fab1-4831-b3bf-919d156dc645 · outbound

This paper cites FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.474845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.474845Z digest=sha256:e83ba497735a5b989349552e934cb43f2db60aab531a89291894bfb49e8f0fd7

Observation 2054ba4f-37bc-48f7-929b-8e2ba982880b · outbound

This paper cites Audio deepfake detection: A survey,.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Audio deepfake detection: A survey,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.571914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.571914Z digest=sha256:ede488e6412dde6dd15daa4dde5649290ded3cb610d7eff082d4347db26ec8b1

Observation 3b2c8806-5029-448d-a9f0-f8d30e158781 · outbound

This paper cites Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.749037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.749037Z digest=sha256:a014555fd11a8d3d6e6b47f600764292716e2b136aeb624c93f5aa0be708d0f8

Observation 6efd4d0f-16af-4acb-a937-54ddfebc85c9 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:26.977225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:26.977225Z digest=sha256:4086e39e005b962c874a123afc5e7a5c2c7c244476dd80b5bc4d7f063922dd4b

Observation 9ecae8c7-8b41-48a7-ab47-b5f94b9efde0 · outbound

This paper cites Domain Generalization: A Survey.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Domain Generalization: A Survey

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:24.220094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:24.220094Z digest=sha256:7be444c2d384db2215d8a0801cd98f89243178b1f9cf963ffc620f9544340c02

Observation 3e4629c8-d473-409a-8272-d5c11dd0b926 · outbound

This paper cites Audio Deepfake Detection: A Survey.

How Meta-Learning Shapes LoRA Adapter Geometry in Speech Deepfake Detection Audio Deepfake Detection: A Survey

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-01T06:08:27.680708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:08:27.680708Z digest=sha256:99bdad94c207e7fbe915d62889650384cad76ee6330bb5475401c8e03854794d

Pith citing papers

No inbound Pith citation observations are available.