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Paper Citation Record · LEDGER

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2601.22068.

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

pith.paper-citation-record.v1
2601.22068 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:48:16.071186Z

measured 19 of 19 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

19 of 19 outbound references displayed

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External citation measurements

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Outbound references

Observation 162f9fca-8fed-43e3-93e4-525f2fc0d483 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 3

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:48:15.980675Z digest=sha256:52355e7242b0ff4ed43bf554f972aa581e6bfce4399a96eaa48e1f2957652245

Observation 6b581bc9-1e96-4602-8359-d83948466c46 · outbound

This paper cites Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting

Reference 4

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source=pdf_text observed=2026-08-03T06:48:15.986118Z digest=sha256:87eb7fd1531bc2cf37f56be0693d4e3f13349c5964ce84e6ef971e054671e89e

Observation 75232249-cae0-4b5d-b518-5bce51afd340 · outbound

This paper cites Toy Models of Superposition.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Toy Models of Superposition

Reference 6

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source=pdf_text observed=2026-08-03T06:48:15.996992Z digest=sha256:d8dfd4afc0fdd6ad9c4915080f6f036e43d31cf4d411661c64e66403608033b0

Observation 2e5805ce-1b2a-4601-8454-cc6f0b4a61e4 · outbound

This paper cites Mistral 7B.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Mistral 7B

Reference 10

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source=pdf_text observed=2026-08-03T06:48:16.018720Z digest=sha256:8bc16f5285398fd0137ea285027f47409332ee038cab672b247f26930694735d

Observation d38f3d1c-90f2-4953-954a-93e1ed60e6b9 · outbound

This paper cites J., Halbheer, M., Becker, A., Narnhofer, D., Aasen, H., Schindler, K., and Turkoglu, M.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles J., Halbheer, M., Becker, A., Narnhofer, D., Aasen, H., Schindler, K., and Turkoglu, M

Reference 12

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source=pdf_text observed=2026-08-03T06:48:16.028856Z digest=sha256:8b82d6305c0773f9d0b93db07cb6619251ba59b78af2455ae7de6ac8495f21b7

Observation 2904e053-10b9-4d57-90a3-c80522d78a7d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles LLaMA: Open and Efficient Foundation Language Models

Reference 15

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source=pdf_text observed=2026-08-03T06:48:16.047239Z digest=sha256:57b59e7e1214d74f1a24b9a5bc36aaee8ebf092c62460db257650e5f2cdc74a7

Observation c4fa1086-552c-4344-9d78-966dd1b2a338 · outbound

This paper cites Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity

Reference 16

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source=pdf_text observed=2026-08-03T06:48:16.055144Z digest=sha256:19d7215b1950c168c2bafb5d90834df9a2f1a1a65d6e4713ec895885fdb8bd36

Observation 55f19760-1ca3-4352-80cb-e00b074646c2 · outbound

This paper cites LoRA ensembles for large language model fine-tuning.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles LoRA ensembles for large language model fine-tuning

Reference 17

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source=pdf_text observed=2026-08-03T06:48:16.061140Z digest=sha256:5db6b48b7ee9362d4b5893403d7e99b4de0bd7529d0e016df9be0b9892dc06b5

Observation 3ed4a578-88c0-4ee5-87a8-a7efa43994a7 · outbound

This paper cites an unresolved cited work.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Unresolved cited work

Reference 2009

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source=pdf_text observed=2026-08-03T06:48:16.071186Z digest=sha256:f1482a8e0e2f0cc0a99a28c92ef344ebc4e5a7f4103c94f79af8881fdf4efcda

Observation 4e91d25a-e3c6-4dc1-b7fe-fd8d6afdbd46 · outbound

This paper cites Small singular values matter: A random matrix analysis of transformer models.arXiv preprint arXiv:2410.17770,.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Small singular values matter: A random matrix analysis of transformer models.arXiv preprint arXiv:2410.17770,

Reference 2013

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source=pdf_text observed=2026-08-03T06:48:16.035056Z digest=sha256:55e5afd0071e561da92e9ba1033de016c990c854c4b0b345aa7187350d488b70

Observation 71c31178-5364-4a2f-8b1f-a97f53e9069f · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2014

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source=pdf_text observed=2026-08-03T06:48:15.991451Z digest=sha256:e0b9ab6381a6dbd76476097477f4b5b254f134a0615f3e34725b64de13101f23

Observation 3d266d02-7a44-49fb-924b-fa60ea07adb8 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles On the Opportunities and Risks of Foundation Models

Reference 2015

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source=pdf_text observed=2026-08-03T06:48:15.975455Z digest=sha256:ddf8cafb0a342be6425ce1c92682774ada0ab41712b6805d9f7292823c57b971

Observation 92bac2f4-8e3d-45c7-86c0-164d4271cf66 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 2017

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source=pdf_text observed=2026-08-03T06:48:16.023629Z digest=sha256:6fd3646f318692dd3d95a0d1db2dd9fbd6618600bbec3c14d317c7ffdfa0d330

Observation 259e9184-1ec9-49de-8bd0-6ccc7fb9cc35 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 2019

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source=pdf_text observed=2026-08-03T06:48:16.013861Z digest=sha256:56c9fd896717c93fb425eb418b2dc3413a265ae1a404c9d45a60aa10ed4f92dc

Observation 110782ad-5c59-4e23-8986-064f930d11ed · outbound

This paper cites OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation

Reference 2020

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source=pdf_text observed=2026-08-03T06:48:16.002565Z digest=sha256:86b01522990a9dd7b6aa646a0288666a8d506780e6fcf4cfdabab092a60293be

Observation 4e39e09e-0c8e-4a4f-83a0-9b0b4975d3b6 · outbound

This paper cites Qwen Technical Report.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Qwen Technical Report

Reference 2021

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source=pdf_text observed=2026-08-03T06:48:15.969893Z digest=sha256:d8d13ed09706ddbf9ff80be09ff88056ee0f5692c416c93353ad0e7a9d9043a6

Observation 68a13a1b-0d55-46e8-8b36-d22286696a32 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 2022

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source=pdf_text observed=2026-08-03T06:48:16.008351Z digest=sha256:9ec0ea7f490e83de089ba801acf5525b8ce184ba4a397dde098074032797ca76

Observation c11688b4-da3a-4471-9271-975545bc857e · outbound

This paper cites Ablation Study: Number of Ensemble Members Both accuracy and calibration improve significantly from M= 1 to M= 16 , on a complex task.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles Ablation Study: Number of Ensemble Members Both accuracy and calibration improve significantly from M= 1 to M= 16 , on a complex task

Reference 2023

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source=pdf_text observed=2026-08-03T06:48:16.065954Z digest=sha256:d9bb84c88455e42c1a9e8b2882501e0385adeebb58ff818c8591032e228128c1

Observation 2b0eeedb-b04c-4eb3-b937-e97b915835b4 · outbound

This paper cites SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values.

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values

Reference 2024

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Pith citing papers

No inbound Pith citation observations are available.