Paper Citation Record · LEDGER

LoRA ensembles for large language model fine-tuning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2310.00035.

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

pith.paper-citation-record.v1
2310.00035 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:48:16.061140Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:09:45.341936Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 48482b57-d6e9-43cd-88cd-e134d4e23228 · inbound

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs cites this paper.

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs LoRA ensembles for large language model fine-tuning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:35:47.217161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd0f5501-d24f-4e05-a506-e637fcf20386 · inbound

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems cites this paper.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems LoRA ensembles for large language model fine-tuning

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.948220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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

Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles cites this paper.

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

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T06:48:16.061140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d7c496f-8247-46fa-b0f7-b24d6d96116a · inbound

Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters cites this paper.

Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters LoRA ensembles for large language model fine-tuning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:03:12.314363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a0c2b207-19d6-4893-ae0e-fc070e655ef5 · inbound

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates LoRA ensembles for large language model fine-tuning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:35:52.817410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50af7397-faed-4c29-b9d5-b93e84fbea2b · inbound

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates cites this paper.

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates LoRA ensembles for large language model fine-tuning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-13T09:41:56.414542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfd45e17-132f-4ec3-8962-d92007fa7cf5 · inbound

Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification cites this paper.

Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification LoRA ensembles for large language model fine-tuning

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:31:30.600668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a790bb4-784a-4ae0-a78f-25621f7c390a · inbound

Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis cites this paper.

Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis LoRA ensembles for large language model fine-tuning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:21:13.366798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3888ccec-e02c-45fa-948e-be9e8696b986 · inbound

BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models cites this paper.

BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models LoRA ensembles for large language model fine-tuning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:14.016903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b105615a-c5b4-4eef-8adb-236869f6d66e · inbound

Epistemic Uncertainty for Test-Time Discovery cites this paper.

Epistemic Uncertainty for Test-Time Discovery LoRA ensembles for large language model fine-tuning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:57:06.125375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ea315a8-d0cc-43f0-bb07-09c6878ffa89 · inbound

Spectral Souping: A Unified Framework for Online Preference Alignment cites this paper.

Spectral Souping: A Unified Framework for Online Preference Alignment LoRA ensembles for large language model fine-tuning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.903660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f07b4af7-42b3-4df7-8406-aa27544e7dc3 · inbound

Conf-Gen: Conformal Uncertainty Quantification for Generative Models cites this paper.

Conf-Gen: Conformal Uncertainty Quantification for Generative Models LoRA ensembles for large language model fine-tuning

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:03:29.747445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 653ddf94-e33b-4758-b864-c13a38515fc3 · inbound

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models cites this paper.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models LoRA ensembles for large language model fine-tuning

Reference 106

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:09:45.343322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79a009c3-2d02-4482-9e17-bcfe5f1276fa · inbound

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning cites this paper.

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning LoRA ensembles for large language model fine-tuning

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:04:28.511765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d13d080b-b4b0-46cb-bb0d-fadde9f3dd2a · inbound

Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation cites this paper.

Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation LoRA ensembles for large language model fine-tuning

Reference 41

Resolution: malformed identifier; arxiv_id, observed 2026-07-03T17:08:42.838535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 389d6158-13a9-4949-a1b7-24a860e154a4 · inbound

Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition cites this paper.

Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition LoRA ensembles for large language model fine-tuning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T07:10:36.252051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.