Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2310.00902.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T04:31:41.343121Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
0 of 0 outbound references displayed
External citation measurements
5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
No outbound reference observations are available for this paper version.
Observation 680e511c-2a57-4e6f-a378-ef640abe1669 · inbound
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 2021
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e7f4c45-5cc4-40e4-bffe-ed118d34e932 · inbound
Fairshare Data Pricing via Data Valuation for Large Language Models DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 21
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e10875ad-cf06-4d55-8fe9-b063a61555a3 · inbound
Low-Perplexity LLM-Generated Sequences and Where To Find Them DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 19
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e2ae71c0-b837-4dbb-9ef4-62d50e9e3ee5 · inbound
CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 22
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37559ed8-5782-44c3-bd40-7f1aac894895 · inbound
Newfluence: Boosting Model interpretability and Understanding in High Dimensions DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 19
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 685b9411-9efc-4cae-8004-a92971097cfa · inbound
Influence Functions for Preference Dataset Pruning DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 8
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5808b216-d8c7-422f-80df-41426c8e9ac0 · inbound
Better Training Data Attribution via Better Inverse Hessian-Vector Products DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 63
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2cfbaa79-bd68-4b9f-a64c-350d4bcbbfe1 · inbound
Understanding Data Influence with Differential Approximation DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 3
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dbd49227-867b-4d4e-96f6-2dc467f1592e · inbound
Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 2017
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e1e642b-b84f-4050-8b14-43b97d8e860e · inbound
Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 2017
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d036e6bf-d18c-4a4c-97ef-ed4eb33123c9 · inbound
DataDignity: Training Data Attribution for Large Language Models DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 15
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4d39f370-c1bb-49f8-8176-076173fdca0d · inbound
On the Fragility of Data Attribution When Learning Is Distributed DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 11
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation aedcc4e2-5d2f-456b-83c6-9b4eda261253 · inbound
PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 20
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f225a8e2-aebe-45ae-8520-778d7edb3394 · inbound
Variance Reduction for Expectations with Diffusion Teachers DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 28
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8ba02484-088b-4f23-a433-2e50a8b94932 · inbound
Variance Reduction for Expectations with Diffusion Teachers DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 28
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ca88689d-3ad8-40e4-93f5-6fc04dd6cf56 · inbound
DRIFT: Refining Instruction Data via On-Policy Data Attribution DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 8
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 296382bf-b552-4388-8294-4ef880ee5b58 · inbound
Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 28
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a196bd61-631e-4e62-81be-ce15d5f33917 · inbound
HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 48
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ebd29855-36cf-4707-9d0c-8e9697c3e249 · inbound
One Modality to Forget Them All: Enhancing Cross-Modal Unlearning in Vision-Language Models DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 24
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 306768cd-e2ae-447f-9207-16cc177f9a01 · inbound
Dataset Distillation by Influence Matching DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 31
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a582b594-9750-44f4-8e63-7a4495569e93 · inbound
A Reference-Free Framework for Evaluating Single-Frame ISP Pipelines DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Reference 89
Source-reported events for the cited work
Unavailable: canonical work link unavailable.