Paper Citation Record · LEDGER

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.15798.

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

pith.paper-citation-record.v1
2505.15798 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:18:34.244695Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 7a1b1e06-07cb-414b-9fa7-47714864649c · outbound

This paper cites https://huggingface.co/Dolphin-2.1-Mistral-7B.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning https://huggingface.co/Dolphin-2.1-Mistral-7B

Reference 1

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Observation ad699ec8-9f1c-4bf9-8263-62e721f746a2 · outbound

This paper cites https://huggingface.co/MetaMath-Mistral-7B.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning https://huggingface.co/MetaMath-Mistral-7B

Reference 2

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This paper cites https://huggingface.co/ Speechless-Code-Mistral-7B-v1.0.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning https://huggingface.co/ Speechless-Code-Mistral-7B-v1.0

Reference 3

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Observation 58af553b-51a5-49ed-b2f8-0c91d7d01f1a · outbound

This paper cites Evolutionary optimization of model merging recipes.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Evolutionary optimization of model merging recipes

Reference 4

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Observation eea5609d-508c-4df0-9e96-cb3c38f166fa · outbound

This paper cites User-friendly introduction to PAC-Bayes bounds.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning User-friendly introduction to PAC-Bayes bounds

Reference 5

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Observation dfd603ff-4808-4fa6-8ec0-7552304f8bb0 · outbound

This paper cites SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:39.952333Z

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Observation 5ebed87a-486e-4bb5-a90e-48ce3ae8b863 · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Remote sensing image scene classification: Benchmark and state of the art

Reference 7

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Observation e9bcdc5f-0f0e-4481-9b22-ccf6bc1f1285 · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Describing textures in the wild

Reference 8

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Observation 92156348-2d27-4937-832a-cc9767c593d6 · outbound

This paper cites Model breadcrumbs: Scaling multi-task model merging with sparse masks.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Model breadcrumbs: Scaling multi-task model merging with sparse masks

Reference 9

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Observation 6502cb83-d36a-431b-a7fb-5ed1710579c0 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation eb4c1c01-1acf-43ab-a7a5-16b4de28b405 · outbound

This paper cites Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data

Reference 11

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Observation 6070768b-24ad-4c8f-98c0-1002273cea89 · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Unresolved cited work

Reference 12

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Observation f813858e-b38a-41ff-8888-5b48f64b5b94 · outbound

This paper cites Arcee's MergeKit: A Toolkit for Merging Large Language Models.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Arcee's MergeKit: A Toolkit for Merging Large Language Models

Reference 13

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Observation b93f8573-21a6-4819-9737-b9f93fe59496 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 14

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Observation 8a3d5f87-0d50-4cc8-a639-1acec00dbb13 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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Observation 0187f126-abfb-43a7-a20d-0a351751c25e · outbound

This paper cites LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Reference 16

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Editing models with task arithmetic

Reference 17

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Observation 274b4db5-8710-40e5-a14b-6547ab57afd6 · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Mistral 7B

Reference 18

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Observation 620c2983-42d0-4d03-9577-88b7195e9c27 · outbound

This paper cites Fantastic generalization measures and where to find them.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Fantastic generalization measures and where to find them

Reference 19

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning 3d object representations for fine- grained categorization

Reference 20

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Observation 5349625c-1235-4757-ae1a-4c34743fe410 · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Tutorial on practical prediction theory for classification

Reference 21

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Observation 348a4607-e082-44ff-9a72-b64cd9ccd1d1 · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Bounds for averaging classifiers

Reference 22

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Observation ce52f18f-5d7c-4dd3-b1ef-fa18ac78addc · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Gradient-based learning applied to document recognition

Reference 23

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Learning new tricks from old dogs: Multi-source transfer learning from pre-trained networks

Reference 24

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Observation 001b5d8a-0b71-407f-9b32-d52c141a150b · outbound

This paper cites Deep Model Fusion: A Survey.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Deep Model Fusion: A Survey

Reference 25

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Unresolved cited work

Reference 26

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Observation 86ddb6f2-f69a-4603-b4ae-9a74844371af · outbound

This paper cites Merging models with fisher-weighted averaging.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Merging models with fisher-weighted averaging

Reference 27

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Observation 784ada7c-49f8-47c8-9183-54a8e37fee2e · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Trustworthy Machine Learning

Reference 28

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Observation 7703f498-98aa-43d8-88b1-8dd3a3baf3d8 · outbound

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Reading digits in natural images with unsupervised feature learning

Reference 29

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Exploring generalization in deep learning

Reference 30

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning A survey on transfer learning

Reference 31

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Tighter risk certificates for neural networks

Reference 32

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Learning transferable visual models from natural language supervision

Reference 33

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Rapin and O

Reference 34

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This paper cites Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Pac-bayesian generalisation error bounds for gaussian process classification

Reference 36

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Understanding machine learning: From theory to algorithms

Reference 37

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This paper cites Evaluating the evaluators: Are validation methods for few-shot learning fit for purpose? Transactions on Machine Learning Research, 2024.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Evaluating the evaluators: Are validation methods for few-shot learning fit for purpose? Transactions on Machine Learning Research, 2024

Reference 38

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning A com- prehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities

Reference 39

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Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Unresolved cited work

Reference 40

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This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 41

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This paper cites Fusionbench: A compre- hensive benchmark of deep model fusion.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Fusionbench: A compre- hensive benchmark of deep model fusion

Reference 42

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Observation c45e0d58-7e07-4050-87f1-ed789491e8bc · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Sun database: Large-scale scene recognition from abbey to zoo

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:35.472345Z

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Observation 7d427dd9-a3a3-4748-8c92-11685da281f6 · outbound

This paper cites Ties-merging: Resolving interference when merging models.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Ties-merging: Resolving interference when merging models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:35.184771Z

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Observation 213deba6-b928-43d6-bf8d-3337fe62cb77 · outbound

This paper cites Adamerging: Adaptive model merging for multi-task learning.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning Adamerging: Adaptive model merging for multi-task learning

Reference 45

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Observation 9ca80f36-d132-4f96-a0c4-f22dddbdf190 · outbound

This paper cites + Bound” indicate that we use the PAC-Bayes bound as the optimisation objective. We also provide an additional “Upper Bound.

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning + Bound” indicate that we use the PAC-Bayes bound as the optimisation objective. We also provide an additional “Upper Bound

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:18:34.701657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.