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Paper Citation Record · LEDGER

StackingNet: Collective Inference Across Independent AI Foundation Models

As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2602.13792.

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

pith.paper-citation-record.v1
2602.13792 v3

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:31:27.686541Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

50 of 50 outbound references displayed

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Outbound references

Observation 597c14b6-688e-4b9e-8a1b-17110e34a9ee · outbound

This paper cites A Survey of Large Language Models.

StackingNet: Collective Inference Across Independent AI Foundation Models A Survey of Large Language Models

Reference 1

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Observation bdce0da2-bf88-4e6a-8ec0-afbfb60e222c · outbound

This paper cites an unresolved cited work.

StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 2

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Observation 047a51fd-fa91-4ec5-9563-a11442c02ccc · outbound

This paper cites an unresolved cited work.

StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 3

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Observation 44aa731b-657d-4c95-a8b5-97a96d1ba367 · outbound

This paper cites an unresolved cited work.

StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 4

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Observation 0f25a375-9ebc-4a00-9196-d4acd1a75319 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

StackingNet: Collective Inference Across Independent AI Foundation Models Evaluating Large Language Models Trained on Code

Reference 5

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Observation a5a86cf2-4877-464f-a707-6d5c2b3b21cf · outbound

This paper cites S., Wang, Y.

StackingNet: Collective Inference Across Independent AI Foundation Models S., Wang, Y

Reference 6

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Observation 33335612-32db-4a3f-b5f4-ae07208548a5 · outbound

This paper cites an unresolved cited work.

StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 7

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Observation abddd7ce-11ce-4f58-80fb-6d9488dba6a0 · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

StackingNet: Collective Inference Across Independent AI Foundation Models Large Language Models for Data Annotation and Synthesis: A Survey

Reference 8

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Observation 7ee888ca-9555-4460-bfa8-3ecbf8dcb9ca · outbound

This paper cites & Lin, J.

StackingNet: Collective Inference Across Independent AI Foundation Models & Lin, J

Reference 9

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Observation 3300c529-9e74-46b2-8f4a-e4f4c88b22eb · outbound

This paper cites Can large language models provide useful feedback on resear ch papers? a large-scale empirical analysis.

StackingNet: Collective Inference Across Independent AI Foundation Models Can large language models provide useful feedback on resear ch papers? a large-scale empirical analysis

Reference 10

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Observation 1f48e603-c3bf-4c9a-bfb1-20c646de4039 · outbound

This paper cites an unresolved cited work.

StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 11

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Observation 5f7ac465-9705-4d03-85f3-dbc391841c7d · outbound

This paper cites Advances, challenges and opportunities in creating data fo r trustworthy AI.

StackingNet: Collective Inference Across Independent AI Foundation Models Advances, challenges and opportunities in creating data fo r trustworthy AI

Reference 12

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Observation ccc29ca4-982c-42ab-84f8-6ee3aedba558 · outbound

This paper cites & V ayena, E.

StackingNet: Collective Inference Across Independent AI Foundation Models & V ayena, E

Reference 13

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Observation 58ae9417-afe6-4f45-a271-c221d65e59f8 · outbound

This paper cites an unresolved cited work.

StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 14

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Observation a7a92649-32ee-4b20-95a5-3e269f8eafa2 · outbound

This paper cites P ., Wu, C., Morency, L.-P.

StackingNet: Collective Inference Across Independent AI Foundation Models P ., Wu, C., Morency, L.-P

Reference 15

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Observation c567d1f0-cf81-4710-b686-c1a67c3d2d8b · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation 3dcead4f-8ff9-4863-bfd4-2a10e2f35a6a · outbound

This paper cites AI Alignment: A Comprehensive Survey.

StackingNet: Collective Inference Across Independent AI Foundation Models AI Alignment: A Comprehensive Survey

Reference 17

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Observation b2333cbc-9c6d-4d13-86b4-0bc0f92d9bce · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Fan, Y

Reference 18

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Observation fcb41f25-646a-4bd6-8a09-59f9e468d634 · outbound

This paper cites PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing.

StackingNet: Collective Inference Across Independent AI Foundation Models PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing

Reference 19

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Observation d02fe31c-04fb-47fc-9118-57febbe67531 · outbound

This paper cites A., Y ang, J.

StackingNet: Collective Inference Across Independent AI Foundation Models A., Y ang, J

Reference 20

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Observation 955800c5-8a57-44c0-b9da-3c59bf448b29 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Franklin, M

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Observation 85efdc67-9f43-406f-854e-f892fe900310 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation 628a177a-614b-482c-bdad-3396a29bcaea · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation d4b6ddc0-92bd-4ec0-baf7-70593e151ab4 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation 86527e2d-2118-4ec4-a9c0-8561d2371051 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation e6371af1-cd62-4ea5-94df-cec2650b0b96 · outbound

This paper cites S., Correll, J.

StackingNet: Collective Inference Across Independent AI Foundation Models S., Correll, J

Reference 26

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Observation 62ba681f-3578-4318-ab69-f6f48a6c9534 · outbound

This paper cites Holistic evaluation of language models.

StackingNet: Collective Inference Across Independent AI Foundation Models Holistic evaluation of language models

Reference 27

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Observation 1beadbbb-a59f-4852-a7dc-bda0638ba8b0 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models A survey on evaluation of large language models

Reference 28

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Observation e95a773f-6ffd-443e-8da1-845730f4b317 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

StackingNet: Collective Inference Across Independent AI Foundation Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 29

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Observation 03cdc2db-d912-40f8-a781-e5f07907da8b · outbound

This paper cites GPT-4 Technical Report.

StackingNet: Collective Inference Across Independent AI Foundation Models GPT-4 Technical Report

Reference 30

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Observation 146aebb8-bda5-488f-a34a-bdfa48bb3d53 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Attacks on third-party APIs of large language models

Reference 31

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Observation 9d60d546-b9fd-43d3-95c6-7044c07175f8 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation 0ff2a73b-374c-4240-8993-9755dc8bed1c · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Ensemble Methods: F oundations and Algorithms (CRC Press, 2012)

Reference 33

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Observation bdc97df3-2031-4d71-a18e-e4d1461c465a · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Bagging predictors

Reference 34

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Observation 17e84b83-1486-45ca-b523-67d858487d92 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

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Observation a382b514-216c-4e96-b645-004787afe8da · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Suen, S

Reference 36

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Observation 7b7bcf6a-dc58-4d78-bd0c-025c699aae9e · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Grofman, B

Reference 37

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Observation e169d733-d2d0-404e-a146-8d946273c69a · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Kluger, Y

Reference 38

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Observation 5260f419-802d-4c5d-a6c8-7fd13055d515 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Ruvolo , P

Reference 39

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Observation ac6fc529-8087-46cf-9ea0-17527e843be2 · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Hovy, E

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Observation bd971f60-6379-4fa6-93e6-a2b19f9115fd · outbound

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StackingNet: Collective Inference Across Independent AI Foundation Models & Olshevsky, A

Reference 41

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StackingNet: Collective Inference Across Independent AI Foundation Models & Shah, D

Reference 42

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 44

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StackingNet: Collective Inference Across Independent AI Foundation Models Unresolved cited work

Reference 45

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StackingNet: Collective Inference Across Independent AI Foundation Models A lightweight, effective, and efficient model for label aggr egation in crowdsourcing

Reference 46

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StackingNet: Collective Inference Across Independent AI Foundation Models & Y u, Y

Reference 47

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StackingNet: Collective Inference Across Independent AI Foundation Models & Cohn, T

Reference 48

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Reference 49

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Reference 50

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Pith citing papers

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