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Paper Citation Record · LEDGER

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.03444.

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

pith.paper-citation-record.v1
2506.03444 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

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measured 33 of 33 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 1519a35d-ee83-4447-a436-09fd75c3ccdb · outbound

This paper cites AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

Reference 1

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Observation 4545f5c6-9cc5-43d3-8b2c-e27cf42cd131 · outbound

This paper cites Data polygamy: The many-many relationships among urban spatio-temporal data sets.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Data polygamy: The many-many relationships among urban spatio-temporal data sets

Reference 2

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Observation 5455087d-2fb9-4e0b-916e-ece31bcb3be4 · outbound

This paper cites LMPriors: Pre-Trained Language Models as Task-Specific Priors.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior LMPriors: Pre-Trained Language Models as Task-Specific Priors

Reference 3

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Observation 129c3d47-ac7a-4653-8ecb-156077ae3f78 · outbound

This paper cites Exploratory data analysis.Secondary analysis of electronic health records, pages 185–203, 2016.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Exploratory data analysis.Secondary analysis of electronic health records, pages 185–203, 2016

Reference 4

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Observation 27f529ad-e93e-4fe7-9c50-17ebba513e68 · outbound

This paper cites Aleatory or epistemic? does it matter?Structural Safety, 31(2):105–112, 2009.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Aleatory or epistemic? does it matter?Structural Safety, 31(2):105–112, 2009

Reference 5

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 6

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Observation c73e2483-6699-44c4-be99-efe91b2e1b25 · outbound

This paper cites Riding tandem: Does cycling infrastructure investment mirror gentrification and privilege in portland, or and chicago, il? Research in Transportation Economics, 60:14–24, 2016.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Riding tandem: Does cycling infrastructure investment mirror gentrification and privilege in portland, or and chicago, il? Research in Transportation Economics, 60:14–24, 2016

Reference 7

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Observation 92b695e9-110b-42e9-8eb4-01efae95338c · outbound

This paper cites Nexus: Correlation discovery over collections of spatio-temporal tabular data.Proc.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Nexus: Correlation discovery over collections of spatio-temporal tabular data.Proc

Reference 8

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This paper cites Shelf: the sheffield elicitation framework.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Shelf: the sheffield elicitation framework

Reference 9

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Observation f0ea8bc0-b579-413b-8795-5bd807b52f75 · outbound

This paper cites Automated prior elicitation from large language models for bayesian logistic regression.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Automated prior elicitation from large language models for bayesian logistic regression

Reference 10

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Observation ed202021-b835-4ccf-b264-0e6f974ed61f · outbound

This paper cites Big data: A revolution that will transform how we live, work, and think, 2014.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Big data: A revolution that will transform how we live, work, and think, 2014

Reference 11

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Observation 27bfbecb-be4f-4fe5-84b7-11eede560333 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision? InNeurIPS, 2017.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior What uncertainties do we need in bayesian deep learning for computer vision? InNeurIPS, 2017

Reference 12

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This paper cites Biograph: unsupervised biomedical knowledge discovery via automated hypothesis generation.Genome biology, 12:1–12, 2011.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Biograph: unsupervised biomedical knowledge discovery via automated hypothesis generation.Genome biology, 12:1–12, 2011

Reference 13

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 14

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This paper cites Distinguishing cause from effect using observational data: methods and benchmarks.Journal of Machine Learning Research, 17(32):1–102, 2016.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Distinguishing cause from effect using observational data: methods and benchmarks.Journal of Machine Learning Research, 17(32):1–102, 2016

Reference 15

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This paper cites John Wiley & Sons, 2006.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior John Wiley & Sons, 2006

Reference 16

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Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Unresolved cited work

Reference 17

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This paper cites Language Models as Knowledge Bases?.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Language Models as Knowledge Bases?

Reference 18

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This paper cites Chicago data portal, 2025.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Chicago data portal, 2025

Reference 19

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This paper cites Llm processes: Numerical predictive distributions conditioned on natural language.Advances in Neural Information Processing Systems, 37:109609–109671, 2024.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Llm processes: Numerical predictive distributions conditioned on natural language.Advances in Neural Information Processing Systems, 37:109609–109671, 2024

Reference 20

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This paper cites Correla- tion sketches for approximate join-correlation queries.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Correla- tion sketches for approximate join-correlation queries

Reference 21

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This paper cites Efficiently estimating mutual information between attributes across tables.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Efficiently estimating mutual information between attributes across tables

Reference 22

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Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior John Wiley & Sons, 2015

Reference 23

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This paper cites A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948

Reference 24

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This paper cites Directlingam: A direct method for learning a linear non-gaussian structural equation model.Journal of Machine Learning Research-JMLR, 12(Apr):1225–1248, 2011.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Directlingam: A direct method for learning a linear non-gaussian structural equation model.Journal of Machine Learning Research-JMLR, 12(Apr):1225–1248, 2011

Reference 25

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This paper cites Routledge, 2018.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Routledge, 2018

Reference 26

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This paper cites Large language models encode clinical knowledge.Nature, 620(7972):172–180, 2023.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Large language models encode clinical knowledge.Nature, 620(7972):172–180, 2023

Reference 27

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This paper cites Statistical methods, 8thedn.Ames: Iowa State Univ.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Statistical methods, 8thedn.Ames: Iowa State Univ

Reference 28

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This paper cites Wilkins, Benjamin J.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Wilkins, Benjamin J

Reference 29

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This paper cites Can large language models predict data correlations from column names? Proc.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Can large language models predict data correlations from column names? Proc

Reference 30

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Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Improving Scientific Hypothesis Generation with Knowledge Grounded Large Language Models

Reference 31

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This paper cites Large language models for scientific discovery in molecular property prediction.

Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior Large language models for scientific discovery in molecular property prediction

Reference 32

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Exploiting LLMs for Automatic Hypothesis Assessment via a Logit-Based Calibrated Prior coefficient

Reference 33

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Pith citing papers

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