Paper Citation Record · LEDGER

Towards Strong AI: Transformational Beliefs and Scientific Creativity

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2412.19938.

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

pith.paper-citation-record.v1
2412.19938 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:53:04.015151Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:58:32.606060Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T14:58:33.176736Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy38
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ebb43af-ea4d-4489-baf5-3213047bad94 · outbound

This paper cites , π(K) 1 and ϕ(1),.

Towards Strong AI: Transformational Beliefs and Scientific Creativity , π(K) 1 and ϕ(1),

Reference 1

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Source-reported events for the cited work

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Observation 18d45688-9ee8-4e5c-ba4d-9c4dcba44a62 · outbound

This paper cites , π(t) K , ϕ(t) 1 ,.

Towards Strong AI: Transformational Beliefs and Scientific Creativity , π(t) K , ϕ(t) 1 ,

Reference 2

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Source-reported events for the cited work

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Observation 904af373-bcde-49fa-b02f-2d7ab9ee581e · outbound

This paper cites , K, sample (π(t+1) 1 ,.

Towards Strong AI: Transformational Beliefs and Scientific Creativity , K, sample (π(t+1) 1 ,

Reference 3

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Observation 8c67b754-1510-4417-b8f2-34efcc868677 · outbound

This paper cites Would you like a deeper dive into any of these, or should we explore potential applications?.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Would you like a deeper dive into any of these, or should we explore potential applications?

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.438303Z

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Observation 2eed945f-8b33-437e-8f7b-fd51bc20ceb0 · outbound

This paper cites an unresolved cited work.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 5

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Observation 06d7add5-7dba-4396-976d-d14f596e90c4 · outbound

This paper cites Modern Logic and Science.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Modern Logic and Science

Reference 6

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Observation b3851a48-748f-4b32-b839-aaba4e351db5 · outbound

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Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 7

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Observation 833ea31d-487d-4bbe-bdbe-4fd86fe6c797 · outbound

This paper cites The Emergence of Formal Logic.

Towards Strong AI: Transformational Beliefs and Scientific Creativity The Emergence of Formal Logic

Reference 8

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Observation 6a74eda6-c6f3-4423-be8e-cd9097e38327 · outbound

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Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 9

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Observation 0812de1e-ce24-4a3d-9a22-787a06e1d590 · outbound

This paper cites 20th Century and Beyond 32.

Towards Strong AI: Transformational Beliefs and Scientific Creativity 20th Century and Beyond 32

Reference 10

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Observation d5a250d9-1bdf-4c92-98b2-9a80edde716e · outbound

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Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 11

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Observation 477e51d1-8f62-4904-9e56-bf95599b0bb0 · outbound

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Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 12

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Observation 0149c07b-6f23-40d4-bc14-17f59d8bb34f · outbound

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Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 13

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Observation c3f61100-eaa1-4faa-ba59-d149c5d08432 · outbound

This paper cites Scientists often begin with hypotheses (prior beliefs) and update these beliefs based on experimental data (evidence).

Towards Strong AI: Transformational Beliefs and Scientific Creativity Scientists often begin with hypotheses (prior beliefs) and update these beliefs based on experimental data (evidence)

Reference 14

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Observation 4a290638-106c-4fad-9a31-4ce8257719a4 · outbound

This paper cites Deduction: Using Bayesian models to derive consequences of a hypothesis.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Deduction: Using Bayesian models to derive consequences of a hypothesis

Reference 15

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Observation 94281086-b4ad-48be-a978-9d534d6cf50c · outbound

This paper cites For instance, Bayes factors allow scientists to evaluate the relative likelihood of competing theories.

Towards Strong AI: Transformational Beliefs and Scientific Creativity For instance, Bayes factors allow scientists to evaluate the relative likelihood of competing theories

Reference 16

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Source-reported events for the cited work

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Observation c4afec76-ae05-4008-8282-83474b6f0b03 · outbound

This paper cites As data accumulates, the posterior probability of a paradigm may shift, potentially triggering a paradigm shift when an alternative explanation becomes more plausible.

Towards Strong AI: Transformational Beliefs and Scientific Creativity As data accumulates, the posterior probability of a paradigm may shift, potentially triggering a paradigm shift when an alternative explanation becomes more plausible

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.319229Z

Source-reported events for the cited work

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Observation 21241a4e-3bfc-40e8-862b-b5b68e4eec5a · outbound

This paper cites This perspective emphasizes the probabilistic nature of scientific knowledge and its inherent uncertainty.

Towards Strong AI: Transformational Beliefs and Scientific Creativity This perspective emphasizes the probabilistic nature of scientific knowledge and its inherent uncertainty

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.311122Z

Source-reported events for the cited work

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Observation eaef3756-de86-445f-afc0-cf023f4cd3e0 · outbound

This paper cites Core Idea: Hypothesis testing and parameter estimation are based on long-run frequency properties of data.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Hypothesis testing and parameter estimation are based on long-run frequency properties of data

Reference 19

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Source-reported events for the cited work

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Observation b1e833b8-634b-4928-b6a5-6a0ab9b5ad18 · outbound

This paper cites Core Idea: Compare models or hypotheses directly based on how well they explain the observed data.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Compare models or hypotheses directly based on how well they explain the observed data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.294286Z

Source-reported events for the cited work

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Observation 0bf3d901-f86a-42d4-9035-19b5c741af94 · outbound

This paper cites Core Idea: Measure and compare the information content of competing models.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Measure and compare the information content of competing models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.285271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b30be8b2-1978-486b-8a09-3d0244f2b84c · outbound

This paper cites Carnap’s Logical Probability: An early attempt to formalize induction without strong priors, although later criticized for still requiring some subjectivity.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Carnap’s Logical Probability: An early attempt to formalize induction without strong priors, although later criticized for still requiring some subjectivity

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.276457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 28478c55-904f-4839-9648-c8947c5e88ce · outbound

This paper cites Core Idea: The prior is derived from the data itself, creating a hybrid between fre- quentist and Bayesian methods.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: The prior is derived from the data itself, creating a hybrid between fre- quentist and Bayesian methods

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.267020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b6d57283-fa1e-4ebd-8266-5ba63b3455e5 · outbound

This paper cites Symbolic Reasoning Systems: Use rule-based approaches to infer conclusions directly from observed patterns and logical relationships.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Symbolic Reasoning Systems: Use rule-based approaches to infer conclusions directly from observed patterns and logical relationships

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.257696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c4bddb6c-37f5-4510-ac35-d9ebc715d1d1 · outbound

This paper cites Core Idea: Science progresses by proposing bold hypotheses and subjecting them to rigorous testing.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Science progresses by proposing bold hypotheses and subjecting them to rigorous testing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.248869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6a21033-ade0-4f45-bb18-0d2e468cf230 · outbound

This paper cites Core Idea: These priors are derived mathematically from the Fisher information of the likelihood function and aim to reflect ignorance without subjective input.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: These priors are derived mathematically from the Fisher information of the likelihood function and aim to reflect ignorance without subjective input

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.239418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d88b7033-3efe-4306-99f6-867cf9d7078d · outbound

This paper cites Core Idea: They are constructed to maximize the divergence between the posterior and the prior, ensuring the posterior reflects information primarily from the data.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: They are constructed to maximize the divergence between the posterior and the prior, ensuring the posterior reflects information primarily from the data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.230516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1292e0a9-5d3b-4862-bcf9-8d172553c3b0 · outbound

This paper cites 37 Core Idea: The prior is informed by partitioning the data, using part of the data to estimate the prior and the rest to compute the posterior.

Towards Strong AI: Transformational Beliefs and Scientific Creativity 37 Core Idea: The prior is informed by partitioning the data, using part of the data to estimate the prior and the rest to compute the posterior

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.222064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b9d26df1-bcd6-4751-969d-68ce983a6cf6 · outbound

This paper cites Core Idea: Instead of specifying a prior, resampling weights are treated probabilis- tically, generating a posterior distribution for the parameter of interest.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Instead of specifying a prior, resampling weights are treated probabilis- tically, generating a posterior distribution for the parameter of interest

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.213063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 88a37200-4efd-40f7-bc68-429b278a6a04 · outbound

This paper cites While not technically ”probabilistic” in the strict sense, they often yield posteriors that are well-calibrated in a frequentist sense.

Towards Strong AI: Transformational Beliefs and Scientific Creativity While not technically ”probabilistic” in the strict sense, they often yield posteriors that are well-calibrated in a frequentist sense

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.204356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 632d2a79-787c-4b2f-9333-d5b8e580c248 · outbound

This paper cites Core Idea: Generate posterior-like distributions by normalizing likelihoods without explicitly defining a prior.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Generate posterior-like distributions by normalizing likelihoods without explicitly defining a prior

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.196062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89d62b66-19ca-4174-89ba-8c8b1f52938a · outbound

This paper cites 38 Core Idea: Modify the Bayesian updating process to prevent overconfidence, relying on likelihood-driven adjustments rather than subjective priors.

Towards Strong AI: Transformational Beliefs and Scientific Creativity 38 Core Idea: Modify the Bayesian updating process to prevent overconfidence, relying on likelihood-driven adjustments rather than subjective priors

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.187756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0b751a25-43c7-491e-8165-4ceb9246cc43 · outbound

This paper cites When extended to randomized confidence distributions, probabilistic assertions about hypotheses can achieve frequency calibration.

Towards Strong AI: Transformational Beliefs and Scientific Creativity When extended to randomized confidence distributions, probabilistic assertions about hypotheses can achieve frequency calibration

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.178570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 97ac1663-0c30-401d-8079-16cf47754021 · outbound

This paper cites Fisher, assigns a probabilistic distri- bution to a parameter based on the observed data, without requiring a prior.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Fisher, assigns a probabilistic distri- bution to a parameter based on the observed data, without requiring a prior

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.169441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9bba6567-dbcc-4f4f-adfd-27f31ba0c9ac · outbound

This paper cites Core Idea: Adjust priors, likelihoods, or posterior probabilities using calibration tech- niques to align with long-run frequency properties.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Adjust priors, likelihoods, or posterior probabilities using calibration tech- niques to align with long-run frequency properties

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.160293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c3534649-5880-45ab-ae42-91566f5a74cc · outbound

This paper cites They avoid priors but generate belief functions and plausibility functions that are probabilistically interpretable and frequency-calibrated.

Towards Strong AI: Transformational Beliefs and Scientific Creativity They avoid priors but generate belief functions and plausibility functions that are probabilistically interpretable and frequency-calibrated

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.151409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 67e41925-82bf-46f5-b4bd-93bec8dc7d4f · outbound

This paper cites Core Idea: Derive distributions based on sampling distributions and pivot quanti- ties that yield posterior-like intervals and probabilities.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Derive distributions based on sampling distributions and pivot quanti- ties that yield posterior-like intervals and probabilities

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.141653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d4daec3a-4bb8-4c82-bc1b-b8588e2e7db4 · outbound

This paper cites Core Idea: Modify the Bayesian framework to guarantee that posterior probabilities align with frequentist properties, such as coverage.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Core Idea: Modify the Bayesian framework to guarantee that posterior probabilities align with frequentist properties, such as coverage

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.131915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8e036d6d-8c0f-493a-8e3f-3dd519326a3a · outbound

This paper cites This step formalizes the connection between observ- able and unobservable quantities, similar to a likelihood function in Bayesian inference but avoids priors.

Towards Strong AI: Transformational Beliefs and Scientific Creativity This step formalizes the connection between observ- able and unobservable quantities, similar to a likelihood function in Bayesian inference but avoids priors

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.122660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cffbe029-1d0b-47b0-9d5b-a2fb057eafce · outbound

This paper cites Plausibility Function Pl( A): Quantifies the extent to which the data do not contradict A.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Plausibility Function Pl( A): Quantifies the extent to which the data do not contradict A

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.113359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e1730aa4-0afa-4fab-82fb-0dd6720c19d1 · outbound

This paper cites This is a fundamental prop- erty that many Bayesian methods lack due to their reliance on priors.

Towards Strong AI: Transformational Beliefs and Scientific Creativity This is a fundamental prop- erty that many Bayesian methods lack due to their reliance on priors

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.104097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ed0fe6db-4474-410a-a1c2-5823b7b4a4c9 · outbound

This paper cites Probabilistic statements are inherently ob- jective and calibrated, addressing long-standing criticisms of subjectivity in Bayesian inference.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Probabilistic statements are inherently ob- jective and calibrated, addressing long-standing criticisms of subjectivity in Bayesian inference

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.094121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f58c23e-c091-446a-9bd6-88b81a91e71d · outbound

This paper cites an unresolved cited work.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:53:04.084812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 659c72bd-186c-40eb-b0d3-1fe19820da6a · outbound

This paper cites an unresolved cited work.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:53:04.075775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 31903aa2-e1ba-4f13-96ec-efe4b0ace5b7 · outbound

This paper cites Complex models with auxiliary variables.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Complex models with auxiliary variables

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.066947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b6028fb-094f-439c-a10a-2d81f041bef8 · outbound

This paper cites Guaranteeing frequentist-calibrated error control.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Guaranteeing frequentist-calibrated error control

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:53:04.057336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6d1a9f93-f3d1-4abb-aa15-557fe3675962 · outbound

This paper cites an unresolved cited work.

Towards Strong AI: Transformational Beliefs and Scientific Creativity Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-10T23:53:04.045354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 4ffbc347-4d89-4239-8e12-479ec727f5d7 · inbound

The typicality principle and its implications for statistics and data science cites this paper.

The typicality principle and its implications for statistics and data science Towards Strong AI: Transformational Beliefs and Scientific Creativity

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:58:33.181712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.