Paper Citation Record · LEDGER

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2505.07883.

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

pith.paper-citation-record.v1
2505.07883 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:41:50.907910Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

42 of 42 outbound references displayed

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verified fuzzy12
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External citation measurements

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Outbound references

Observation 16a90c72-9def-48e5-96bf-3646f17de138 · outbound

This paper cites Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.714164Z

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Observation ec9528fc-9f5f-44e8-9b8b-76f8b6020523 · outbound

This paper cites Deep learning of representations: Looking forward.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Deep learning of representations: Looking forward

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.517963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b66fc586-7332-4a9e-b72d-c695f292e13a · outbound

This paper cites Using cognitive psychology to understand gpt-3.Proceedings of the National Academy of Sciences, 120(6):e2218523120, 2023.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Using cognitive psychology to understand gpt-3.Proceedings of the National Academy of Sciences, 120(6):e2218523120, 2023

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.724723Z

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Observation 1a67009e-afc0-4b94-9e35-3417fcd244a6 · outbound

This paper cites An Interpretability Illusion for BERT.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints An Interpretability Illusion for BERT

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.729699Z

Source-reported events for the cited work

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Observation 7cf96e5d-1da0-40a8-8cda-0a47f4b16fc6 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.734775Z

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Observation fdfc84cb-913a-4b77-8b91-e61a83f0f890 · outbound

This paper cites Understanding disentangling in $\beta$-VAE.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Understanding disentangling in $\beta$-VAE

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.739519Z

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Observation 91632720-02a0-40ad-9c04-21924761d399 · outbound

This paper cites Isolating sources of disentanglement in variational autoencoders.Advances in neural information processing systems, 31, 2018.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Isolating sources of disentanglement in variational autoencoders.Advances in neural information processing systems, 31, 2018

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.481248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7cce7327-6e9e-4e62-be98-02df5df0f10e · outbound

This paper cites Group equivariant convolutional networks.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Group equivariant convolutional networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.749591Z

Source-reported events for the cited work

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Observation 64424a4f-93b0-45c7-9862-3970cca0ff45 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.753877Z

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Observation 3a8519eb-c283-462f-9d9d-4a69bfed5081 · outbound

This paper cites Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.758598Z

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Observation 9aa35b10-6b92-4edf-87fc-fdff43965894 · outbound

This paper cites The Llama 3 Herd of Models.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints The Llama 3 Herd of Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.763595Z

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Observation 029ba942-cfee-464d-ad3e-8025f643e779 · outbound

This paper cites Toy Models of Superposition.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Toy Models of Superposition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.768076Z

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Observation 2f29237b-9b8e-4102-9c9f-b88b91e3805d · outbound

This paper cites A mathematical framework for transformer circuits.Transformer Circuits Thread, 1(1):12, 2021.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints A mathematical framework for transformer circuits.Transformer Circuits Thread, 1(1):12, 2021

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.772525Z

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Observation 1a1db8d1-15b0-4114-93a5-9302f2b6d9d3 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Gemma: Open Models Based on Gemini Research and Technology

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.776900Z

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Observation 6e0eccc0-03b3-4ecf-a9a7-f4311788d0df · outbound

This paper cites On calibration of modern neural networks.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints On calibration of modern neural networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.781533Z

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Observation 08284514-5534-4b73-99dc-445649be6fe6 · outbound

This paper cites Finding Neurons in a Haystack: Case Studies with Sparse Probing.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.785870Z

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Observation 453fa5c3-866c-4e80-b8ab-f21dd456ca23 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Gaussian Error Linear Units (GELUs)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.790449Z

Source-reported events for the cited work

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Observation 4364d622-aa55-4e8a-84ba-f11df918cac7 · outbound

This paper cites beta-V AE: Learning basic visual concepts with a constrained variational framework.International Conference on Learning Representations, 3, 2017.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints beta-V AE: Learning basic visual concepts with a constrained variational framework.International Conference on Learning Representations, 3, 2017

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.434656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 42146d0a-2b0e-4afc-b6f2-3ae2dedbfe79 · outbound

This paper cites Large language models as simulated economic agents: What can we learn from homo silicus? Technical report, National Bureau of Economic Research, 2023.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Large language models as simulated economic agents: What can we learn from homo silicus? Technical report, National Bureau of Economic Research, 2023

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.799454Z

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Observation 5cc73f31-b565-478a-9641-b92c421acfae · outbound

This paper cites Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning.Patterns, 4(10), 2023.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning.Patterns, 4(10), 2023

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.804433Z

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Observation 8d297e32-234b-405b-9c0e-2d9d3edb530c · outbound

This paper cites Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.809131Z

Source-reported events for the cited work

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Observation 690fdbcb-b3b2-4537-bd22-0cf90a949857 · outbound

This paper cites Disentangling by factorising.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Disentangling by factorising

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.814089Z

Source-reported events for the cited work

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Observation 305900a9-123d-4f4a-abc6-b630678713cc · outbound

This paper cites Kingma and Max Welling.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Kingma and Max Welling

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.388793Z

Source-reported events for the cited work

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Observation c3791942-7c31-44f5-a55f-8b3d386bba25 · outbound

This paper cites Courier Dover Publications, 2018.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Courier Dover Publications, 2018

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.373500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d26dee81-906d-416f-8709-ee18e1711fa9 · outbound

This paper cites Verified uncertainty calibration.Advances in Neural Information Processing Systems, 32, 2019.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Verified uncertainty calibration.Advances in Neural Information Processing Systems, 32, 2019

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.357600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9edc394e-642b-4b8e-b2a8-6c9a36d55d69 · outbound

This paper cites Large Language Models Assume People are More Rational than We Really are.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Large Language Models Assume People are More Rational than We Really are

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.832161Z

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Observation 111c8037-2ad1-45a2-b6c4-d2dea40e7ca8 · outbound

This paper cites Decoupled Weight Decay Regularization.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Decoupled Weight Decay Regularization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.837203Z

Source-reported events for the cited work

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Observation 1b954b9f-6d8a-4fbb-802d-a6c9f8b57e9d · outbound

This paper cites Disentangling disentan- glement in variational autoencoders.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Disentangling disentan- glement in variational autoencoders

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.342476Z

Source-reported events for the cited work

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Observation e7567f25-733b-450c-b103-cd268e65ab3e · outbound

This paper cites Sparse autoencoder.CS294A Lecture notes, 72(2011):1–19, 2011.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Sparse autoencoder.CS294A Lecture notes, 72(2011):1–19, 2011

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.846248Z

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Observation c8676643-b14c-475a-ba74-4ba25715dd0d · outbound

This paper cites Oxford University Press, 2016.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Oxford University Press, 2016

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.317092Z

Source-reported events for the cited work

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Observation 7f21b72d-67ec-4c3d-a1ae-2dca96501947 · outbound

This paper cites Cambridge University Press, 2020.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Cambridge University Press, 2020

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.301859Z

Source-reported events for the cited work

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Observation 1e12a7a1-ed69-453c-84f9-53e4563cdc14 · outbound

This paper cites STEER: Assessing the Economic Rationality of Large Language Models.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints STEER: Assessing the Economic Rationality of Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.860234Z

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Observation 8f82cdd9-918e-433e-b114-a39c971a8bc6 · outbound

This paper cites LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.865109Z

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Observation db879c97-fb28-4273-ba7b-119b175ea0b1 · outbound

This paper cites Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.871546Z

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Observation a4c0d51f-4811-4d80-ad4e-e7357e28e3cd · outbound

This paper cites Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.286355Z

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Observation e5fad3e7-c2d3-497b-9c2a-8d904d0f4aea · outbound

This paper cites Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.880976Z

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Observation 57a7a4cc-b903-42ee-bfcb-4c248d6deb4f · outbound

This paper cites Benchmarking LLMs via Uncertainty Quantification.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Benchmarking LLMs via Uncertainty Quantification

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.885772Z

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Observation b800f7a2-af13-4aa8-bf50-6cc5105bffe0 · outbound

This paper cites Deep sets.Advances in neural information processing systems, 30, 2017.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Deep sets.Advances in neural information processing systems, 30, 2017

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.890322Z

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Observation c1666094-f452-42d2-aa6d-562c8f810b87 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.Advances in Neural Information Processing Systems, 35:15476–15488, 2022.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Star: Bootstrapping reasoning with reasoning.Advances in Neural Information Processing Systems, 35:15476–15488, 2022

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.894781Z

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Observation adbb92fb-9df3-43b8-8591-b540e5e83d19 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T22:41:50.899132Z

Source-reported events for the cited work

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Observation 91da683d-a3d7-4d47-8a47-0ac0572bb267 · outbound

This paper cites Incoherent probability judgments in large language models.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Incoherent probability judgments in large language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.242267Z

Source-reported events for the cited work

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Observation 9e6b385f-042a-4b76-897f-757e631b49d3 · outbound

This paper cites <or≤”, “=.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints <or≤”, “=

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:41:51.226030Z

Source-reported events for the cited work

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Pith citing papers

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