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Paper Citation Record · LEDGER

Perspectives on Tsallis Statistics for Artificial Intelligence

As of 20 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2608.01223.

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

pith.paper-citation-record.v1
2608.01223 v1

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Reference resolution

59 of 59 outbound references displayed

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Outbound references

Observation db8c61e3-3734-4bfb-beaa-9bf2415b993a · outbound

This paper cites Tsallis, Possible generalization of Boltzmann–Gibbs statistics, Journal of Statistical Physics 52 (1988) 479–487.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, Possible generalization of Boltzmann–Gibbs statistics, Journal of Statistical Physics 52 (1988) 479–487

Reference 1

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 2

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Observation a72cf68e-e736-485c-9344-e2c5871e55ce · outbound

This paper cites Entropy measures and their applications: A comprehensive review.

Perspectives on Tsallis Statistics for Artificial Intelligence Entropy measures and their applications: A comprehensive review

Reference 3

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Observation 84b2e5bd-6ae0-4750-8e20-f297742fa126 · outbound

This paper cites Tsallis, Introduction to Nonextensive Statistical Mechanics: Ap- proaching a Complex World, Springer, New York, 2009.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, Introduction to Nonextensive Statistical Mechanics: Ap- proaching a Complex World, Springer, New York, 2009

Reference 4

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Observation feba4b6c-a055-49ce-996c-565f5ae714c4 · outbound

This paper cites Naudts, Generalised Thermostatistics, Springer, London, 2011.

Perspectives on Tsallis Statistics for Artificial Intelligence Naudts, Generalised Thermostatistics, Springer, London, 2011

Reference 5

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 6

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This paper cites Gell-Mann, C.

Perspectives on Tsallis Statistics for Artificial Intelligence Gell-Mann, C

Reference 7

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Observation c9d34bac-99fb-4992-961d-d05abe30a15a · outbound

This paper cites Tsallis, Beyond Boltzmann–Gibbs–Shannon in physics and elsewhere, Entropy 21 (2019) 696.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, Beyond Boltzmann–Gibbs–Shannon in physics and elsewhere, Entropy 21 (2019) 696

Reference 8

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This paper cites Penrose, Foundations of Statistical Mechanics: A Deductive Treat- ment, Pergamon Press, Oxford, 1970.

Perspectives on Tsallis Statistics for Artificial Intelligence Penrose, Foundations of Statistical Mechanics: A Deductive Treat- ment, Pergamon Press, Oxford, 1970

Reference 9

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Observation 560e194e-af02-4f9f-8fc8-f5fbb9b2e459 · outbound

This paper cites Furuichi, Information theoretical properties of Tsallis entropies, Jour- nal of Mathematical Physics 47 (2006) 023302.

Perspectives on Tsallis Statistics for Artificial Intelligence Furuichi, Information theoretical properties of Tsallis entropies, Jour- nal of Mathematical Physics 47 (2006) 023302

Reference 10

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Observation 0b321bc6-2ecd-4ecf-a664-62798bf225bb · outbound

This paper cites Tsallis, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, R

Reference 11

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Observation a69c35da-6166-4468-8aab-932a0fed45dd · outbound

This paper cites Nielsen, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Nielsen, R

Reference 12

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This paper cites Hanel, S.

Perspectives on Tsallis Statistics for Artificial Intelligence Hanel, S

Reference 13

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

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Perspectives on Tsallis Statistics for Artificial Intelligence Umarov, C

Reference 15

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 16

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This paper cites Prato, C.

Perspectives on Tsallis Statistics for Artificial Intelligence Prato, C

Reference 17

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Perspectives on Tsallis Statistics for Artificial Intelligence Peters, V

Reference 20

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Perspectives on Tsallis Statistics for Artificial Intelligence Blondel, A

Reference 22

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Perspectives on Tsallis Statistics for Artificial Intelligence Sparse and Continuous Attention Mechanisms

Reference 23

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Perspectives on Tsallis Statistics for Artificial Intelligence Sparse Continuous Distributions and Fenchel-Young Losses

Reference 24

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Perspectives on Tsallis Statistics for Artificial Intelligence Gonçalves, M

Reference 25

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Perspectives on Tsallis Statistics for Artificial Intelligence Vasylenko, H

Reference 26

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Perspectives on Tsallis Statistics for Artificial Intelligence Haarnoja, A

Reference 27

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Reference 28

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Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning

Reference 29

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Reference 30

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Perspectives on Tsallis Statistics for Artificial Intelligence A Theory of Regularized Markov Decision Processes

Reference 31

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Perspectives on Tsallis Statistics for Artificial Intelligence Munchausen Reinforcement Learning

Reference 32

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Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits

Reference 33

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Perspectives on Tsallis Statistics for Artificial Intelligence Zhang, S

Reference 34

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Perspectives on Tsallis Statistics for Artificial Intelligence Predicting Attention Sparsity in Transformers

Reference 35

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Perspectives on Tsallis Statistics for Artificial Intelligence Sparse Graph Attention Networks

Reference 36

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Observation b5f568c6-942a-46b7-b9a3-389a18881094 · outbound

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Perspectives on Tsallis Statistics for Artificial Intelligence Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Reference 37

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Observation 693170bc-9347-44aa-be91-9e58663ce955 · outbound

This paper cites an unresolved cited work.

Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 38

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raw_fallback, observed 2026-08-06T00:33:25.065521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.014747Z digest=sha256:75849936bc7f72af838e4c0f282fbd9c3fbbd59c3764d2c8cf6abaa8b8455d93

Observation ba1efa52-8213-4440-85cf-2de89efdb523 · outbound

This paper cites Takahashi, T.

Perspectives on Tsallis Statistics for Artificial Intelligence Takahashi, T

Reference 39

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malformed identifier
no resolver link, observed 2026-08-06T00:33:18.055086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.055086Z digest=sha256:e044a83de00bf77c7d9d06606d8e13f9cf83a64cb3d552fe4ff853589313a3b1

Observation e1fe679a-dd15-455d-a896-2536951e468c · outbound

This paper cites Star-Shaped Denoising Diffusion Probabilistic Models.

Perspectives on Tsallis Statistics for Artificial Intelligence Star-Shaped Denoising Diffusion Probabilistic Models

Reference 40

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.365698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.094746Z digest=sha256:7de0d50d1e1cfdecf09cf7520e38d66bbb5f2fb4ad3f016fa4000f35d545762f

Observation e78e911a-1f8d-40eb-b81c-359ba12563a7 · outbound

This paper cites Heavy-Tailed Diffusion Models.

Perspectives on Tsallis Statistics for Artificial Intelligence Heavy-Tailed Diffusion Models

Reference 41

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unresolved
no resolver link, observed 2026-08-06T00:33:18.144751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.144751Z digest=sha256:c15f74226739368e1c15d0e222c432d052016d818521fa8d74c38b29a353f96d

Observation a30a92e3-40fe-461c-b85d-e0a4d579f3a2 · outbound

This paper cites Heavy-Tailed Diffusion with Denoising L\'evy Probabilistic Models.

Perspectives on Tsallis Statistics for Artificial Intelligence Heavy-Tailed Diffusion with Denoising L\'evy Probabilistic Models

Reference 42

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.324152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.162553Z digest=sha256:52b69241701325ffe81b18f19da99d8af6859b08097592788f3c6cad385c3f0e

Observation 7be93606-f979-4bd9-8d49-ed6d652fa2aa · outbound

This paper cites van der Maaten, G.

Perspectives on Tsallis Statistics for Artificial Intelligence van der Maaten, G

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-06T00:33:24.790634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.175368Z digest=sha256:98a5c38a71f7dbbc934ef067de27684719755d45c8ed275c5fa9159e0dea8943

Observation 2df78168-a4ff-4125-8f72-dd6bde5c25fb · outbound

This paper cites Zhang, M.

Perspectives on Tsallis Statistics for Artificial Intelligence Zhang, M

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-06T00:33:24.484756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.186906Z digest=sha256:d38c5f74a930bdbd4af0656be427a70f8810ec73cd9c7538f521f3cbff65e7d7

Observation 9c2ca8b8-6d57-477a-8efd-a9b7b3bf4ebb · outbound

This paper cites Two-temperature logistic regression based on the Tsallis divergence.

Perspectives on Tsallis Statistics for Artificial Intelligence Two-temperature logistic regression based on the Tsallis divergence

Reference 45

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.288694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.215835Z digest=sha256:5ad1113096c17a83efea455ebcbeca4542edb5e18462392633e74c49a92c9aff

Observation 70d28c16-3929-472e-8fff-2a26cbf897df · outbound

This paper cites Robust Bi-Tempered Logistic Loss Based on Bregman Divergences.

Perspectives on Tsallis Statistics for Artificial Intelligence Robust Bi-Tempered Logistic Loss Based on Bregman Divergences

Reference 46

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.228332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.232705Z digest=sha256:aa12c9d13a152ccba53b272540b79b3fa6fd90a78479adf64e753edeec5bbe24

Observation e886a9b3-632a-40f7-be40-edfdc96a5112 · outbound

This paper cites Tsallis, D.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, D

Reference 47

Resolution
verified exact
doi, observed 2026-08-06T00:33:19.054763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.236249Z digest=sha256:69cfc649242db56f754522ce1743e5c6bff3e3f5bfd0ca9d91d6cac3784f61b9

Observation 1731f578-7e0b-4e2a-828e-1ffb90bb9e5f · outbound

This paper cites The q-gradient method for global optimization.

Perspectives on Tsallis Statistics for Artificial Intelligence The q-gradient method for global optimization

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:33:20.114750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.274744Z digest=sha256:3bc99d5fcd6764917f6c74f29c8c2157c35667266bdc697f441261b99a2a14f6

Observation f5ad3b19-7341-4f56-a864-f26b9dc1b0c6 · outbound

This paper cites an unresolved cited work.

Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-06T00:33:24.164754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.314842Z digest=sha256:4d09dd94e8dfd92546946e94dddfb2de1b2f934e99e537eabc7cf6302d0159d6

Observation cff575e7-54cb-4149-93f8-ebb94254f8f1 · outbound

This paper cites Şimşekli, L.

Perspectives on Tsallis Statistics for Artificial Intelligence Şimşekli, L

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-06T00:33:23.804032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.374752Z digest=sha256:11307c2f771b911f6c1c8f91e31324de4d8d8c72cd26ffc876220687f64929e0

Observation ec3d0365-8012-44ca-a753-86b354271d46 · outbound

This paper cites The Heavy-Tail Phenomenon in SGD.

Perspectives on Tsallis Statistics for Artificial Intelligence The Heavy-Tail Phenomenon in SGD

Reference 51

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.044752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.414831Z digest=sha256:1f15b764f4fc67ca4a086fcef9b13609e3189478b54056ffe13d346611c31ee8

Observation 2df48e7d-70d9-4c6f-aca7-32b3fc4a2ae0 · outbound

This paper cites Multiplicative noise and heavy tails in stochastic optimization.

Perspectives on Tsallis Statistics for Artificial Intelligence Multiplicative noise and heavy tails in stochastic optimization

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T00:33:18.454829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.454829Z digest=sha256:d2607d3a72328c967ed5ce5e47fd790e6b3b02510904c0bacf4c8ca6ba72d581

Observation 73a61d7f-1348-4bff-8101-0779609e941c · outbound

This paper cites Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks.

Perspectives on Tsallis Statistics for Artificial Intelligence Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:33:19.871401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.484832Z digest=sha256:f0f6e80596821a4ac29bf9664d3c3fff00585c0004a61518f168494430b0676b

Observation baea902a-07eb-4d84-bddc-4185221bfbc5 · outbound

This paper cites Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks.

Perspectives on Tsallis Statistics for Artificial Intelligence Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:33:19.808401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.504752Z digest=sha256:2c232bc0e7ad4d5301596b910c656386183731c2b8d8ccb7509e271be65ac9e4

Observation d4855ecc-eccf-4605-bce6-6e989a0a2545 · outbound

This paper cites Amari, Information Geometry and Its Applications, volume 194 ofApplied Mathematical Sciences, Springer, 2016.

Perspectives on Tsallis Statistics for Artificial Intelligence Amari, Information Geometry and Its Applications, volume 194 ofApplied Mathematical Sciences, Springer, 2016

Reference 55

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no resolver link, observed 2026-08-06T00:33:18.532149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.532149Z digest=sha256:594afc4ea5cee93c3d10fc2d4dd8157aa2c546d13853e1e2e2eed4dd16599128

Observation 557e15ca-7506-4dec-b2c3-96f1b3237c6a · outbound

This paper cites Amari, A.

Perspectives on Tsallis Statistics for Artificial Intelligence Amari, A

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T00:33:18.564756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.564756Z digest=sha256:160de4413426c31e3501ce9f0ca3eb877d8f859ece19bbf9a11d86242e472dde

Observation 34f1bc68-22d0-4da0-9a98-06342ba57cf3 · outbound

This paper cites Korbel, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Korbel, R

Reference 57

Resolution
verified exact
doi, observed 2026-08-06T00:33:18.724905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.583334Z digest=sha256:1b70a9eb1bd56b5d3a5901daa30d496319663d13772ba4a19304ceab27914998

Observation 2ce07f68-61b3-48da-a2d0-514596e74372 · outbound

This paper cites w_key"]) @ params[.

Perspectives on Tsallis Statistics for Artificial Intelligence w_key"]) @ params[

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:33:23.474271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T00:33:18.591792Z digest=sha256:b0db06bedea9e60f3aed7ad260a19611c7b79b648bcc913bd6c5d78c6706a606

Observation 2cb0968c-b162-4856-bb1f-8958827466ae · outbound

This paper cites Why are Adaptive Methods Good for Attention Models?.

Perspectives on Tsallis Statistics for Artificial Intelligence Why are Adaptive Methods Good for Attention Models?

Reference 2020

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unresolved
no resolver link, observed 2026-08-06T00:33:17.824749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:17.824749Z digest=sha256:502d7cacd1ab2ea2e7ac9783ba53fd96f5166e3023dfaa6aefc4991270f749b0

Pith citing papers

No inbound Pith citation observations are available.