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Paper Citation Record · LEDGER

Laguerre Geometry for Interpreting Large Language Models

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2607.10578.

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

pith.paper-citation-record.v1
2607.10578 v1

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measured 42 of 42 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-14T10:42:30.055074Z

measured 42 of 42 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

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Reference resolution

42 of 42 outbound references displayed

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Outbound references

Observation 71fb587f-81b4-4067-86df-ec5ff9513f8c · outbound

This paper cites The geometry of thought: Disclosing the transformer as a tropical polynomial circuit.

Laguerre Geometry for Interpreting Large Language Models The geometry of thought: Disclosing the transformer as a tropical polynomial circuit

Reference 1

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Observation eab40d66-5c45-485c-8140-2157cce25435 · outbound

This paper cites Understanding Deep Neural Networks with Rectified Linear Units.

Laguerre Geometry for Interpreting Large Language Models Understanding Deep Neural Networks with Rectified Linear Units

Reference 2

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:bfb2fc46c0c23d8acefb4a22a51102de34c15916e9ef58677a43f9894a56fc24

Observation 151ec53f-cd19-4cdb-8792-9b85cb507ddb · outbound

This paper cites Layer Normalization.

Laguerre Geometry for Interpreting Large Language Models Layer Normalization

Reference 3

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Observation ac716c7e-953f-472d-95b2-322c64a0baa4 · outbound

This paper cites Representation Alignment Rests on Linear Structure.

Laguerre Geometry for Interpreting Large Language Models Representation Alignment Rests on Linear Structure

Reference 4

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:5ba771cb81aaf6dcf59bf51f538dc584d4072a7a4bc5550596f828d2d10bb7b2

Observation ca618610-d43d-4d23-a648-ecf675f692d7 · outbound

This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

Laguerre Geometry for Interpreting Large Language Models Eliciting Latent Predictions from Transformers with the Tuned Lens

Reference 5

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:55516c7d15c4886b18278976c86bf7a4441930b0c51c1df8c5c43285e5b1e9af

Observation e15aa203-f0c1-4b61-a00d-3243b6c02e96 · outbound

This paper cites Temporal sparse autoencoders: Leveraging the sequential nature of language for interpretability.arXiv preprint arXiv:2511.05541,.

Laguerre Geometry for Interpreting Large Language Models Temporal sparse autoencoders: Leveraging the sequential nature of language for interpretability.arXiv preprint arXiv:2511.05541,

Reference 6

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Observation 67906b4e-c84e-4c7e-a5b6-615e9e47ad26 · outbound

This paper cites Do Sparse Autoencoders Capture Concept Manifolds?.

Laguerre Geometry for Interpreting Large Language Models Do Sparse Autoencoders Capture Concept Manifolds?

Reference 7

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:fecaa2acf2eabb0bad9a16b1cce4a0a44a3e7982f1dea1a990b81ec03262ec63

Observation 925d2e04-d986-48fd-a149-d2fa3a3f3d1b · outbound

This paper cites Belief dynamics reveal the dual nature of in-context learning and activation steering.arXiv preprint arXiv:2511.00617,.

Laguerre Geometry for Interpreting Large Language Models Belief dynamics reveal the dual nature of in-context learning and activation steering.arXiv preprint arXiv:2511.00617,

Reference 8

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Observation 7a3a95a4-e408-4ede-bc01-400c88d83542 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Laguerre Geometry for Interpreting Large Language Models Evaluating Large Language Models Trained on Code

Reference 9

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Observation 3f277156-acb1-4f4b-a717-475b363e0322 · outbound

This paper cites Toy Models of Superposition.

Laguerre Geometry for Interpreting Large Language Models Toy Models of Superposition

Reference 10

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:916b991904250226e9bff9b8b2d2313475a8e0130e23de5e2f214a3ead349d5f

Observation 5ab52fad-374f-4a5e-ae90-7b39cf05eed5 · outbound

This paper cites Not all language model features are one- dimensionally linear.

Laguerre Geometry for Interpreting Large Language Models Not all language model features are one- dimensionally linear

Reference 11

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:a917330e488f6a2b587fdac38d2b51c3a724ce37884b0cfb9d5df40c6ce8f823

Observation 308e08c6-a0f2-4997-9d17-4c33fd736468 · outbound

This paper cites Characterizing the Discrete Geometry of ReLU Networks.

Laguerre Geometry for Interpreting Large Language Models Characterizing the Discrete Geometry of ReLU Networks

Reference 12

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:9f0136fe1cabf2d9e5a68e754d2601a39b0e7992bf0cb740246f753ba153ff07

Observation aefb2b5e-3169-4aad-b5f1-426ed508f0d3 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Laguerre Geometry for Interpreting Large Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 13

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:e51ffe3717d8b2d32256853dc08d2c24aee1c59ab7e84accc509d5f2806ba1dc

Observation 9e242e43-d075-406a-8786-dea8b52f295c · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Laguerre Geometry for Interpreting Large Language Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 14

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:bf96f88b74dda9be2c5ac279d0c7a21e19d373fa48ab20406ca44479cde991de

Observation cd060afd-64f6-4caf-ab63-9572dde5ef20 · outbound

This paper cites Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models.

Laguerre Geometry for Interpreting Large Language Models Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models

Reference 15

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:fd6c86bdc24884708c16c1c9a07bfbe56f2ba2471f3318fdcce4b650c1ef0e34

Observation 87b65419-f9b8-497c-9144-96767ab4251d · outbound

This paper cites Intricacies of feature geometry in large language models.

Laguerre Geometry for Interpreting Large Language Models Intricacies of feature geometry in large language models

Reference 16

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:13cce70cae6811f4ec9c77f8440e9d37065e3dd5fc6408462cc7518c9696ddf2

Observation e7c0d2e6-f540-4b46-bbb8-089ab83cf1ad · outbound

This paper cites When models manipulate manifolds: The geometry of a counting task.arXiv preprint arXiv:2601.04480, 2026a.

Laguerre Geometry for Interpreting Large Language Models When models manipulate manifolds: The geometry of a counting task.arXiv preprint arXiv:2601.04480, 2026a

Reference 17

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:621cfdb526f41e1a304576b7d7b65f23b7bc7146ba84bf89b7561b9d555fc18e

Observation 97d5900d-30b8-48a1-a560-8cc1fd865ffd · outbound

This paper cites A structural probe for finding syntax in word representations.

Laguerre Geometry for Interpreting Large Language Models A structural probe for finding syntax in word representations

Reference 18

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:7a0ae31214a0feaef7aea95379ae7a0d65aae41a821e79c696f0410a60d0bcba

Observation 7401db93-f643-4f57-9f88-a000cf5a9fa3 · outbound

This paper cites On the Origins of Linear Representations in Large Language Models.

Laguerre Geometry for Interpreting Large Language Models On the Origins of Linear Representations in Large Language Models

Reference 19

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:7c8f4df1ee07ff65091ddc7cc32dd6724f4661c25ba00014165c570d631ccbe5

Observation 8797ce8b-d67c-4c51-baa5-3e720a96cf38 · outbound

This paper cites Are Sparse Autoencoders Useful? A Case Study in Sparse Probing.

Laguerre Geometry for Interpreting Large Language Models Are Sparse Autoencoders Useful? A Case Study in Sparse Probing

Reference 20

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:8d3e533f0e4a8780372381c2886824b8c803d8f592dbd28bb201981311c2c2a1

Observation c15f40d8-0ecc-454a-be49-9d2ecd8dd30c · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Laguerre Geometry for Interpreting Large Language Models Efficient Estimation of Word Representations in Vector Space

Reference 21

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:3a59be718749e43c61586b5d1aff9306c51759678b081552a5c080b6265f2af9

Observation de2fbdc6-7f4e-4d7f-adc9-1edae24406eb · outbound

This paper cites Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures.

Laguerre Geometry for Interpreting Large Language Models Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures

Reference 22

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:2ee1336820625c5d0d581c3df39b7fe5464b417f9c8ba86525d36d1ca57cc51c

Observation 464273ff-aa07-418d-b1f5-1101a216c8aa · outbound

This paper cites Steering Llama 2 via Contrastive Activation Addition.

Laguerre Geometry for Interpreting Large Language Models Steering Llama 2 via Contrastive Activation Addition

Reference 23

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:b583fc56a2fc57bea5dda689248b4a7dfb34bd43aa33b4aacb91b233ee693a29

Observation 2a46f501-d06b-4f94-8d62-f950147282db · outbound

This paper cites The Linear Representation Hypothesis and the Geometry of Large Language Models.

Laguerre Geometry for Interpreting Large Language Models The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 24

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:9e1bace660ea9261708df70775e6ad74e9224a85acd9d799a6f527cdf2763341

Observation 9c693dfc-b249-4a75-8312-09c74618330a · outbound

This paper cites The geometry of categorical and hierarchical concepts in large language models.

Laguerre Geometry for Interpreting Large Language Models The geometry of categorical and hierarchical concepts in large language models

Reference 25

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:a4584d13302d9cccb2731ac41e968cc1fb75ea317b1d440262019ece94097a21

Observation 1d4ba225-c87f-42d0-b513-3e8b461a5255 · outbound

This paper cites On the number of response regions of deep feed forward networks with piece-wise linear activations.

Laguerre Geometry for Interpreting Large Language Models On the number of response regions of deep feed forward networks with piece-wise linear activations

Reference 26

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:33da53696d5fa1f221c8c3d3136aa8833f247a301ec96fa92fce605324d3f5dd

Observation 8b5c2bae-b07d-421e-9179-ab9abe6f8603 · outbound

This paper cites Linear Representations of Hierarchical Concepts in Language Models.

Laguerre Geometry for Interpreting Large Language Models Linear Representations of Hierarchical Concepts in Language Models

Reference 27

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:c97ad00bff192f61dde3a56bb5c9dfbef807d7aab77193679d3bf8f91f5b5601

Observation 3c44db94-bbc3-4416-bdeb-2f5f38bf5afc · outbound

This paper cites From directions to regions: Decomposing activations in language models via local geometry.arXiv preprint arXiv:2602.02464,.

Laguerre Geometry for Interpreting Large Language Models From directions to regions: Decomposing activations in language models via local geometry.arXiv preprint arXiv:2602.02464,

Reference 28

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:6fdc66ec5f8822ca3c624d79f7901584bf4773b87527c9c1dc68b9f23de05448

Observation 8255463b-7d81-4aa7-a958-0bdf24a853db · outbound

This paper cites Open Problems in Mechanistic Interpretability.

Laguerre Geometry for Interpreting Large Language Models Open Problems in Mechanistic Interpretability

Reference 29

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:7dcdf2a99a7a7edb53db9d3010fa25fe33587548755dfe691e83cb57c54cd863

Observation 3b449814-c0f5-4f38-b522-6a40a3ae3d9d · outbound

This paper cites Emotion Concepts and their Function in a Large Language Model.

Laguerre Geometry for Interpreting Large Language Models Emotion Concepts and their Function in a Large Language Model

Reference 30

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:4693540eb71573ab4f919a73587834761a562a79a2e855c4195cb1fe3b80c7f6

Observation 702f2fe1-953d-4aea-a02a-ed0f969b84cd · outbound

This paper cites Geometric Capacity of Transformers: A Tropical Geometry Perspective.

Laguerre Geometry for Interpreting Large Language Models Geometric Capacity of Transformers: A Tropical Geometry Perspective

Reference 31

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:9d564ff8f942b7a14f4f5e297e513e6d9e66bf17d8235196a6d79499448982bf

Observation a549c34f-e862-40ca-91ac-0459e9ef02ef · outbound

This paper cites Sparsity is Combinatorial Depth: Quantifying MoE Expressivity via Tropical Geometry.

Laguerre Geometry for Interpreting Large Language Models Sparsity is Combinatorial Depth: Quantifying MoE Expressivity via Tropical Geometry

Reference 32

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:0f833eb91a49d508ff64650ac7af366b54477835ffcd945f82318c1c9813ddba

Observation 5e94fa43-6f44-4ba5-95f1-5bc354ee7f31 · outbound

This paper cites Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet.

Laguerre Geometry for Interpreting Large Language Models Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

Reference 33

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:b6d80f4ba96193c4490bff093b5e47e66e717a273d49c1199ee3e4281baf3b8e

Observation 43239167-f0df-40cd-9de9-7d858358ce81 · outbound

This paper cites What do you learn from context? Probing for sentence structure in contextualized word representations.

Laguerre Geometry for Interpreting Large Language Models What do you learn from context? Probing for sentence structure in contextualized word representations

Reference 34

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:5b7d5a0ef8bedd32b03d226ce5a0c6c54507fdddabb087f14dadece3ab54b0e7

Observation 7f30ac83-cb7f-4193-95a0-7708534aeb8e · outbound

This paper cites Steering Language Models With Activation Engineering.

Laguerre Geometry for Interpreting Large Language Models Steering Language Models With Activation Engineering

Reference 35

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:d20b0989d8fd67d5740d0018a770afc247892a44ab67faeb06e8856ef8defd4d

Observation 984aec55-eda8-4a39-a2e3-559eded843aa · outbound

This paper cites NExT-GPT: Any-to-Any Multimodal LLM.

Laguerre Geometry for Interpreting Large Language Models NExT-GPT: Any-to-Any Multimodal LLM

Reference 36

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:163b2b44f5e56767e53461fdc8c543e433117eb35b2a412f3dc5a6b71e59a64b

Observation c9c080b5-f940-4150-a1bb-18bb7ee8f6f5 · outbound

This paper cites The Lattice Representation Hypothesis of Large Language Models.

Laguerre Geometry for Interpreting Large Language Models The Lattice Representation Hypothesis of Large Language Models

Reference 37

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:d8844af167eb4bedf48a1e495f0d289f4d74a2c5c1fb91be3c9bfcd724a25690

Observation e22c11b4-7b40-4613-8ff1-716bda0dd6c1 · outbound

This paper cites Beyond single concept vector: Modeling concept subspace in llms with gaussian distribution.

Laguerre Geometry for Interpreting Large Language Models Beyond single concept vector: Modeling concept subspace in llms with gaussian distribution

Reference 38

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:547f02218caf339cd8995db1b07d13774ad1b1ecb8b165d41e4a048504123619

Observation c6e0e58b-817e-4300-b374-bbe92420dffb · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Laguerre Geometry for Interpreting Large Language Models Representation Engineering: A Top-Down Approach to AI Transparency

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-14T10:42:30.055074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:ab70beb273d88b5580c72bba45db6eb9750d45957b45c8e00bdc0e3a499cdb27

Observation 6f0a6cee-e0bd-4ae9-b3d4-36014e12b4fe · outbound

This paper cites Let z′ denote the vertical projection of z onto U, and let z′′ denote its vertical projection onto Π(S).

Laguerre Geometry for Interpreting Large Language Models Let z′ denote the vertical projection of z onto U, and let z′′ denote its vertical projection onto Π(S)

Reference 40

Resolution
unresolved
no resolver link, observed 2026-07-14T10:42:30.055074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:8f542e44db80b4437ae054fa5c97bce0a30cea371f277e54245b5618a0c4de8d

Observation 68b75fed-9017-4450-9b30-2b75c1428cfb · outbound

This paper cites _Dogs" to retrieve its hyponyms/hypernyms. (B) Use the domination score of.

Laguerre Geometry for Interpreting Large Language Models _Dogs" to retrieve its hyponyms/hypernyms. (B) Use the domination score of

Reference 41

Resolution
unresolved
no resolver link, observed 2026-07-14T10:42:30.055074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:8d983a1d6bd112da233eef0db38d02f08e1844568619fafb5009a66832c9f767

Observation 5f476400-0f46-4d89-88f4-cfe6ba95138e · outbound

This paper cites You are in a fictional world where Hamburg and Frankfurt have swapped their names. The Bode Museum is located in the city of.

Laguerre Geometry for Interpreting Large Language Models You are in a fictional world where Hamburg and Frankfurt have swapped their names. The Bode Museum is located in the city of

Reference 42

Resolution
unresolved
no resolver link, observed 2026-07-14T10:42:30.055074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:f3dcf73583f75fa14fa38e96b68f2d3d993f19375b0db470decda8dc7bb0d164

Pith citing papers

No inbound Pith citation observations are available.