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Paper Citation Record · LEDGER

Laguerre Geometry for Interpreting Large Language Models

As of 19 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 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

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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:b3dfcdaae34d04e8b1d144086c09e2252d4bbab164a18affa618a7885c8be8a1

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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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:c48ed3b8c404c469140e9340b1c1f226a13c6a9dd350026a345b4d8f163a6de1

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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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=sha256:2b0df2aed1df83aae30779fb4afb0e983bd9c3161ef72a8e7b9e7732915cb299

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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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:eff8b4b8b79465d6e8dd1eabc5bc77874fe733a20c01d1ea2272f30d64968683

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:f9a63f4f10ceb3cd1043178cec16c66be768eccfd47836d269cbcf3c16832b28

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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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:13017558ae77bbd57cdadc89ac23d0c3d8d018e16859aa2a318123f53d40aac0

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=sha256:0bb8919c4d4bcc7c75f9fd5f40ee818faca8c3f6f2165beca2bae4d2dfae991d

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=sha256:42ac9cc06951f67673c8019ecd347cf2bc071760ff6719f9174aefede6e32a17

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=sha256:70f81100b91f3895243a8aedd1d231435ffd4b5d50f681f8a746754c0a66cd42

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=sha256:42f9cabf80aa4db25f6f535833315cd678ea3f2c1fad5124547abd2c46c5ceea

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=sha256:20eaa3a64616712416c8773f9386ac37f657c08f26f39f812272656eae39e612

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:dda8c9a060de67c5e759e4aceda847e975c34853e472356a505e0ed7260a4a62

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=sha256:943b4cbad6f2e4c325c884e47150e6872823219aa366966d6643cb3b49a0d16e

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:f452db5d6821acb935181810950359b4c583435597f0d8c84dbe5ec0e9ea13db

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=sha256:7ef5579ec20e271f9335c5a8a9bec43e25577810e9bb3e7256cbf31cbadf0faf

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=sha256:97aa2db09c6cdffd6208c7a5cd3813f7a1a45710ce0e0b99dbf520484bcbf15e

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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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=sha256:4f0c9422daa31c0ee05f7fcb401023ede0dd3f16d3ee40094efd284d341dc508

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:dc55698345e6df0a0035d9f08cfb8a8372cea1bf8a43fc3b1ed2a4726e53065d

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:b822126713f871529e4d6b8e89906d0716aff595827318c3336169e8fdc97cda

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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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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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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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=sha256:8946751a7dee0edfe12fcda80984bcb9d853fff8f4f2a0ece23e4aeb688d1518

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=sha256:95856a7f69427641b785a6c115c738cfe6deed389195942472a7caf05bdb9e2f

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=sha256:232bc1721331eb5caa579434faca6bb5f36d3908475f9dedea34ec7eea63e160

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:dc469c384afa10401d2a387568574c86488c1f753ec1429dad2acdbb63a00a7e

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:c44e9a73886c898204d5d6f7a7d5fd7f7fe84c1df67523aef9a59132ee24703b

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=sha256:11538e91a39e1a4addeeb57b32ec7990d6ad7197233d1ddf1a949985101684e4

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=sha256:6043d295f58decf9799325ce0ebc2f51f490e8293bd7dbe7183a6c856d545fca

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=sha256:43368170cd6f2d86441935882145f1e7106075df16a35635c7c6f4afebdcee8a

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=sha256:101e3b9bd6a669924e7f6b40440cb6da462834dc91ca7c9f36124cc552b6583a

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=sha256:7d8792de75b34995fd743a92b2a7b5d61ac16cb8b1dad5c456082e84a37be5ee

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:b0b8caf2f94b706af114ed8c494bb54e3d0be614d0fc320c66b39652cffb7cf4

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:e80f65f8da846ffa3d1b36a6318ea3f08650c379e25cfe1ac512a8571e1d8a0a

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

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

Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T10:42:30.055074Z digest=sha256:4afe26cb5a23e92348a51d481253a56aaacc98148c728835efd71897e2386bc1

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=sha256:8ab3d3029422d8d9b14258dbb37286df52df86e9215561034af63214e2c0a8cd

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:ba4ddec6a8ca9cd26455ec18890eca1a859dae214d36e693b5eb7d0cca27de72

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:e80238296849caac41dcf26fb0121565ad7aa12fd1b9d523e84809ecbc16d30f

Pith citing papers

No inbound Pith citation observations are available.