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Paper Citation Record · LEDGER

Sparsification and Reconstruction from the Perspective of Representation Geometry

As of 8 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2505.22506.

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

pith.paper-citation-record.v1
2505.22506 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:11:40.473262Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

48 of 48 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2f85038-48b3-4789-9cad-ea89136df48a · outbound

This paper cites Open Problems in Mechanistic Interpretability.

Sparsification and Reconstruction from the Perspective of Representation Geometry Open Problems in Mechanistic Interpretability

Reference 1

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Observation 7dae0578-66a7-4a69-83d6-25c21cc00e58 · outbound

This paper cites Mechanistic Interpretability for AI Safety -- A Review.

Sparsification and Reconstruction from the Perspective of Representation Geometry Mechanistic Interpretability for AI Safety -- A Review

Reference 2

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Observation 21ac2969-93b1-47db-b6e7-3bf3678d8448 · outbound

This paper cites Towards automated circuit discovery for mechanistic interpretability,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Towards automated circuit discovery for mechanistic interpretability,

Reference 3

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Observation 79ced0bc-9c07-4cae-85fc-a3ea2ee6d445 · outbound

This paper cites Toy Models of Superposition.

Sparsification and Reconstruction from the Perspective of Representation Geometry Toy Models of Superposition

Reference 4

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Observation ac8a01a9-53f1-4e8d-87c8-b553f563900b · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Towards monosemanticity: Decomposing language models with dictionary learning,

Reference 5

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Source-reported events for the cited work

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Observation 74b492ac-40e0-47d4-ae3a-8587d259fbc1 · outbound

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

Sparsification and Reconstruction from the Perspective of Representation Geometry The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 6

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

Sparsification and Reconstruction from the Perspective of Representation Geometry On the Origins of Linear Representations in Large Language Models

Reference 7

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Observation 3506ef1b-8f23-4d0d-a5c3-758ddf888829 · outbound

This paper cites Feature manifold toy model,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Feature manifold toy model,

Reference 8

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Observation b3f89a17-2f54-4b91-b149-3593b7a074d4 · outbound

This paper cites Not All Language Model Features Are One-Dimensionally Linear.

Sparsification and Reconstruction from the Perspective of Representation Geometry Not All Language Model Features Are One-Dimensionally Linear

Reference 9

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Observation c787a2d9-6011-42ff-ba99-5f75b7dbde95 · outbound

This paper cites Latent Space Characterization of Autoencoder Variants.

Sparsification and Reconstruction from the Perspective of Representation Geometry Latent Space Characterization of Autoencoder Variants

Reference 10

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Observation d06c810a-49e8-45d3-896c-12eb645f61ac · outbound

This paper cites Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering.

Sparsification and Reconstruction from the Perspective of Representation Geometry Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering

Reference 11

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Observation efd36319-e7ef-429f-a342-580829d6bd37 · outbound

This paper cites Interpreting Attention Layer Outputs with Sparse Autoencoders.

Sparsification and Reconstruction from the Perspective of Representation Geometry Interpreting Attention Layer Outputs with Sparse Autoencoders

Reference 12

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Observation adaf7e9d-bd00-4910-aee2-5d9d9bfdff4b · outbound

This paper cites Language models are unsupervised multitask learners,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Language models are unsupervised multitask learners,

Reference 13

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Observation 8632adb9-6c75-4a4f-8345-3d60f5060d84 · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Pythia: A suite for analyzing large language models across training and scaling,

Reference 14

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Observation b79504d4-9e49-47a5-8c4d-a7397c164c65 · outbound

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

Sparsification and Reconstruction from the Perspective of Representation Geometry Gemma 2: Improving Open Language Models at a Practical Size

Reference 15

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Observation c0063508-be3a-4cf5-8339-d3d01ad6715b · outbound

This paper cites Saelens,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Saelens,

Reference 16

Resolution
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Observation bc83f33f-b26c-4f08-b338-1d447135515f · outbound

This paper cites Geometry-Preserving Encoder/Decoder in Latent Generative Models.

Sparsification and Reconstruction from the Perspective of Representation Geometry Geometry-Preserving Encoder/Decoder in Latent Generative Models

Reference 17

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Observation eae553db-3ad9-4dca-8723-445127e17ccf · outbound

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Sparsification and Reconstruction from the Perspective of Representation Geometry Gromov-Wasserstein autoencoders,

Reference 18

Resolution
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Source-reported events for the cited work

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Observation 028d7c1d-8fcd-464a-b68d-918cd6caa6a4 · outbound

This paper cites Improving Dictionary Learning with Gated Sparse Autoencoders.

Sparsification and Reconstruction from the Perspective of Representation Geometry Improving Dictionary Learning with Gated Sparse Autoencoders

Reference 19

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Observation b36f4f1b-36a9-47c3-a7c3-b40aaf7446b2 · outbound

This paper cites Prolu: A nonlinearity for sparse autoencoders,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Prolu: A nonlinearity for sparse autoencoders,

Reference 20

Resolution
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Observation c821c2f1-f561-42bd-9b9d-10148a12a06e · outbound

This paper cites JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks.

Sparsification and Reconstruction from the Perspective of Representation Geometry JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks

Reference 21

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Observation 50f392a6-1331-4668-b790-836ee4e1ae44 · outbound

This paper cites BatchTopK Sparse Autoencoders.

Sparsification and Reconstruction from the Perspective of Representation Geometry BatchTopK Sparse Autoencoders

Reference 22

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Observation 776f3d7f-9f2a-496d-b447-daade0a074e4 · outbound

This paper cites Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders.

Sparsification and Reconstruction from the Perspective of Representation Geometry Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders

Reference 23

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Observation 34e39b35-705a-4e5a-b26d-dc0ddcbfbfb7 · outbound

This paper cites Efficient training of sparse autoencoders for large language models via layer groups,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Efficient training of sparse autoencoders for large language models via layer groups,

Reference 24

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Observation 3cacb0be-1ba7-4eeb-91ed-83c7e13cbd30 · outbound

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Sparsification and Reconstruction from the Perspective of Representation Geometry Orthogonal neural representations support perceptual judgments of natural stimuli,

Reference 26

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Observation 133b2240-07fc-49d9-b0da-d90583bd2b0e · outbound

This paper cites How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.

Sparsification and Reconstruction from the Perspective of Representation Geometry How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings

Reference 27

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Observation 7f2bf4d8-1cad-4c96-a0be-87a6ff4c2ba6 · outbound

This paper cites The Geometry of Multilingual Language Model Representations.

Sparsification and Reconstruction from the Perspective of Representation Geometry The Geometry of Multilingual Language Model Representations

Reference 28

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Observation 532c25da-386b-4954-8a68-c408d77ba38c · outbound

This paper cites Transformers represent belief state geometry in their residual stream,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Transformers represent belief state geometry in their residual stream,

Reference 29

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Source-reported events for the cited work

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Observation f8b66d2d-829b-40d4-972f-5710a5b59856 · outbound

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Sparsification and Reconstruction from the Perspective of Representation Geometry The Geometry of Concepts: Sparse Autoencoder Feature Structure

Reference 30

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Observation 265ebe3f-1167-479f-b87c-0a0154ff3962 · outbound

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Sparsification and Reconstruction from the Perspective of Representation Geometry Representation learning: A review and new perspectives,

Reference 31

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Observation 05415a07-9e36-42c0-b6cb-23b40fc3e038 · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework,.

Sparsification and Reconstruction from the Perspective of Representation Geometry beta-vae: Learning basic visual concepts with a constrained variational framework,

Reference 32

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Observation 7b669ba6-1dd6-4df7-931e-bbb093cd5bd8 · outbound

This paper cites Online dictionary learning for sparse coding,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Online dictionary learning for sparse coding,

Reference 33

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6485adf9-3021-484c-a404-927e4d6e0c98 · outbound

This paper cites Disentangled representation learning,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Disentangled representation learning,

Reference 34

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2c31b373-8fa6-4cff-8baf-fcd9ab950189 · outbound

This paper cites Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering,

Reference 35

Resolution
verified fuzzy
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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 26e858cb-f920-4db8-ba4d-ca6560e909e1 · outbound

This paper cites Disentangling disentanglement in variational autoencoders,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Disentangling disentanglement in variational autoencoders,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:43.636003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 02b69ed6-cdcb-4457-ab90-9abfb09c026d · outbound

This paper cites Guided variational autoencoder for disentanglement learning,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Guided variational autoencoder for disentanglement learning,

Reference 37

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 27bafa8e-5618-4351-8c3a-959f34316f0d · outbound

This paper cites Oogan: Disentangling gan with one-hot sampling and orthogonal regularization,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Oogan: Disentangling gan with one-hot sampling and orthogonal regularization,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:43.214794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation edea661e-ebe4-4b3d-9aa0-db19ffc149ea · outbound

This paper cites Disentanglement in a gan for unconditional speech synthesis,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Disentanglement in a gan for unconditional speech synthesis,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:42.945860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:39.454381Z digest=sha256:e24de3519e5e85de5ee6d40daf29e0b6311123753bb30414e9d2858b4e424ea5

Observation 516f40f4-bb46-4afd-b950-0222ff780f35 · outbound

This paper cites Diagonal attention and style-based gan for content-style disentanglement in image generation and translation,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Diagonal attention and style-based gan for content-style disentanglement in image generation and translation,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:42.644557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:39.586404Z digest=sha256:05af12d2250d569bf708e9c200f319b0cbc813209699e4a7ed8b7094018ec3ce

Observation b8af7ad5-5752-43f0-80b2-e250a30e059d · outbound

This paper cites Efficient Dictionary Learning with Switch Sparse Autoencoders.

Sparsification and Reconstruction from the Perspective of Representation Geometry Efficient Dictionary Learning with Switch Sparse Autoencoders

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T13:11:39.663720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:11:39.663720Z digest=sha256:7878770abd09a4c0f8a38b297b1c7fec17bb9cc368a77c7950efc24249635def

Observation 09fca0d3-dca8-4891-96d6-9b1488c71792 · outbound

This paper cites Towards Achieving Concept Completeness for Textual Concept Bottleneck Models.

Sparsification and Reconstruction from the Perspective of Representation Geometry Towards Achieving Concept Completeness for Textual Concept Bottleneck Models

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:11:40.775989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:39.789952Z digest=sha256:d33b1c446b476cfb755985a6b9ecfe41a3c6b5a18f6e505c4159d600685233bd

Observation 31407280-9ce3-4666-bc25-bd31f906c793 · outbound

This paper cites Measuring progress in dictionary learning for language model interpretability with board game models,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Measuring progress in dictionary learning for language model interpretability with board game models,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:42.447094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:39.920990Z digest=sha256:f0cb41dd07ab3cea9913bf06ad22543e018040247ee512e27f09d24472dd0e7c

Observation d18aace0-064c-4829-ab80-451c2e39bd78 · outbound

This paper cites Superposition Yields Robust Neural Scaling.

Sparsification and Reconstruction from the Perspective of Representation Geometry Superposition Yields Robust Neural Scaling

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T13:11:39.974971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:11:39.974971Z digest=sha256:5c40a8921e3546c6a58812d0c4fc4c22a37c52ff69138f1da89c94c851df0f12

Observation e5128492-c3e1-4dc1-8b1c-03e174697e46 · outbound

This paper cites Estimating local intrinsic dimensionality,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Estimating local intrinsic dimensionality,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:42.126926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:40.079113Z digest=sha256:8cd779e27cc547cacb942833b483f6fff92ff70199de0aa29cabfddb1bc3e6f8

Observation e7406a22-262c-4d59-b1a5-1b7fd8239eeb · outbound

This paper cites Geometrically bounding 3–manifolds, volume and betti numbers,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Geometrically bounding 3–manifolds, volume and betti numbers,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:41.738923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:40.191937Z digest=sha256:294dd34a027d2dc4c59c83ea9899fc12dfd853b22dcbf20fc1984e9bf0db8300

Observation 316eecc1-b888-4b2c-8743-0659a1d47546 · outbound

This paper cites Fast parallel algorithms for euclidean minimum spanning tree and hierarchical spatial clustering,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Fast parallel algorithms for euclidean minimum spanning tree and hierarchical spatial clustering,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:41.565322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:40.306693Z digest=sha256:678d146375089f3611f406ffbb6fb719de571c3bfeee909658e4419bb58c3b9c

Observation 928e618b-203e-403f-95ac-bcfa5559616c · outbound

This paper cites Applications of average geodesic distance in manifold learning,.

Sparsification and Reconstruction from the Perspective of Representation Geometry Applications of average geodesic distance in manifold learning,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:11:41.329955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:11:40.390899Z digest=sha256:6a650e26854a49d5e16ffbd4f0fdde75393515136ea92a8640b9d8ea9ee8a44c

Observation ad58d652-5385-49e9-aee3-ac4e087f0e4e · outbound

This paper cites A survey on sparse autoencoders: Interpreting the internal mechanisms of large language models,.

Sparsification and Reconstruction from the Perspective of Representation Geometry A survey on sparse autoencoders: Interpreting the internal mechanisms of large language models,

Reference 49

Resolution
malformed identifier
no resolver link, observed 2026-08-07T13:11:40.473262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:11:40.473262Z digest=sha256:c9101066ae9fe082ec7b89a42aa0a9d39eee897a3fe2260f443ec06740073bda

Pith citing papers

No inbound Pith citation observations are available.