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Paper Citation Record · LEDGER

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2509.22553.

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

pith.paper-citation-record.v1
2509.22553 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:57:06.132639Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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Outbound references

Observation d237b6d7-cb0a-403f-83f5-af2ffc67ee4d · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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Observation 6ba09028-70f1-46ed-9b4d-7180c385b2bb · outbound

This paper cites GPT-4 Technical Report.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement GPT-4 Technical Report

Reference 2

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Observation bc50ede7-2b82-4ef7-95a8-9da9819c953d · outbound

This paper cites Interventional causal representation learning.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Interventional causal representation learning

Reference 3

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Observation 33624871-afa5-4a19-8ecf-49759c3a92c3 · outbound

This paper cites Physics of Language Models: Part 1, Learning Hierarchical Language Structures.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Physics of Language Models: Part 1, Learning Hierarchical Language Structures

Reference 4

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Observation 5151b62b-802f-499d-893b-9859f89afc5a · outbound

This paper cites A latent variable model approach to PMI -based word embeddings.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement A latent variable model approach to PMI -based word embeddings

Reference 5

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Observation ca71de8e-8931-426c-bdee-dca8d9c29bfb · outbound

This paper cites GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow , March 2021.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow , March 2021

Reference 6

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Observation 0d933759-5271-4a22-8098-4f33aa5cd61f · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 7

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Observation 65b9d872-7d4c-4fa0-8125-66c04b322445 · outbound

This paper cites Learning linear causal representations from interventions under general nonlinear mixing.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Learning linear causal representations from interventions under general nonlinear mixing

Reference 8

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Observation 3bbbcdf8-f172-4dc9-8dd9-350d19f3b1e4 · outbound

This paper cites Invariance, causality and robustness.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Invariance, causality and robustness

Reference 9

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Observation c371356b-740b-4ded-baf9-be2a53cd9e84 · outbound

This paper cites Causal inference of general treatment effects using neural networks with a diverging number of confounders.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal inference of general treatment effects using neural networks with a diverging number of confounders

Reference 10

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Observation af4cb13a-71f8-4401-861b-1b457b84165e · outbound

This paper cites Independent component analysis, a new concept? Signal Processing, 36 0 (3): 0 287--314, 1994.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Independent component analysis, a new concept? Signal Processing, 36 0 (3): 0 287--314, 1994

Reference 11

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Observation a1967e33-87b1-4e63-9526-19f87148a882 · outbound

This paper cites Analyse g \'e n \'e rale des liaisons stochastiques: etude particuli \`e re de l'analyse factorielle lin \'e aire.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Analyse g \'e n \'e rale des liaisons stochastiques: etude particuli \`e re de l'analyse factorielle lin \'e aire

Reference 12

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Observation a6540e20-9497-41a1-982e-0ef448e4e044 · outbound

This paper cites Identifying the consequences of dynamic treatment strategies: A decision theoretic overview.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Identifying the consequences of dynamic treatment strategies: A decision theoretic overview

Reference 13

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Observation 45b5f90d-5971-4ec3-a040-e08be84ddc78 · outbound

This paper cites On random graphs.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement On random graphs

Reference 14

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Observation 4b60dd83-c319-4a65-bb71-a9c59719ae0e · outbound

This paper cites Causal disentanglement for single-cell representations and controllable counterfactual generation.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal disentanglement for single-cell representations and controllable counterfactual generation

Reference 15

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Observation 035c92f0-e1e4-4fc4-a860-965ffd7b2970 · outbound

This paper cites Measuring statistical dependence with hilbert-schmidt norms.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Measuring statistical dependence with hilbert-schmidt norms

Reference 16

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Observation 897d0d3d-2bab-4bdf-9c97-69d320b422e8 · outbound

This paper cites Independent component analysis: Algorithms and applications.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Independent component analysis: Algorithms and applications

Reference 17

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Observation c2d29d32-c349-4927-b84d-5a807a193cfb · outbound

This paper cites Learning causal representations from general environments: Identifiability and intrinsic ambiguity.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Learning causal representations from general environments: Identifiability and intrinsic ambiguity

Reference 18

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Observation ea65090f-e8fc-4c36-b735-07b47362ef54 · outbound

This paper cites What is causal about causal models and representations?.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement What is causal about causal models and representations?

Reference 19

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Observation ed244df1-27b3-46f3-9058-653445eb3658 · outbound

This paper cites Causal Reasoning and Large Language Models: Opening a New Frontier for Causality.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

Reference 20

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Observation f1fd01ba-1319-4c2b-8d9f-f579f05fa5d5 · outbound

This paper cites Optimality of the J ohnson- L indenstrauss lemma.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Optimality of the J ohnson- L indenstrauss lemma

Reference 21

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Observation 87155193-dedd-415a-9f38-af9e65d38e06 · outbound

This paper cites Factor analysis as a statistical method.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Factor analysis as a statistical method

Reference 22

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Observation b92a8f1a-da94-44be-ac96-00fbf2fd53e6 · outbound

This paper cites Causal estimation of memorisation profiles.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal estimation of memorisation profiles

Reference 23

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Observation 305d73f4-0a5d-4ce1-be6c-5ae1d70f78b4 · outbound

This paper cites DeepSeek-V3 Technical Report.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement DeepSeek-V3 Technical Report

Reference 24

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Observation 2ed4928f-4360-44e1-bcf8-05e592929f33 · outbound

This paper cites The Llama 3 family of models.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement The Llama 3 family of models

Reference 25

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Observation 7da37483-bcc9-4746-8bf6-62d7e0a1d009 · outbound

This paper cites Fourth moments and independent component analysis.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Fourth moments and independent component analysis

Reference 26

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Observation 4f4d0875-fb86-4533-906b-9be389370489 · outbound

This paper cites Linguistic regularities in continuous space word representations.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Linguistic regularities in continuous space word representations

Reference 27

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Observation e15c5264-3451-4094-80ce-25295576313c · outbound

This paper cites Determining the number of factors from empirical distribution of eigenvalues.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Determining the number of factors from empirical distribution of eigenvalues

Reference 28

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Observation 50067741-3a91-4eb5-ae9d-af68632c60ea · outbound

This paper cites The linear representation hypothesis and the geometry of large language models.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement The linear representation hypothesis and the geometry of large language models

Reference 29

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Observation 5a87c866-3c42-4bff-b8f8-00cf501a623a · outbound

This paper cites Automatic differentiation in PyTorch.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Automatic differentiation in PyTorch

Reference 30

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Observation c28b0a56-5fec-44e1-84a0-51715b4940b8 · outbound

This paper cites Elements of causal inference: foundations and learning algorithms.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Elements of causal inference: foundations and learning algorithms

Reference 31

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Observation 8b80538e-0907-498a-a770-99e0fc306cbc · outbound

This paper cites From causal to concept-based representation learning.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement From causal to concept-based representation learning

Reference 32

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Observation 94f51811-81cd-4c6a-b5b7-1ceaf70fd6ec · outbound

This paper cites Consistency and asymptotic normality of FastICA and bootstrap FastICA.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Consistency and asymptotic normality of FastICA and bootstrap FastICA

Reference 33

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Observation 6b3ca4b9-ff29-42f4-ab2c-c6f31e6e6bc2 · outbound

This paper cites a us Kleindessner, Chris Russell, Dominik Janzing, Bernhard Sch \.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement a us Kleindessner, Chris Russell, Dominik Janzing, Bernhard Sch \

Reference 34

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Observation d1a5245f-bbc2-479b-957f-95ee02d21e54 · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 35

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source=arxiv_source observed=2026-08-04T14:57:06.080466Z digest=sha256:c08553bec0f4503367dbf3896fb1899323a608ed9449e86a7f244d9f870378be

Observation 91334cdd-08c6-4afa-adaa-75e7182a7a44 · outbound

This paper cites Toward causal representation learning.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Toward causal representation learning

Reference 36

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source=arxiv_source observed=2026-08-04T14:57:06.083877Z digest=sha256:5900834eb1b60a32f8852c5cc005599bca71d5861e47e57f5fbbcecae4d5cd95

Observation e3aee7a0-4bad-4705-b3f7-f0eabbbcf400 · outbound

This paper cites On a property of the normal distribution.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement On a property of the normal distribution

Reference 37

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source=arxiv_source observed=2026-08-04T14:57:06.086852Z digest=sha256:a9eebd6e28919074cf24a5b490b51fafddbc0c1d88caa380f6be6980667f2a95

Observation 4014a7ad-6a0c-43d0-a1b1-9cfef787c39e · outbound

This paper cites Causal temporal representation learning with nonstationary sparse transition.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal temporal representation learning with nonstationary sparse transition

Reference 38

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source=arxiv_source observed=2026-08-04T14:57:06.089890Z digest=sha256:e87c1f166d4d67a7c03be5642951ec0e923839c35bfd5b18868080915d87a446

Observation 1aa24e5b-c2b3-480c-b117-5ac80d9a1d5f · outbound

This paper cites Linear causal disentanglement via interventions.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Linear causal disentanglement via interventions

Reference 39

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source=arxiv_source observed=2026-08-04T14:57:06.092862Z digest=sha256:2179bc64009f472fb5a62de215d5f19300c0dc7435362f0d5fe14e1d035b4ac5

Observation d43993c5-dcdb-46fd-8680-77fed4e2bda3 · outbound

This paper cites Causal representation learning from multimodal biomedical observations.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal representation learning from multimodal biomedical observations

Reference 40

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source=arxiv_source observed=2026-08-04T14:57:06.095848Z digest=sha256:fc5b100aa4abc7f3095764ddbcf2b362cf2385395f18df8df593cb4f121eefdd

Observation c86e53e9-13a9-4a1a-902f-c523f5e33e2f · outbound

This paper cites Score-based causal representation learning from interventions: Nonparametric identifiability.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Score-based causal representation learning from interventions: Nonparametric identifiability

Reference 41

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source=arxiv_source observed=2026-08-04T14:57:06.098908Z digest=sha256:22fb7159706a94d3ad096459b66c6f25805b4054116698ec6147cb6df8f450a6

Observation 25e28b86-e45d-42c4-ad41-ce6fd92aba00 · outbound

This paper cites General identifiability and achievability for causal representation learning.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement General identifiability and achievability for causal representation learning

Reference 42

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source=arxiv_source observed=2026-08-04T14:57:06.101862Z digest=sha256:3499ab2093b1d3caf883e866bb98a79ef89886d302b22bf642394942935af3e5

Observation d484b82a-2fb6-417b-a08d-3a110d4680cf · outbound

This paper cites Linear Causal Representation Learning from Unknown Multi-node Interventions.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Linear Causal Representation Learning from Unknown Multi-node Interventions

Reference 43

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source=arxiv_source observed=2026-08-04T14:57:06.104962Z digest=sha256:d685fc9f06cf53e9b508e8ea7373c782140ac5c97a464869ced13c30643cd2f2

Observation 01dbb8e7-a9af-4ed5-be30-822cf3676ff7 · outbound

This paper cites Score-based Causal Representation Learning: Linear and General Transformations.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Score-based Causal Representation Learning: Linear and General Transformations

Reference 44

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source=arxiv_source observed=2026-08-04T14:57:06.108333Z digest=sha256:3e6d4dc83e88c84eae2323c16947ea4b1debfaaed4037f6b463a9637b474f259

Observation ee664493-0939-46f8-9ea9-731b573140ba · outbound

This paper cites Dimension reduction via adaptive slicing.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Dimension reduction via adaptive slicing

Reference 45

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source=arxiv_source observed=2026-08-04T14:57:06.111302Z digest=sha256:3293cb5146609329db7570a5c428f5095f2588d64d6d05ec810be31ee9e82240

Observation 70d2189f-0134-4a71-92e0-8a40e42c335f · outbound

This paper cites Identifiability guarantees for causal disentanglement from purely observational data.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Identifiability guarantees for causal disentanglement from purely observational data

Reference 46

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source=arxiv_source observed=2026-08-04T14:57:06.114541Z digest=sha256:38ec228958b6153b7ad3f04077be65bbf38579f1dcf0883d996c5278e66d188a

Observation 0b899b64-4914-4198-9e39-7d44f859447f · outbound

This paper cites Sketching as a tool for numerical linear algebra.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Sketching as a tool for numerical linear algebra

Reference 47

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source=arxiv_source observed=2026-08-04T14:57:06.117954Z digest=sha256:dbbbaed9677531e694d6f7247a6d8f71846afa5dde0456b53c45dddbd272a9fc

Observation 450b2471-2f01-47b6-934b-e04e7fe1cda6 · outbound

This paper cites Stability.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Stability

Reference 48

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source=arxiv_source observed=2026-08-04T14:57:06.120895Z digest=sha256:c247aa80d46393473fd51467f7632487116ce8c4c8704161cb76c66ffdbd425b

Observation e34efd3c-2249-46b4-ab3e-ae53eafa64d1 · outbound

This paper cites Identifiability guarantees for causal disentanglement from soft interventions.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Identifiability guarantees for causal disentanglement from soft interventions

Reference 49

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source=arxiv_source observed=2026-08-04T14:57:06.123738Z digest=sha256:25172a89fc31472d1d67e03d62e3dbb9bad5a3911ce21ea4cb1cb2a35ad7d06d

Observation f10c4f61-0f3c-46a5-a379-405167a8c121 · outbound

This paper cites Causal representation learning from multiple distributions: A general setting.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement Causal representation learning from multiple distributions: A general setting

Reference 50

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source=arxiv_source observed=2026-08-04T14:57:06.126698Z digest=sha256:0e9fc67d107b4dfb8365367c2d59ed13fc3a52f31b19c5b74931fc18cee82d15

Observation a65f9a36-e326-4b25-b871-f323ea112445 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement TinyLlama: An Open-Source Small Language Model

Reference 51

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source=arxiv_source observed=2026-08-04T14:57:06.129509Z digest=sha256:0724235eaa7c780a0c32c4579bad251cfa489f7cacac00ab46279e75cfbbd849

Observation dc714cd4-72e9-4822-a875-6a2845610105 · outbound

This paper cites write newline.

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement write newline

Reference 52

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source=arxiv_source observed=2026-08-04T14:57:06.132639Z digest=sha256:acfd12fe802b900cdc50ae9dfa116ff932178868979e1d1140f6ed2d8c0b8b1f

Pith citing papers

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