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Paper Citation Record · LEDGER

Learning Elementary Cellular Automata with Transformers

As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.01417.

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

pith.paper-citation-record.v1
2412.01417 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:27:33.694814Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

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Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b952f01-70ce-485f-844f-7ae6c0053235 · outbound

This paper cites Learning to reason with llms.

Learning Elementary Cellular Automata with Transformers Learning to reason with llms

Reference 1

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Observation e7c74e89-b831-434b-a610-efb054451b35 · outbound

This paper cites Faith and fate: Limits of transformers on compositionality.

Learning Elementary Cellular Automata with Transformers Faith and fate: Limits of transformers on compositionality

Reference 2

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Observation 3f93f98d-05f0-43ee-9e88-30e24ee0a716 · outbound

This paper cites LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models.

Learning Elementary Cellular Automata with Transformers LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models

Reference 3

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Observation b96b3f47-6c40-4000-b62d-fba51ec6580b · outbound

This paper cites Conditional and Modal Reasoning in Large Language Models.

Learning Elementary Cellular Automata with Transformers Conditional and Modal Reasoning in Large Language Models

Reference 4

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Observation fd67d47e-bdc7-4f3f-84fd-d59615ca9845 · outbound

This paper cites Inductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis.

Learning Elementary Cellular Automata with Transformers Inductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis

Reference 5

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Observation 503d58db-f912-4555-8245-8818d895c830 · outbound

This paper cites Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models.

Learning Elementary Cellular Automata with Transformers Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models

Reference 6

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Observation d279f28a-95cc-4e03-8fe1-11ac98c8230e · outbound

This paper cites Llms still can’t plan; can lrms? a preliminary evaluation of openai’s o1 on planbench, 2024.

Learning Elementary Cellular Automata with Transformers Llms still can’t plan; can lrms? a preliminary evaluation of openai’s o1 on planbench, 2024

Reference 7

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Observation d59e03af-e484-4af4-aac1-36a7d6e079a4 · outbound

This paper cites Attention is All you Need.

Learning Elementary Cellular Automata with Transformers Attention is All you Need

Reference 8

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Observation 8b281bd1-3071-44e9-b299-67e2035c949b · outbound

This paper cites Approximations by superpositions of a sigmoidal function.

Learning Elementary Cellular Automata with Transformers Approximations by superpositions of a sigmoidal function

Reference 9

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Observation d32bc398-426d-4bea-b357-bd65e661ee49 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Learning Elementary Cellular Automata with Transformers Multilayer feedforward networks are universal approximators

Reference 10

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Observation cb614542-6b76-4fc1-a6f8-a55fb35bd9f5 · outbound

This paper cites Are Transformers universal approximators of sequence-to-sequence functions?.

Learning Elementary Cellular Automata with Transformers Are Transformers universal approximators of sequence-to-sequence functions?

Reference 11

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Observation 58f4bba4-6865-4dba-be74-ae36a8ea0cb7 · outbound

This paper cites Representational strengths and limitations of transformers.

Learning Elementary Cellular Automata with Transformers Representational strengths and limitations of transformers

Reference 12

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Observation eb5794ee-ce39-4e09-af3a-571f3b6ef403 · outbound

This paper cites Uni- versal transformers.

Learning Elementary Cellular Automata with Transformers Uni- versal transformers

Reference 13

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Observation 9be132c5-0d99-4745-b608-25468cad540b · outbound

This paper cites On the Computational Power of Transformers and its Implications in Sequence Modeling.

Learning Elementary Cellular Automata with Transformers On the Computational Power of Transformers and its Implications in Sequence Modeling

Reference 14

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Observation c4b59e39-3568-4c7a-8638-08d7324d584f · outbound

This paper cites Attention is turing-complete.

Learning Elementary Cellular Automata with Transformers Attention is turing-complete

Reference 15

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Observation 3b5fceb4-cc64-4918-9a6b-9ac1f439a8df · outbound

This paper cites What Formal Languages Can Transformers Express? A Survey.

Learning Elementary Cellular Automata with Transformers What Formal Languages Can Transformers Express? A Survey

Reference 16

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Observation 82a08b74-7c91-45d6-a822-5596ea922997 · outbound

This paper cites Deep Learning for Symbolic Mathematics.

Learning Elementary Cellular Automata with Transformers Deep Learning for Symbolic Mathematics

Reference 17

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Observation 80bca968-db99-4e38-8899-4e8f54497e01 · outbound

This paper cites End-to-end symbolic regression with transformers.

Learning Elementary Cellular Automata with Transformers End-to-end symbolic regression with transformers

Reference 18

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Observation 425a7b6f-5d05-43e0-b566-b570ddba0f1f · outbound

This paper cites Deep Symbolic Regression for Recurrent Sequences.

Learning Elementary Cellular Automata with Transformers Deep Symbolic Regression for Recurrent Sequences

Reference 19

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Observation 5a4eb420-6838-4845-8fb1-c535daba2b86 · outbound

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Learning Elementary Cellular Automata with Transformers Unresolved cited work

Reference 20

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Observation e0b28e52-8256-40e8-b80c-499c72dfda35 · outbound

This paper cites Learning cellular automaton dynamics with neural networks.

Learning Elementary Cellular Automata with Transformers Learning cellular automaton dynamics with neural networks

Reference 21

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Observation 68507808-6180-4647-8b6c-04885b38a388 · outbound

This paper cites Cellular automata as convolutional neural networks.

Learning Elementary Cellular Automata with Transformers Cellular automata as convolutional neural networks

Reference 22

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Observation 99a49fd3-d140-4479-983b-58ad426a1997 · outbound

This paper cites Generalization over different cellular automata rules learned by a deep feed-forward neural network, 2021.

Learning Elementary Cellular Automata with Transformers Generalization over different cellular automata rules learned by a deep feed-forward neural network, 2021

Reference 23

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Observation 3aa46dd3-679e-43e5-9fc2-06cf0d42c730 · outbound

This paper cites Growing neural cellular automata.

Learning Elementary Cellular Automata with Transformers Growing neural cellular automata

Reference 24

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Observation 3a117c51-bcc8-41f9-bf0d-26cddf304c1a · outbound

This paper cites Hierarchical neural cellular automata.

Learning Elementary Cellular Automata with Transformers Hierarchical neural cellular automata

Reference 25

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Observation 4e335b31-9112-41bf-8a42-642932c34725 · outbound

This paper cites Cellular automata, many-valued logic, and deep neural networks.

Learning Elementary Cellular Automata with Transformers Cellular automata, many-valued logic, and deep neural networks

Reference 26

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Observation 16bb56b6-4340-45c4-8c58-03c7d501bd87 · outbound

This paper cites E(n)-equivariant Graph Neural Cellular Automata.

Learning Elementary Cellular Automata with Transformers E(n)-equivariant Graph Neural Cellular Automata

Reference 27

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Observation 415311c7-e3a2-4b86-a734-7fd91e3c9ec9 · outbound

This paper cites Attention-based neural cellular au- tomata.

Learning Elementary Cellular Automata with Transformers Attention-based neural cellular au- tomata

Reference 28

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Observation 57145220-aeaa-4244-81c5-8f6b34dc3965 · outbound

This paper cites Learning graph cellular automata.

Learning Elementary Cellular Automata with Transformers Learning graph cellular automata

Reference 29

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Observation 1034acef-605e-4321-9473-dcea7b259bb8 · outbound

This paper cites Learning spatio- temporal patterns with neural cellular automata.

Learning Elementary Cellular Automata with Transformers Learning spatio- temporal patterns with neural cellular automata

Reference 30

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Observation 6469ac0b-d071-49ad-bdfa-423ab4d8da56 · outbound

This paper cites Learning locally interacting discrete dynamical systems: Towards data-efficient and scalable prediction.

Learning Elementary Cellular Automata with Transformers Learning locally interacting discrete dynamical systems: Towards data-efficient and scalable prediction

Reference 31

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Observation 5bf69565-4864-4782-9ebc-4fd714cce07c · outbound

This paper cites It’s hard for neural networks to learn the game of life.

Learning Elementary Cellular Automata with Transformers It’s hard for neural networks to learn the game of life

Reference 32

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Source-reported events for the cited work

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Observation 168d163f-715a-4f06-a6ea-ef04eef68ddf · outbound

This paper cites Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life.

Learning Elementary Cellular Automata with Transformers Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life

Reference 33

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Observation 07153d5b-8b79-439d-b9b1-15102e2977e2 · outbound

This paper cites Reconstructing cellular automata rules from observations at nonconsecutive times.

Learning Elementary Cellular Automata with Transformers Reconstructing cellular automata rules from observations at nonconsecutive times

Reference 34

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Source-reported events for the cited work

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Observation 07d0d9fd-9205-4375-a605-fb56e1ba4866 · outbound

This paper cites LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata.

Learning Elementary Cellular Automata with Transformers LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata

Reference 35

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Observation 97198ba9-ace4-41fc-b58c-238a5972ada1 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Learning Elementary Cellular Automata with Transformers Chain-of-thought prompting elicits reasoning in large language models

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Pith citing papers

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