Paper Citation Record · LEDGER

Automating the Search for Artificial Life with Foundation Models

As of 23 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 9 inbound Pith citation observations for arXiv:2412.17799.

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

pith.paper-citation-record.v1
2412.17799 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:11:36.150297Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:49:08.620176Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T00:04:06.731165Z

Reference resolution

37 of 37 outbound references displayed

verified exact3
verified fuzzy11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8550288b-6203-4ed5-b574-e34fdf5ba9ac · outbound

This paper cites Following the original NCA paper [Mordvintsev et al., 2020], a pool of 256 simulation states is maintained.

Automating the Search for Artificial Life with Foundation Models Following the original NCA paper [Mordvintsev et al., 2020], a pool of 256 simulation states is maintained

Reference 2

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Observation 1fe8831c-ccbc-4495-a946-a062fdbef644 · outbound

This paper cites DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data.

Automating the Search for Artificial Life with Foundation Models DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data

Reference 8

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Observation 8e57dadd-5167-4e1a-8e2e-7b56df4b0e71 · outbound

This paper cites Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks.

Automating the Search for Artificial Life with Foundation Models Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks

Reference 9

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Observation cf0da6ac-e3a5-4625-ba0d-d777044eef09 · outbound

This paper cites Open-Endedness is Essential for Artificial Superhuman Intelligence.

Automating the Search for Artificial Life with Foundation Models Open-Endedness is Essential for Artificial Superhuman Intelligence

Reference 10

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Observation 707d3767-a193-49b8-a8a4-a377d8a43907 · outbound

This paper cites The Platonic Representation Hypothesis.

Automating the Search for Artificial Life with Foundation Models The Platonic Representation Hypothesis

Reference 11

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Observation 51b4683f-6ea2-413c-82c8-bdeb0596331b · outbound

This paper cites Scaling Laws for Neural Language Models.

Automating the Search for Artificial Life with Foundation Models Scaling Laws for Neural Language Models

Reference 12

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Observation aa1f6ae9-530a-464e-b84b-1fec528158b9 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Automating the Search for Artificial Life with Foundation Models UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 15

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Observation acef9ffd-1e7b-4676-94ac-e09d42086e80 · outbound

This paper cites Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, and Michael Levin.

Automating the Search for Artificial Life with Foundation Models Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, and Michael Levin

Reference 17

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Observation 774f056f-9008-47a2-acf5-fb07874ec55a · outbound

This paper cites Particle lenia and the energy-based for- mulation.

Automating the Search for Artificial Life with Foundation Models Particle lenia and the energy-based for- mulation

Reference 18

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Observation 84dcf678-5e9a-4539-aa59-39cb9564b2c8 · outbound

This paper cites Sophistication as randomness deficiency.

Automating the Search for Artificial Life with Foundation Models Sophistication as randomness deficiency

Reference 19

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Observation 372828f6-c849-4330-9089-48ce992fa250 · outbound

This paper cites Biomaker CA: a Biome Maker project using Cellular Automata.

Automating the Search for Artificial Life with Foundation Models Biomaker CA: a Biome Maker project using Cellular Automata

Reference 24

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Observation 33f072e8-9bee-46e6-b883-752269b8ea4b · outbound

This paper cites Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems.

Automating the Search for Artificial Life with Foundation Models Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems

Reference 25

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Observation 6a231f84-78a8-49c5-bc27-805f2653b871 · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Automating the Search for Artificial Life with Foundation Models Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 27

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Observation e577b1f6-ef8c-4fc6-9031-ab648731e547 · outbound

This paper cites Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents.

Automating the Search for Artificial Life with Foundation Models Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents

Reference 28

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Observation 4c63df78-b311-4a7b-b4a6-c5b09ebc107a · outbound

This paper cites Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley.

Automating the Search for Artificial Life with Foundation Models Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:11:36.782960Z

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Observation ff9af3f5-2cdc-4fcd-9095-d9903a5ebd76 · outbound

This paper cites A Class of Models with the Potential to Represent Fundamental Physics.

Automating the Search for Artificial Life with Foundation Models A Class of Models with the Potential to Represent Fundamental Physics

Reference 31

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Source-reported events for the cited work

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Observation 8e7b85d8-1c8e-4ac3-bc9b-b781c8f8a429 · outbound

This paper cites VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding.

Automating the Search for Artificial Life with Foundation Models VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

Reference 32

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Observation 20eaf634-29a1-4202-b918-57b527f1a90a · outbound

This paper cites The y-axis measures the negative score from Equation.

Automating the Search for Artificial Life with Foundation Models The y-axis measures the negative score from Equation

Reference 35

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Source-reported events for the cited work

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Observation cf6ca45d-4040-49cd-b674-1afaef7e3064 · outbound

This paper cites a biological cell.

Automating the Search for Artificial Life with Foundation Models a biological cell

Reference 37

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Observation 2942e58b-987d-4194-b789-390d165b92ba · outbound

This paper cites OMNI: Open-endedness via Models of human Notions of Interestingness.

Automating the Search for Artificial Life with Foundation Models OMNI: Open-endedness via Models of human Notions of Interestingness

Reference 1994

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Observation 8b9b7ade-2187-4b91-92e6-183fa8f6f5c0 · outbound

This paper cites Snowbird’98, Utah, 1998; see also ?.

Automating the Search for Artificial Life with Foundation Models Snowbird’98, Utah, 1998; see also ?

Reference 1997

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Observation aabbf737-b155-461f-95e2-b79616167221 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Automating the Search for Artificial Life with Foundation Models On the Opportunities and Risks of Foundation Models

Reference 2000

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Observation 7ce5ebb5-2664-47a9-8057-68561d180962 · outbound

This paper cites Discovering Preference Optimization Algorithms with and for Large Language Models.

Automating the Search for Artificial Life with Foundation Models Discovering Preference Optimization Algorithms with and for Large Language Models

Reference 2001

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Observation 68ce2f18-e1d4-42fb-9b6b-c6d936127e3f · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Automating the Search for Artificial Life with Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 2004

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Observation 430a5586-3457-4c8e-be84-68e50807b79c · outbound

This paper cites Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations.

Automating the Search for Artificial Life with Foundation Models Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations

Reference 2009

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Observation 27f73f3f-e9dd-4fd6-9172-4678f4985dd3 · outbound

This paper cites Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity.

Automating the Search for Artificial Life with Foundation Models Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity

Reference 2010

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Observation 1b6b2293-681d-4dad-b70f-ff18cb48389d · outbound

This paper cites Illuminating search spaces by mapping elites.

Automating the Search for Artificial Life with Foundation Models Illuminating search spaces by mapping elites

Reference 2013

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Observation 49624422-8de9-4ba3-8095-258fec1586a4 · outbound

This paper cites Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction.

Automating the Search for Artificial Life with Foundation Models Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction

Reference 2014

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Observation 9dc0b440-6631-4c4c-aa5d-429c7735f285 · outbound

This paper cites From text to life: On the reciprocal relationship between artificial life and large language models.

Automating the Search for Artificial Life with Foundation Models From text to life: On the reciprocal relationship between artificial life and large language models

Reference 2015

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Observation ef2b489d-783b-45b6-8ded-4d32f5614405 · outbound

This paper cites Flow-lenia: Towards open-ended evolution in cellular automata through mass conser- vation and parameter localization.

Automating the Search for Artificial Life with Foundation Models Flow-lenia: Towards open-ended evolution in cellular automata through mass conser- vation and parameter localization

Reference 2017

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Observation 3dff8cb2-0bcf-494a-bb18-28aeb4002eaa · outbound

This paper cites bird-like objects.

Automating the Search for Artificial Life with Foundation Models bird-like objects

Reference 2018

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Observation effbff9e-755e-4ece-80f0-39f4263d2fdf · outbound

This paper cites Video understanding with large language models: A survey.

Automating the Search for Artificial Life with Foundation Models Video understanding with large language models: A survey

Reference 2019

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Observation 9e6e52e3-ae7e-4ead-9bdc-46754aab948a · outbound

This paper cites Goodhart's Law in Reinforcement Learning.

Automating the Search for Artificial Life with Foundation Models Goodhart's Law in Reinforcement Learning

Reference 2020

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Observation 097530ce-923c-47ef-941e-f1b340814b1a · outbound

This paper cites Lenia - Biology of Artificial Life.

Automating the Search for Artificial Life with Foundation Models Lenia - Biology of Artificial Life

Reference 2021

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Observation 74021077-a3af-49ca-977f-342bf5544816 · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Automating the Search for Artificial Life with Foundation Models The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 2022

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Observation 50e5081a-cfe0-4a9d-92bd-381d9598fc96 · outbound

This paper cites AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence.

Automating the Search for Artificial Life with Foundation Models AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

Reference 2023

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Automating the Search for Artificial Life with Foundation Models OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code

Reference 2024

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Pith citing papers

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Extracting Interaction Kernels for Many-Particle Systems by a Two-Phase Approach cites this paper.

Extracting Interaction Kernels for Many-Particle Systems by a Two-Phase Approach Automating the Search for Artificial Life with Foundation Models

Reference 17

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Evolution and The Knightian Blindspot of Machine Learning cites this paper.

Evolution and The Knightian Blindspot of Machine Learning Automating the Search for Artificial Life with Foundation Models

Reference 105

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Humans Coexist, So Must Embodied Artificial Agents cites this paper.

Humans Coexist, So Must Embodied Artificial Agents Automating the Search for Artificial Life with Foundation Models

Reference 74

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Adversarial Coevolutionary Illumination with Generational Adversarial MAP-Elites cites this paper.

Adversarial Coevolutionary Illumination with Generational Adversarial MAP-Elites Automating the Search for Artificial Life with Foundation Models

Reference 42

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Observation 4a36f904-4327-4134-83e2-2467ed870144 · inbound

Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics cites this paper.

Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics Automating the Search for Artificial Life with Foundation Models

Reference 2015

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Observation 60c9f10a-3287-477d-85e1-b185115e19b7 · inbound

AutomataGPT: Forecasting and Ruleset Inference for Two-Dimensional Cellular Automata cites this paper.

AutomataGPT: Forecasting and Ruleset Inference for Two-Dimensional Cellular Automata Automating the Search for Artificial Life with Foundation Models

Reference 38

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Observation 65192aa6-09b9-47e5-98ac-a43810506970 · inbound

Participatory Evolution of Artificial Life Systems via Semantic Feedback cites this paper.

Participatory Evolution of Artificial Life Systems via Semantic Feedback Automating the Search for Artificial Life with Foundation Models

Reference 5

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ZapGPT: Free-form Language Prompting for Simulated Cellular Control cites this paper.

ZapGPT: Free-form Language Prompting for Simulated Cellular Control Automating the Search for Artificial Life with Foundation Models

Reference 12

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Observation 84515a05-c2d0-4fce-abae-f715905269d2 · inbound

Cultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies cites this paper.

Cultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies Automating the Search for Artificial Life with Foundation Models

Reference 48

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