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REVIEW 4 major objections 6 minor 7 cited by

Automating the Search for Artificial Life with Foundation Models

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Automated Search for Artificial Life (ASAL) claims that vision-language foundation models can automate the discovery of lifelike simulations, finding target phenomena, temporally open-ended novelty, and diverse simulation landscapes…

desk verdict ASAL is a solid, well-engineered method paper that brings vision-language FMs into ALife search, but its strongest claims about open-endedness and newly discovered lifeforms outrun the validation. read the letter →

arxiv 2412.17799 v2 pith:UV3AK3H7 submitted 2024-12-23 cs.AI cs.NE

Akarsh Kumar , Chris Lu , Louis Kirsch , Yujin Tang , Kenneth O. Stanley , Phillip Isola , David Ha This is my paper · ORCID
classification cs.AIcs.NE
keywords artificiallifefoundationmodelsCLIPopen-endednesssimulationsearchcellularautomataLeniaBoids
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to replace manual trial-and-error in artificial-life research with automated search driven by foundation models. Its central claim is that a vision-language model that embeds rendered simulation frames can judge which simulations look like cells, organisms, or ecosystems, stay persistently interesting over time, or fill a diverse atlas of behaviors. If true, this would let researchers specify what they want to see, such as 'a self-replicating pattern', and let the search find the simulation rules that produce it, across substrates as different as Boids, Lenia, and cellular automata. The payoff would be a general, substrate-agnostic pipeline for discovering lifelike worlds and for quantifying qualities like open-endedness that have resisted formal definition.

What carries the argument

The load-bearing object is the vision-language embedding of a rendered simulation frame. CLIP, and in the ablation DINOv2, maps each frame to a representation space where text prompts can be compared with images; ASAL then defines three objectives in that space: text-image alignment for targets, historical nearest-neighbor similarity for open-endedness, and nearest-neighbor distance for illumination. A renderer is part of the substrate parameterization for cases like Neural Cellular Automata where the raw state is not visually interpretable. The search optimizers are Sep-CMA-ES for single targets, Adam with backpropagation through time for temporal NCA targets, brute force for the 262,144 life-like cellular automata, and a custom genetic algorithm for illumination.

What would settle it

A blind human-preference study would settle the load-bearing assumption. Take the highest-scoring open-ended cellular automata from the brute-force search and an equal number of randomly chosen automata, render their rollouts, and ask human raters which are more interesting or more lifelike. If preference does not track the ASAL open-endedness score, or if humans find the random automata just as engaging, then the claimed human alignment of the foundation-model representation is not doing the work.

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Extended reading notes

Core claim

ASAL is a three-mode search procedure. In supervised-target mode it maximizes the CLIP similarity between a rendered simulation frame and a text prompt, optionally at multiple timesteps to produce temporal sequences. In open-endedness mode it minimizes the similarity between each frame and its closest historical frame in CLIP space, treating sustained historical novelty as the signature of open-endedness. In illumination mode it searches for a population of simulations whose frames are far from their nearest neighbor in CLIP space. The paper reports that this procedure rediscovers known lifeforms, finds previously unseen Lenia and Boids organisms, and identifies life-like cellular automata whose CLIP-space trajectories keep diverging, which the authors describe as open-ended in the same sense as Conway's Game of Life. It also uses the same embeddings to quantify nonlinearity in parameter interpolation, the 'more is different' particle-number threshold in Particle Life, parameter sensitivity, and the time at which a Lenia simulation plateaus.

Load-bearing premise

The method assumes that the internal representation a vision-language model builds of a rendered simulation frame matches what a human would find interesting, novel, or diverse, so that maximizing novelty or diversity in that representation yields genuinely lifelike discoveries instead of patterns that merely fool the model.

Editorial extensions

If this is right

  • Researchers can request a phenomenon in plain language, such as 'a caterpillar' or 'a network of neurons', and recover a simulation that produces it, as shown in Lenia, Boids, and Particle Life.
  • Searching for historical novelty in CLIP space can serve as a concrete, computable stand-in for open-endedness on any substrate that renders to images.
  • Illumination produces a 'simulation atlas' that maps the space of possible behaviors of a substrate, organized by visual similarity rather than by hand-picked descriptors.
  • The same embedding-based similarity can become a quantitative instrument: measuring parameter nonlinearity, ranking parameter importance, and detecting when a simulation has reached a static plateau.
  • Because the method is agnostic to the choice of foundation model and substrate, the same search recipes can be re-run as better vision models and new substrates appear.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next test is whether the open-endedness score tracks human judgments: if human raters are shown pairs of cellular automata and asked which keeps getting more interesting, their choices should match the CLIP-space novelty ranking for the central claim to carry over to perceived open-endedness.
  • The same pipeline could be applied outside ALife, for example to search hypergraph update rules or physical model parameters for behavior that an embedding model finds natural, since the only requirement is a renderable state and an embedding space.
  • Temporal prompts may be a route to directed counterfactual evolution: specifying a sequence like 'primordial soup', then 'predator-prey', then 'alien animals' tests whether a substrate can host a chosen evolutionary trajectory, not just a single final state.
  • If future video-language models replace CLIP, the open-endedness and illumination objectives could be computed on whole trajectories rather than single final frames, which would likely sharpen the distinction between genuinely open-ended and merely varied simulations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces ASAL, a framework that uses vision-language foundation models (CLIP, DINOv2) to search for artificial life simulations. Three search mechanisms are proposed: supervised target search via text prompts (Eq. 2), open-endedness search via historical nearest-neighbor novelty in FM embedding space (Eq. 3), and illumination via nearest-neighbor diversity (Eq. 4). The method is applied to Boids, Particle Life, Game of Life/Life-like CA, Lenia, and Neural Cellular Automata. The authors report discovery of target phenomena, previously unseen Lenia and Boids forms, Life-like CAs that are open-ended like Conway's Game of Life, and simulation atlases. Additional FM-based metrics quantify nonlinearity, parameter sensitivity, and plateau behavior.

Significance. If validated, ASAL would be a significant advance: it is substrate-agnostic, requires no hand-crafted objectives beyond a pretrained FM, and covers three distinct search goals. The paper ships code, presents an exhaustive brute-force evaluation of all 262,144 Life-like CAs for the open-endedness objective, and demonstrates qualitative success across diverse substrates. The FM-based quantification in Section 4.4 is a useful contribution. However, the strongest claims—open-endedness and 'previously unseen' lifeforms—are validated only through CLIP embedding statistics and qualitative inspection, not through human judgments or independent benchmarks. The contribution is currently a promising proof of concept rather than a fully established result.

major comments (4)
  1. [Section 3.2, Eq. (3); Section 4.2] The central claim that ASAL discovers cellular automata that are open-ended 'like Conway's Game of Life' is supported exclusively by the historical CLIP-novelty score in Eq. (3). Since Conway's Game of Life is ranked by that same score (top 5% in Figure 5c), the finding is metric-relative, and no independent validation shows that the score tracks human perceptions of open-endedness or known OE properties such as unbounded growth, glider production, or computational universality. The paper is transparent that the subjectivity is outsourced to the representation, but a human rating study or a comparison with established complexity/open-endedness indicators (e.g., Shannon entropy over time, diversity of emergent attractors) is necessary to justify the wording 'open-ended like Conway's Game of Life'.
  2. [Section 4.3 and 4.1; Appendix B.1] The claims of discovering 'previously unseen Lenia and Boids lifeforms' are not checked against an exhaustive inventory of known forms (e.g., Chan's Lenia expanded universe or LeniaBreeder's catalog), and the supporting evidence consists of qualitative screenshots. In addition, the supervised-target and illumination experiments use a single initial state seed per evaluation (Appendix B.1) and report no quantitative diversity or novelty metrics. To make the novelty claim load-bearing, the authors should either compare against a systematic catalog or provide quantitative evidence that the discovered final states lie outside previously documented behavioral classes.
  3. [Section 4.5, Figure 8] The foundation-model ablation for illumination reports no numeric results; the text states only that 'CLIP seems slightly better than DINOv2' and that both are 'qualitatively better' than pixel representations. Given that the paper's core premise is the advantage of FM representations, the ablation should include a quantitative measure of coverage, diversity, or human-rated quality of the illuminated atlas. As written, the ablation does not substantiate the claimed importance of the FM choice.
  4. [Appendix B.2; Section 4.2] The open-endedness ranking of Life-like CAs depends on several procedural choices—32 subsampled timesteps from a 2,048-step rollout, 256 random initial states with sparsity sampled from U(0.05, 0.4), and a single CLIP ViT variant—but no sensitivity analysis is provided. Since the headline result is a ranking that places Conway's Game of Life only in the top 5%, it is important to show that the ranking is stable under changes in timestep subsampling and initial-state sparsity; otherwise the specific top-ranked rules in Figure 5a may be artifacts of these choices.
minor comments (6)
  1. [Section 3.3] Typo: 'diverse phonenema' should be 'diverse phenomena'.
  2. [Equation (4)] Grammar: 'an "diversity" score' should be 'a "diversity" score'.
  3. [Section 3.1, Figure 2] The phrase 'colors match the equation below' is not meaningful in a black-and-white print version; please add explicit labels or markers instead.
  4. [Section 4.4] Figure 7a reports CLIP similarity values but provides no baseline or calibration to indicate what counts as high or low similarity; adding a random-pair baseline would help interpretation.
  5. [Section 4.4(d)] The plateau-halting metric in Figure 7d is presented for a single Lenia simulation; a few examples or a quantitative threshold would strengthen the claimed utility as a halting condition.
  6. [Appendix A] The Lenia substrate description notes that the search space is centered at a solution from Chan (2020), but the anchor and its bounds are not specified; this makes the search space definition incomplete for replication.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ASAL's objectives are explicit operational definitions, and the Conway's Game of Life ranking is an independent brute-force result, not a fitted input.

full rationale

The paper's three objectives are defined directly in terms of CLIP/DINOv2 embeddings (Eqs. 2-4), so statements such as "open-endedness" or "diversity" are transparently operationalized rather than derived from hidden inputs. The Conway's Game of Life result is obtained by brute-force evaluation of all 262,144 Life-like CAs against the pre-specified Eq. 3 metric; no parameter is fit to make Conway rank highly, and the benchmark is external to the optimized objectives. The assumption that FM embeddings approximate human perception is stated as an assumption (Section 3.2: "the vision-language FM representations act as a proxy for a human's representation"), supported by independent perceptual-similarity work and tested only qualitatively in the ablation, but an untested assumption is a validation gap, not a circular reduction. Self-citations to prior novelty-search and perceptual-similarity work provide background and are not used to forbid alternatives or to import a uniqueness theorem. The paper therefore exhibits no Eq.-to-Eq. reduction in which an output is equivalent to its input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim relies on the assumption that foundation model embeddings are human-aligned evaluators of lifelike behavior, plus several domain-specific design choices. No new physical entities are introduced.

free parameters (4)
  • Open-endedness timestep subsampling = 32
    Equation 3 computes the open-endedness score over 32 subsampled timesteps, a hand-chosen value that affects which cellular automata rank as open-ended.
  • Illumination pruning neighbor count = 2
    The genetic algorithm in Appendix B.3 removes solutions with the smallest average distance to their two nearest neighbors, deviating from the single-nearest-neighbor diversity in Equation 4 and shaping the resulting atlases.
  • Life-like CA initial sparsity = U(0.05, 0.4)
    Open-endedness ranking depends on the random initial state distribution; the chosen sparsity range is a design decision that may influence which rules appear novel.
  • Lenia search-space anchor = Chan (2020) solution
    The Lenia substrate is centered on a known solution from Chan, bounding the search space and affecting what new lifeforms can be discovered.
assumptions (4)
  • domain assumption CLIP and DINOv2 embeddings of rendered simulation frames are a valid proxy for human judgments of interest, novelty, and diversity.
    Invoked throughout Sections 3.2 and 4.5; the FM ablation compares against pixel distance, not human ratings, so alignment with human perception is assumed rather than measured.
  • domain assumption Historical novelty in a fixed representation space captures open-endedness.
    Section 3.2 states this explicitly, citing novelty search and open-endedness literature, while acknowledging that open-endedness is contested and hard to define.
  • domain assumption Rendering a simulation state as a single image preserves enough information for the FM to judge the simulation.
    Section 3 assumes any substrate can be displayed as an image; temporal phenomena are evaluated only through rendered frames, which may lose dynamical information.
  • domain assumption The substrate parameterizations used are representative enough that discovered configurations are genuinely novel lifeforms.
    Sections 4.1 to 4.3 assert 'previously unseen' lifeforms without an exhaustive catalog baseline of all known Lenia and Boids configurations.

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Cite this review

Pith. "Pith review of Automating the Search for Artificial Life with Foundation Models." pith.science (2026). https://pith.science/paper/UV3AK3H7

@misc{pith2026241217799,
  author       = {Pith},
  title        = {Pith review of: Automating the Search for Artificial Life with Foundation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UV3AK3H7}},
  note         = {Machine review of arXiv:2412.17799}
}
read the original abstract

With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial-and-error to discover the configurations of lifelike simulations. This paper presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called Automated Search for Artificial Life (ASAL), (1) finds simulations that produce target phenomena, (2) discovers simulations that generate temporally open-ended novelty, and (3) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids lifeforms, as well as cellular automata that are open-ended like Conway's Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.

Figures

Figures reproduced from arXiv: 2412.17799 by the authors.

Figure 1
Figure 1. Overview: Our method, ASAL, searches for interesting ALife simulations by using a vision￾language foundation model to evaluate the simulation’s produced videos. ALife lifeforms are discovered across different substrates with three different mechanisms: (1) found via a text prompt, (2) found via searching for open-ended simulations, and (3) illuminating a set of diverse simulations. Abstract With the recent Nobel Pri… view at source ↗
Figure 2
Figure 2. ASAL: Our proposed framework, ASAL, uses vision-language foundation models to discover ALife simulations by formulating the processes as three search problems. Supervised Target: To find target simulations, ASAL searches for a simulation which produces a trajectory in the foundation model space that aligns with a given sequence of prompts. Open-Endedness: To find open-ended simulations, ASAL searches for a simulatio… view at source ↗
Figure 3
Figure 3. Discovered target simulations: Using Equation 2, ASAL discovered simulations that result in a final state which matches the specified prompt. Results are shown for three different substrates. More prompts can be seen in appendix Figures 12, 13, and 14 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Discovered temporal target simulations: Using Equation 2, ASAL discovered simulations that produce a sequence of events which match a list of prompts. The second row shows how the first simulation generalizes to a different initial state. The results are shown for the …
Figure 5
Figure 5. Figure 5: Discovered open-ended simulations: Using Equation 3, ASAL discovered open-ended simu￾lations in the Life-Like CAs substrate. Simulations are labeled in Golly notation [Eppstein, 2010] to denote the number of living neighbors required for birth and survival. (a) The dis…
Figure 6
Figure 6. Figure 6: Simulation Atlas: ASAL discovered a large set of diverse simulations by using the illumination algorithm from Equation 4 on the Lenia and Boids substrates. The resulting final states of these simulations are then embedded using CLIP and projected into 2-D with UMAP [Mc…
Figure 7
Figure 7. Figure 7: Quantifying ALife: Foundation Models allow human-aligned quantification of previously quali￾tative phenomena. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Importance of Foundation Models: The FM is ablated in the illumination experiments. CLIP seems to be slightly better than DINOv2 in creating human-aligned diversity, but both are significantly better than a pixel based representations. 4.5 Agnostic to Foundation Model …
Figure 9
Figure 9. Figure 9: The training curve for the supervised target experiments. The y-axis measures the negative score [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 10
Figure 10. Figure 10: The open-endedness score from Equation 3 for all the life-like CAs. The left subplot shows how [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: The training curve for the illumination experiments. The y-axis measures the diversity of the [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]
Figure 12
Figure 12. Figure 12: Extra supervised targets in Lenia. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]
Figure 13
Figure 13. Figure 13: Extra supervised targets in Boids. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Extra supervised targets in Particle Life. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]
Figure 16
Figure 16. Figure 16: Simulation Atlases for the Life-Like CA substrate. Top: Atlas of the main cluster from Figure 5c. [PITH_FULL_IMAGE:figures/full_fig_p027_16.png]
Figure 17
Figure 17. Figure 17: Large Simulation Atlas for Lenia. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_17.png]
Figure 18
Figure 18. Figure 18: Large Simulation Atlas for Boids. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_18.png]
Figure 19
Figure 19. Figure 19: Illumination of Particle Life. 30 [PITH_FULL_IMAGE:figures/full_fig_p030_19.png]

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.