Journal Publications

L. Pio-Lopez, B. Hartl, M. Levin
“BraiNCA: brain-inspired neural cellular automata and applications to morphogenesis and motor control”, Conference on Artificial Life, ALIFE (2026) doi.org/10.1162/ISAL.a.1033.

M. Cvjetko, B. Hartl, M. Levin, C. Moulin-Frier, PY Oudeyer
“The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning”
Conference on Artificial Life, ALIFE (2026), doi.org/10.1162/ISAL.a.971

J. Cool, B. Hartl, M. Levin, S. Petti
“Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia”, Conference on Artificial Life, ALIFE (2026), doi.org/10.1162/ISAL.a.1012.

B. Hartl*, L. Pio-Lopez*, C. Fields, M. Levin(* authors contributed equally)
“Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems”, Physics of Life Reviews 58, 336-359 (2026) doi.org/10.1016/j.plrev.2026.06.009 (2026), arxiv:2601.14096,

A. Adel, J. Szmitek, B. Hartl, R. Wanzenböck, G. K. H. Madsen
“Guided adaptive diffusion: An evolutionary framework for multi-modal atomistic structure prediction”, Journal of Chemical Information and Modeling 66, 7414-7425 (2026), doi.org/10.1021/acs.jcim.6c00843, chemrxiv:15001299.

J. Ashford, B. Sakallioglu, M. Tataryn, A. Valerio, B. Hartl, L. Pio-Lopez, M. Cvjetko, R. Löffler, S. Nichele
“Symbiogenesis: from Barricelli’s Legacy to Collective Intelligence”, GECCO (2026), pp 1228–1236, doi.org/10.1145/3795101.3814707.

R. Maity, M. Huebl, J. Lemmel, B. Hartl, G. Kahl
“Emergent swimming strategies of a smart three-bead swimmer”, arXiv:2606.05984 (2026).

H. Hazan, Y. Zhang, B. Hartl, M. Levin
“A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need”, arXiv:2604.08749 (2026).

J. Ashford, M. Cvjetko, R. Löffler, B. Sakallioglu, A. Valerio, M. Tataryn, B. Hartl, L. Pio-Lopez, S. Nichele
“Evolving Symbiosis, from Barricelli’s Legacy to Collective Intelligence: A Simulated and Conceptual Approach”, ALICE 2026 Report, arxiv:2603.08463 (2026).

B. Hartl*, Y. Zhang*, H. Hazan*, M. Levin, (* authors contributed equally)
“Heuristically Adaptive Diffusion-Model Evolutionary Strategy”, Advanced Science, e11537 (2024-2026) with Supplementary Information.

B. Hartl, M. Levin, L. Pio-Lopez
“Neural cellular automata: applications to biology and beyond classical AI”, Physics of Life Reviews 56, 94-108 (2026) - arXiv:2509.11131 (2025), see also Notebook LM Podcast [DE].

L. Pio-Lopez*, B. Hartl*, M. Levin, (* authors contributed equally)
“Aging as a Loss of Goal-Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation”, Advanced Science 12, e09872 (2025) - OSF Preprints DOI: 10.31219/osf.io/m5bnx_v1 (2025), Preprints.org DOI: 10.20944/preprints202412.2354 (2024), see also Notebook LM Podcast [DE].

B. Hartl, M. Levin,
“What does evolution make? Learning in living lineages and machines”, Trends in Genetics 41(6), 480-496 (2025), see also OSF Preprints

B. Hartl, M. Levin, A. Zöttl
“Neuroevolution of Decentralized Decision-Making in N-Bead Swimmers Leads to Scalable and Robust Collective Locomotion”, Commun. Phys. 8, 194 (2025) as part of the Swarm intelligence - Collective motions from biology to robotic collection; also see arXiv:2407.09438,

Y. Zhang*, B. Hartl*, H. Hazan*, M. Levin, (* authors contributed equally)
“Diffusion Models are Evolutionary Algorithms”, in preceedings of the ICLR (2025), also see arXiv.2407.09438 (2024).
Social Media Posts: Paper Tweet, Gonzo ML@substack,
Audio: Discussion by Carlos E. Perez, papersread.ai, apple podcasts, open spotify.

B. Hartl, S. Risi, M. Levin,
“Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales”, Entropy 26(7), 532, (2024), OSF Preprint DOI: 10.31219/osf.io/sp9kf (2024),

B. Hartl, M. Mihalkovič, L. Šamaj, M. Mazars, E. Trizac, and G. Kahl,
“Ordered ground state configurations of the asymmetric Wigner bilayer system – revisited: an unsupervised clustering algorithm analysis”, The Journal of Chemical Physics 159, 204112 (2023), arXiv:2211.04985 (2022),

B. Hartl, M. Hübl, G. Kahl, and A. Zöttl,
“Microswimmers learning chemotaxis with genetic algorithms”, The Proceedings of the National Academy of Sciences 118 (19) e2019683118 (2021), arXiv:2101.12258 (2021)

B. Hartl, S. Sharma, O. Brügner, S.F.L. Mertens, M.Walter, and G. Kahl,
“Reliable Computational Prediction of the Supramolecular Ordering of Complex Molecules under Electrochemical Conditions”, The Journal of Chemical Theory and Computation 16 (8), 5227-5243 (2020), arXiv:1912.07397 (2019)

D. O. Krimer*, B. Hartl*, F. Mintert, and S. Rotter, (* authors contributed equally)
“Optimal control of non-Markovian dynamics in a single-mode cavity strongly coupled to an inhomogeneously broadened spin ensemble”, Physical Review A 96, 043837 (2017), arXiv:1701.06224 (2017)

D.O. Krimer, B. Hartl, and S. Rotter,
“Hybrid Quantum Systems with Collectively Coupled Spin States: Suppression of Decoherence through Spectral Hole Burning”, Physical Reveview Letters 115, 033601 (2015), arXiv:1501.03487 (2015)

Conferences and Events

• Invited keynote at the Foresight Institute (virtual; scheduled for November 2026)
• Co-organizer of the special session on Artificial Life for Science and Engineering; one poster and two talks by supervised students at ALIFE 2026, Waterloo, Canada (August 2026)
• Invited talk at the ERA Journal Club (virtual; June 2026)
• Invited talk at the Flowers Seminar at Inria (virtual; May 2026)
• ALICE Workshop, Copenhagen, Denmark (February 2026; runner-up project prize, with the resulting work published at GECCO 2026)
• Invited talk at the NeuroAI Seminar, TU Wien, Vienna, Austria (January 2026)
• Interactive station on Bio-Inspired AI at the AI Festival 2025, TU Wien, Vienna, Austria
• Invited talk at the ALIFE 2025 ERA Workshop, Kyoto, Japan
• Invited talk at the ENCECON 2025 Workshop of Mind & Life Europe, Gomde, Scharnstein, Austria
• Invited talk at the Ludwig Boltzmann Seminar for Network Medicine, CeMM, Vienna, Austria (2025)
• Talk at Woodstock.Bio² + Night Science, Prague, Czechia (2025)
• Invited talk at the Seminar of the Institute of Materials Chemistry (TACO), TU Wien, Vienna, Austria (2025)
• Invited talk at the ICLR 2025 Workshop on Deep Generative Models in Machine Learning: Theory, Principle and Efficacy, Singapore
• One talk and one poster at the Physics of Life Conference, Harrogate, UK (2025)
• Two posters at the 12th Liquid Matter Conference, Mainz, Germany (2024)
• Two accepted talks (one delivered) and two posters at the European Colloid and Interface Society Conference, Copenhagen, Denmark (2024)
• Talk at the Austrian-Slovenian HPC Meeting, Grundlsee, Austria (2024)
• Invited seminar at the Center for Artificial Intelligence and Machine Learning (CAIML), Vienna, Austria (2024)
• Participant at the International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA (2023)
• Volunteer at IJCAI–ECAI, Vienna, Austria (2022)
• Talk and poster at the 11th Liquid Matter Conference, Prague, Czechia (2020/2021)
• Co-organizer of the Proof Society Workshop and Winter School, Funchal, Madeira, Portugal (2021)
• Participant at the VDSP–ESI Winter School on Machine Learning in Physics, Vienna, Austria (2020)
• Invited talk at Kurt Gödel’s Legacy: Does the Future Lie in the Past?, Vienna, Austria (2019)
• Poster at the 14th International Conference on Quasicrystals, Kranjska Gora, Slovenia (2019)
• Invited seminar talk at the Laboratoire de Physique Théorique et Modèles Statistiques, Université Paris-Sud, France (2019)
• Co-organizer and lecturer at the Frauen in der Technik (FIT) Workshop, TU Wien, Vienna, Austria (2019)
• Questract presentation at the Workshop on Machine Learning and Reverse Engineering for Soft Materials, Leiden, the Netherlands (2018)
• Talk at the European Colloid and Interface Society Conference, Ljubljana, Slovenia (2018)
• Talk at the IMPRESS Workshop: Interfacing Machine Learning and Experimental Methods, Graz, Austria (2018)
• Poster at From Electrons to Phase Transitions – A ViCoM Conference, Vienna, Austria (2018)
• Co-organizer of the International Summer School for Proof Theory in First-Order Logic, Funchal, Madeira, Portugal (2017)
• Poster at the 10th Liquid Matter Conference, Ljubljana, Slovenia (2017)
• Invited seminar talk at the ITRG Seminar, Freiburg, Germany (2017)
• Invited talk at the ViCoM Lecture, Vienna, Austria (2016)
• Participant at the ESI School on Synergies between Mathematical and Computational Approaches to Quantum Many-Body Physics, Vienna, Austria (2016)
• Participant at the CECAM Workshop on Structure Formation in Soft Colloids, Vienna, Austria (2016)
• Participant at the 12th International Tbilisi Summer School in Logic and Language, Tbilisi, Georgia (2016)
• Invited seminar talk at Florian Mintert’s Quantum Information Theory Group, Imperial College London, UK (2015)
• Participant at the Complex Quantum Systems (CoQuS) Summer School, Vienna, Austria (2015)

Media Outreach

• Featured in Neural Cellular Automata Are Unlocking the Secrets of Biological and Artificial Intelligence, dailyneuron.com (2025)
• Featured in Self-Assembly Gets Automated in Reverse of ‘Game of Life’, Quanta Magazine (2025)
• How to swim without a brain, also see Comm. Phys. Metrics
• Sub auspiciis doctoral graduation, 26.01.2022 (TU Wien News, brief interview, ITP News, ORF)
• Wie man als Einzeller ans Ziel gelangt (Reaching your life goals as a single-celled organism), see entire media coverage
• Wie Moleküle Mosaike bilden (How molecules form mosaics)
• derStandard user-foto of the week #5: “Love breaks walls”
• DOC fellowship ceremony 2017

Other Literature

Some Good Reads

• Machine Learning – kurz & gut by Chi Nhan Nguyen and Oliver Zeigermann
• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• Neural networks and deep learning by Michael Nielsen
• Generative Deep Learning by David Foster
• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
• A high-bias, low-variance introduction to Machine Learning for physicists by P. Mehta et al
• The Book of Why: The New Science of Cause and Effect by J. Pearl and D. Mackenzie
• Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds by M. Levin
• Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology by M. Levin

Blogs and Blog-Posts

• David Ha’s Blog - Great stuff about evolutionary strategies in AI
• DINO and PAWS - Advancing the state of the art in computer vision with self-supervised Transformers and 10x more efficient training
• Same or Different? The Question Flummoxes Neural Networks. in Quantamagazine
• The Computer Scientist Training AI to Think With Analogies in Quantamagazine
• Hopfield Networks is All You Need by the Sepp Hochreiter-group
• The Illustrated Transformer by Jay Alammar

Channels and Lectures

• Michael Levin’s Academic Content - Mindblowing stuff
• Yannic Kilcher’s Channel - Awesome communication of ML literature
• Machine Learning for Physicists by Florian Marquardt