paper:doi-10-1016-j-tree-2015-11-009How can evolution learn?
Related work— refs + corpus + external arXiv
Cited / in-corpus / arXiv badges show which signals surfaced each row. Multi-source rows weighted higher.
- General Mechanism of Evolution Shared by Proteins and WordsHsing-Yi Lai, Sun-Ting Tsai, Chen Siang Ng, Kevin Sheng-Kai Ma, Shan-Jyun Wu, Meng-Xue Tsai, Yi-Ching Su, Daw-Wei Wang, and Tzay-Ming Hong Li-Min Wang2026≈ 72%
- ≈ 71%
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- The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamicsHideaki Shimazaki2019≈ 70%
- ≈ 70%
- Emergent social transmission of model-based representations without inferenceMiriam Bautista-Salinero, Claudio Tennie and Charley M. Wu Silja Ke{\ss}ler2026≈ 69%
- The Origin of Life in the Light of EvolutionTom A. Williams, Laura Eme, Johann Peter Gogarten, Patricia Sanchez-Baracaldo, Anja Spang, Frank O. Aylward, Michael Travisano, Paula V. Welander, Julie A. Huber, Vaughn S. Cooper, Paul E. Turner, Timothy W. Lyons, Andrew D. Ellington, Shelley D. Copley, Eugene V. Koonin, Michael Lynch Bet\"ul Ka\c{c}ar2026≈ 69%
- Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary SearchL.B. Soros, Olaf Witkowski Kevin Frans2021≈ 69%
- A variational synthesis of evolutionary and developmental dynamicsDaniel Ari Friedman, Axel Constant, V. Bleu Knight, Thomas Parr, John O. Campbell Karl Friston2023≈ 69%
- Learning in embodied action-perception loops through explorationDaniel Y. Little and Friedrich T. Sommer2011≈ 68%
- Learning to Theorize the World from ObservationGyubin Lee, Junyeob Baek, Hosung Lee, Sungjin Ahn Doojin Baek2026≈ 68%
- Learning to Infer Program SketchesLuke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama Maxwell Nye2019≈ 68%
- Learning without neurons in physical systemsin corpus2022≈ 68%
- Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge ExplorationDongyang Ma, Tianqing Fang, Jia Li, Jing Tang, Nuo Chen, Haitao Mi, Yan Wang Qifan Zhang2026≈ 68%
- How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data StructuresSayamindu Dasgupta, Benjamin Mako Hill Ruijia Cheng2026≈ 67%
- Workspace Optimization: How to Train Your AgentGal Kaplun, Ron Banner, Daniel Soudry, Boris Ginsburg Elad Sarafian2026≈ 67%
- Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individualityin corpus2022≈ 64%
- Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Gluein corpus2024≈ 63%
- ≈ 63%
- ≈ 63%
- ≈ 62%
- ≈ 62%
- Why Learning Requires Feelingin corpus2026≈ 62%
- ≈ 62%
- The biogenic approach to cognitionin corpus2005≈ 61%
- ≈ 61%
- ≈ 61%
- The Machine Consciousness Hypothesisin corpus≈ 61%
- Darwin's agential materials: evolutionary implications of multiscale competency in developmental biologyin corpus2023≈ 60%
- ≈ 60%
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Cellular collectives exhibit goal-directed competency that is substrate-independent, composition-independent, and origin-independent — a property Clawson and Levin term teleonomy — and this invariant,
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TAME—Technological Approach to Mind Everywhere—formalizes a non-binary, empirically grounded framework for recognizing, comparing, and manipulating cognition across radically diverse substrates, from
- The computational boundary of a 'self': developmental bioelectricity drives multicellularity and scale-free cognition
Scale-Free Cognition, the framework introduced here, proposes that any coherent Self is demarcated by a 'cognitive light cone'—a spatio-temporal boundary of events a system can measure, model, and att
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Watson and Levin argue that evolutionary individuality, organismic individuality, and cognition are coextensive — the causal structures necessary to produce fitness that belongs to a collective rather