Closing the Loop: How Semantic Closure Enables Open-Ended Evolution
- URL: http://arxiv.org/abs/2404.04374v6
- Date: Fri, 27 Jun 2025 12:06:31 GMT
- Title: Closing the Loop: How Semantic Closure Enables Open-Ended Evolution
- Authors: Amahury J. López-Díaz, Carlos Gershenson,
- Abstract summary: This manuscript explores the evolutionary emergence of semantic closure.<n>It integrates concepts from relational biology, physical biosemiotics, and ecological psychology into a unified computational enactivism framework.
- Score: 0.5755004576310334
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This manuscript explores the evolutionary emergence of semantic closure -- the self-referential mechanism through which symbols actively construct and interpret their own functional contexts -- by integrating concepts from relational biology, physical biosemiotics, and ecological psychology into a unified computational enactivism framework. By extending Hofmeyr's (F, A)-systems -- a continuation of Rosen's (M, R)-systems -- with temporal parametrization and multiscale causality, we develop a model capable of capturing critical life properties, including autopoiesis, anticipation, and adaptation. We then establish a formal equivalence between our extended (F, A)-systems and swarms of communicating automata, resolving self-referential challenges concerning the realizability of relational models. Our stepwise model traces the evolution of semantic closure from simple reaction networks that recognize regular languages to self-replicating chemical systems with memory and anticipatory capabilities, identifying self-reference as necessary for robust self-replication and open-ended evolution. Such a computational enactivist perspective underscores the essential necessity of implementing symbol-matter transformations into computing architectures, providing a cohesive theoretical basis for a recently proposed trialectic between autopoiesis, anticipation, and adaptation to solve the problem of relevance realization. Thus, our work opens pathways to new models of computation for life, agency and cognition, offering fundamental principles underlying biological information processing.
Related papers
- A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
arXiv Detail & Related papers (2025-07-28T17:59:05Z) - Computational Irreducibility as the Foundation of Agency: A Formal Model Connecting Undecidability to Autonomous Behavior in Complex Systems [0.0]
we establish precise mathematical connections, proving that for any truly autonomous system, questions about its future behavior are fundamentally undecidable.<n>The findings have significant implications for artificial intelligence, biological modeling, and philosophical concepts like free will.
arXiv Detail & Related papers (2025-05-05T21:24:50Z) - Self-Organizing Graph Reasoning Evolves into a Critical State for Continuous Discovery Through Structural-Semantic Dynamics [0.0]
We show how agentic graph reasoning systems spontaneously evolve toward a critical state that sustains continuous semantic discovery.<n>We identify a subtle yet robust regime in which semantic entropy dominates over structural entropy.<n>Our findings provide practical strategies for engineering intelligent systems with intrinsic capacities for long-term discovery and adaptation.
arXiv Detail & Related papers (2025-03-24T16:30:37Z) - GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.
Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.
It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
arXiv Detail & Related papers (2025-02-11T05:39:49Z) - No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology [5.925258390690544]
We argue that genuine "foundation models" of regulatory biology will remain out of reach unless guided by frameworks that integrate mechanistic insight with principled experimental design.
We present one such ground-up, semi-mechanistic framework that unifies perturbation-based experimental designs.
arXiv Detail & Related papers (2025-01-31T14:43:16Z) - No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process.
Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system.
Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
arXiv Detail & Related papers (2025-01-30T18:36:48Z) - Rethinking Cognition: Morphological Info-Computation and the Embodied Paradigm in Life and Artificial Intelligence [1.14219428942199]
This study aims to place Lorenzo Magnanis Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition.
We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organisation, and autopoiesis across physical, chemical, and biological domains.
arXiv Detail & Related papers (2024-12-01T10:04:53Z) - Emergence of Implicit World Models from Mortal Agents [0.276240219662896]
We discuss the possibility of world models and active exploration as emergent properties of open-ended behavior optimization in autonomous agents.
In discussing the source of the open-endedness of living things, we start from the perspective of biological systems as understood by the mechanistic approach of theoretical biology and artificial life.
arXiv Detail & Related papers (2024-11-19T07:43:30Z) - Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition [64.56321246196859]
We propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework.<n>We first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information.<n>We introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes.
arXiv Detail & Related papers (2024-11-18T05:16:11Z) - Deep Signature: Characterization of Large-Scale Molecular Dynamics [29.67824486345836]
Deep Signature is a novel computationally tractable framework that characterizes complex dynamics and interatomic interactions.
Our approach incorporates soft spectral clustering that locally aggregates cooperative dynamics to reduce the size of the system, as well as signature transform to provide a global characterization of the non-smooth interactive dynamics.
arXiv Detail & Related papers (2024-10-03T16:37:48Z) - Bayesian Theory of Consciousness as Exchangeable Emotion-Cognition Inference [5.234742752529437]
This paper proposes a unified framework in which consciousness emerges as a cycle-consistent, affectively anchored inference process.<n>We formalize emotion as a low-dimensional structural prior and cognition as a specificity-instantiating update.<n>This emotion-cognition cycle minimizes joint uncertainty by aligning emotionally weighted priors with context-sensitive cognitive appraisals.
arXiv Detail & Related papers (2024-05-17T17:06:19Z) - LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z) - Causal machine learning for single-cell genomics [94.28105176231739]
We discuss the application of machine learning techniques to single-cell genomics and their challenges.
We first present the model that underlies most of current causal approaches to single-cell biology.
We then identify open problems in the application of causal approaches to single-cell data.
arXiv Detail & Related papers (2023-10-23T13:35:24Z) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - Large Language Models for Scientific Synthesis, Inference and
Explanation [56.41963802804953]
We show how large language models can perform scientific synthesis, inference, and explanation.
We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature.
This approach has the further advantage that the large language model can explain the machine learning system's predictions.
arXiv Detail & Related papers (2023-10-12T02:17:59Z) - Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT)
Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives.
This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
arXiv Detail & Related papers (2023-10-03T05:40:56Z) - Interactive Molecular Discovery with Natural Language [69.89287960545903]
We propose the conversational molecular design, a novel task adopting natural language for describing and editing target molecules.
To better accomplish this task, we design ChatMol, a knowledgeable and versatile generative pre-trained model, enhanced by injecting experimental property information.
arXiv Detail & Related papers (2023-06-21T02:05:48Z) - Towards Predicting Equilibrium Distributions for Molecular Systems with
Deep Learning [60.02391969049972]
We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
arXiv Detail & Related papers (2023-06-08T17:12:08Z) - Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks.
Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Discovery of structure-property relations for molecules via
hypothesis-driven active learning over the chemical space [0.0]
We introduce a novel approach for the active learning over the chemical spaces based on hypothesis learning.
We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data.
This approach combines the elements from the symbolic regression methods such as SISSO and active learning into a single framework.
arXiv Detail & Related papers (2023-01-06T14:22:43Z) - Evolution TANN and the discovery of the internal variables and evolution
equations in solid mechanics [0.0]
We propose a new approach which allows, for the first time, to decouple the material representation from the incremental formulation.
Inspired by the Thermodynamics-based Artificial Neural Networks (TANN) and the theory of the internal variables, the evolution TANN (eTANN) are continuous-time.
Key feature of the proposed approach is the discovery of the evolution equations of the internal variables in the form of ordinary differential equations.
arXiv Detail & Related papers (2022-09-27T09:25:55Z) - A Molecular Multimodal Foundation Model Associating Molecule Graphs with
Natural Language [63.60376252491507]
We propose a molecular multimodal foundation model which is pretrained from molecular graphs and their semantically related textual data.
We believe that our model would have a broad impact on AI-empowered fields across disciplines such as biology, chemistry, materials, environment, and medicine.
arXiv Detail & Related papers (2022-09-12T00:56:57Z) - Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
This paper considers an evolutionary approach to creating a cognitive functionality.
We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
arXiv Detail & Related papers (2022-07-02T12:41:32Z) - Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann
Machines [0.0]
We show how a biochemical computer can use intrinsic chemical noise to perform complex computations.
We also use our explicit physical model to derive thermodynamic costs of inference.
arXiv Detail & Related papers (2022-05-12T18:59:43Z) - Towards a Theory of Evolution as Multilevel Learning [0.0]
We apply the theory of learning to physically renormalizable systems in an attempt to develop a theory of biological evolution, including the origin of life, as multilevel learning.
We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable.
We show that these principles entail the major features of biological evolution, including replication and natural selection.
arXiv Detail & Related papers (2021-10-27T17:21:16Z) - Discovering Latent Causal Variables via Mechanism Sparsity: A New
Principle for Nonlinear ICA [81.4991350761909]
Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application.
We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
arXiv Detail & Related papers (2021-07-21T14:22:14Z) - Applications of the Free Energy Principle to Machine Learning and
Neuroscience [0.0]
We explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience.
We focus on predictive coding, a neurobiologically plausible process theory derived from the free energy principle.
Secondly, we study active inference, a neurobiologically grounded account of action through variational message passing.
Finally, we investigate biologically plausible methods of credit assignment in the brain.
arXiv Detail & Related papers (2021-06-30T22:53:03Z) - Immersive Interactive Quantum Mechanics for Teaching and Learning
Chemistry [0.0]
We show how an immersive learning setting could be applied to help students understand the core concepts of typical chemical reactions.
Our setting relies on an interactive exploration and manipulation of a chemical system; this system is simulated in real-time with quantum chemical methods.
arXiv Detail & Related papers (2020-11-06T09:37:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.