Skip to content
#protein folding Open access

Fractal Compact Manifold Theory Core Mechanics: Multifractal Unification of Quantum Mechanics, Gravity, and Consciousness

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Advanced Mathematical Theories and Applications

Abstract

Abstract This sub-paper, "Fractal Compact Manifold Theory Core Mechanics" serves as a concise, standalone distillation of FCMT's foundational elements, emphasizing the atemporal symmetry breaking, field emergence, Lagrangian formulation, projection operator, and foliation map. By focusing solely on these core mechanics—without delving into the full theory's extensions like general relativity recovery, timeless quantum applications, gauge structures, or empirical predictions—it aims to provide a more accessible entry point, reducing the reading commitment from the comprehensive 200-page main document to a targeted exploration of the theory's essence. Fractal Compact Manifold Theory (FCMT) is a unified framework for quantum mechanics, gravity, electromagnetism, and consciousness, built from atemporal symmetry breaking in a multifractal configuration space. The theory begins in a pre-perturbation state: a stable unified field on a compact manifold whose maximum multifractal dimension is *d*_max ≈ 4.01–4.02. That slight excess over four is required both to recover ordinary four-dimensional physics in the infrared and to supply the scale-dependent measure that keeps loop integrals finite. The field is governed by the quadratic potential V(φ_unified) = ½ m² φ_unified² with m² > 0. An atemporal quantum fluctuation then triggers symmetry breaking and differentiates the unified field into four fields: the informational field φ_info, which encodes self-similar structure; the consciousness field ψ_c, which carries the primordial awareness substrate; the entanglement field φ_e, which supplies non-local correlations; and the Higgs field φ_H, which generates mass. There is one variational structure. It is the action. Inside that action already sits the Shannon term *S*_info = −∑ *p*_ij log *p*_ij. Consciousness-Modulated Informational Entropy Minimization is that term, not a second principle standing beside the action. Isolating ψ_c as the field that weights the Shannon term produces the control Hamiltonian H = λ · f(p, ψ_c) − *S*_info[p]. Pontryagin’s necessary conditions are the Euler–Lagrange equation of the same action written so that the role of the control is explicit. When multifractal spectra rather than a single Shannon functional are required, the same construction applies to the Rényi family *H*_q. The principle does not change. A renormalized projection operator Proj_d^R then foliates the atemporal configuration space into ordinary four-dimensional Lorentzian hypersurfaces. Relational time emerges from the renormalization-group flow that accompanies the projection. Gravity arises as the geometric response to ψ_c-orchestrated clustering of the stress-energy; in the low-consciousness limit the multifractal corrections vanish and the classical Einstein equations are recovered exactly. The same residual symmetry of the entanglement field that produces its transverse-traceless two-point function also yields a massless vector mode whose projected dynamics reproduce Maxwell’s equations, so electromagnetism appears as a controlled consequence of the identical breaking that generates φ_e. In the high-energy sector the multifractal measure and the running intermittency γ(k) generate a Gaussian hard form factor that renders the spectrum finite. There is no infinite linear Regge trajectory. The effective cutoff Λ_R is restricted by three matching conditions, all built from functions already present in the architecture, to a window of roughly 3–30 TeV: the form factor is anchored to the same intermediate dimensionality already used for the consciousness-field length; suppression is required once γ(k) falls below 10⁻³; and suppression is required once the running projection kernel has narrowed enough that non-local comparison ceases to be effective. Those three conditions share the architecture. They are not three independent theories of the cutoff. The projection framework also generates the principal structural features of the Standard Model — three fermion generations from discrete scale bands, hierarchical Yukawa couplings from the running kernel, and the gauge group from residual transformations of the entanglement field — rather than inserting them by hand. Three faces of the same Lagrangian, plus one empirical lock, return one characteristic length for the consciousness field. The effective mass of ψ_c on the infrared slice *d*_i ≈ 3.3, the running width of the projection kernel on that slice, and the feedback coupling κ_c / *v*_IR² ≈ 0.06 share those two anchors and give the Compton length λ_ψc ≈ 1.5 μm (window 1.1–1.9 μm). A fourth contact is empirical rather than calculational: the optical and near-infrared member of the microtubule resonance hierarchy, and the Fröhlich condensate it supports, already sit at that length. That is a lock, not a fourth independent derivation. The length coincides with the characteristic size of large protein complexes, cytoskeletal bundles, and dendritic spines — the regime in which living systems must maintain order against thermal noise. FCMT therefore treats consciousness as a fundamental field that participates in the generation of spacetime, the emergence of electromagnetism, the finiteness of the high-energy spectrum, and the selection of low-informational-entropy configurations. Ordinary quantum phenomena and classical gravity appear as controlled projections of one atemporal stationarity condition. The framework yields sharp, testable signatures: a Higgs-consciousness Yukawa coupling |*y*_h| = 0.0153 ± 0.0022, consistent with public LHC limits as of November 2025; essentially null running of the CMB spectral index α_s ≈ 0; neural coherence times of order τ_d = 10⁻⁴ s; and a size-scanned search for enhanced order-maintenance or coherence in the window 0.5–3 μm, with the predicted peak at 1.5 μm. Consciousness is not emergent. It is the field that folds the universe.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

Affects--emotions and moods--have an impact on cognitive processing activities and the working performance of individuals. It has been established that software development tasks are undertaken through cognitive processing activities. Therefore, we have proposed to employ psychology theory and measurements in software engineering (SE) research. We have called it "psychoempirical software engineering". However, we found out that existing SE research has often fallen into misconceptions about the affect of developers, lacking in background theory and how to successfully employ psychological measurements in studies. The contribution of this paper is threefold. (1) It highlights the challenges to conduct proper affect-related studies with psychology; (2) it provides a comprehensive literature review in affect theory; and (3) it proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds; firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startup’s progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#human-computer interacti... Open access May 2025

Linker-free PROTACs efficiently induce the degradation of oncoproteins

Proteolysis-targeting chimeras (PROTACs) present a potentially effective strategy against various diseases via selective proteolysis. How to increase the efficacy of PROTACs remains challenging. Here, we explore the necessity of the linker, which has been deemed as an integral part of heterobifunctional PROTACs. Adopting single amino acid-based degradation signals, we find that the linker is not a required feature of the PROTACs. Notably, the linker-free PROTAC, Pro-BA, exhibits superior efficacy over its linker-bearing counterparts in degrading EML4-ALK and inhibiting lung cancer cell growth, as Pro-BA induces a stronger interaction between the target and the E3 ubiquitin ligase. Pro-BA is a water-soluble, orally administered degrader that significantly inhibits the tumor growth in a xenograft mouse model. The broad applicability of this linker-free PROTAC strategy is further validated through the development of BCR-ABL degrader. Our study introduces a design paradigm for PROTACs, potentially facilitating the advancement of more efficient therapeutic degraders. Linkers are traditionally seen as important for PROTAC activity. Here, the authors demonstrate that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.

Jianchao Zhang, Congli Chen, Xiao Chen et al. · 41 citations
#machine learning Open access Nov 2025

mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset

Designing effective mRNA sequences for therapeutics remains a formidable challenge. Inspired by successes in protein design, language models (LMs) are now being applied to RNA, but progress is often impeded by the lack of comprehensive training data. Existing models are frequently limited to UTR or CDS regions, restricting their application for complete mRNA sequences. We introduce mRNABERT, a robust, all-in-one mRNA designer pre-trained on the largest available mRNA dataset. To enhance performance, we propose a dual tokenization scheme with a cross-modality contrastive learning framework to integrate semantic information from protein sequences. On a comprehensive benchmark, mRNABERT demonstrates state-of-the-art performance, outperforming previous models in the majority of tasks for 5’ UTR and CDS design, RNA-binding protein (RBP) site prediction, and full-length mRNA property prediction. It also surpasses large protein models in several related tasks. In conclusion, mRNABERT’s superior performance across these diverse tasks signifies a substantial leap forward in mRNA research and therapeutic development. Designing complete mRNA sequences for new vaccines and therapies is a complex challenge. Here, the authors develop mRNABERT, a foundational AI model that designs entire mRNA sequences and demonstrates superior performance across comprehensive benchmarks.

Ying Xiong, Aowen Wang, Yu Kang et al. · 22 citations · ⚡1

Related blog posts

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Google DeepMind Blog Nov 25, 2025

AlphaFold: Five years of impact

Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.