Skip to content
#federated learning Open access

CIVILIZATION META-EVOLUTION, RECOMBINATION & PLURAL FUTURES AT THE LIMIT Lineage Networks, Cross-Civilization Learning, Selection Ecologies, Meta-Rules, Research Phylogenies, Deep-Time Governance, Successor Autonomy, and Handoff

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

CIVILIZATION META-EVOLUTION, RECOMBINATION & PLURAL FUTURES AT THE LIMIT Lineage Networks, Cross-Civilization Learning, Selection Ecologies, Meta-Rules,Research Phylogenies, Deep-Time Governance, Successor Autonomy, and Handoff Feng Cheng-en (33) x Starli When does an ecology of evolving civilizations become capable of revising the rulesof its own evolution without collapsing plurality into a single optimizer? CIVILIZATION META-EVOLUTION, RECOMBINATION & PLURAL FUTURES AT THE LIMITis a high-density research monograph and the third generation of the CivilizationSystems evolutionary research line. Volume I asked how a designed civilization begins to operate. Volume II asked how persistent operation produces inheritance, variation, branching,selection, recombination, plural lineages, and successor ecologies. Volume III begins after multiple lineages already exist and asks how those lineagescan change the rules by which future change itself is generated, compared,transferred, federated, resisted, repaired, and handed forward. Its flagship Three-Coupling System is: Lineage Memoryx Recombination Legibilityx Meta-Evolutionary Freedom The proposed Civilization Meta-Evolutionary Integrity Index (CMEII) is: CMEII =(Lineage Continuityx Recombination Traceabilityx Plural Selectionx Successor Autonomy)/(1 + Monoculture Pressure+ Meta-Rule Opacity+ Cross-Lineage Dependency+ Irreversibility) CMEII is a proposed research heuristic and hypothesis-generating metric. It is not a validated scientific law, universal civilization score,political ranking instrument, psychometric scale, or regulatory standard. Across 82 chapters, the volume develops research programs in meta-evolutionarystate representation, lineage networks, ecology without a single center,recombination, transfer provenance, selection ecologies, rules for changing rules,versioned governance, Research Phylogenies, evidence inheritance,Answer Embryos, Unknown Cartography, mixed human-AI timescales,branch autonomy, plural futures, federation, protocol migration,resource and compute ecologies, cross-lineage failure cascades,meta-rule capture, deep-time archives, restart, successor science,intergenerational autonomy, shared commons, inter-civilizational standards,ecology-scale observability, and a minimum theory of civilizationmeta-evolution and handoff. Each chapter opens with its Three-Coupling System and Core Relation / Formulahighlighted in gold, creating a consistent Research Visual Grammar across the book. The visual language combines warm ivory, deep navy, teal, gold,and circular upper-right / lower-left geometric motifs. The Geometric Hypothesis Atlas includes: CMEII Continuity TetrahedronLineage-to-Ecology Translation PrismMeta-Evolution Evidence PyramidPlurality-Coordination-Irreversibility Tradeoff SurfaceCross-Lineage Dependency LatticeMeta-Rule Revision and Memory HelixFailure-Recombination-Revalidation TorusSuccessor Ecology Handoff Bridge These geometries are proposed visual research hypotheses rather thanestablished universal scientific meanings. The final 200 Research Gates are designed as independent research spaces. Each Gate begins on its own page and includes a Three-Coupling System,Core Question, Why It Matters, Research Move, Evidence Boundary,Success Signal, Failure / Falsification Signal, Handoff Note,and Answer Embryo Seed. The volume closes with the Answer Embryo framework: Inheritance =Starting Point+ Structure+ Freedom to Revise The first baton does not complete the answer. It leaves an Answer Embryo for the next baton. Open boldly.Label honestly.Test rigorously.Recombine visibly.Preserve dissent.Protect exit.Record failure.Hand off clearly. 100K+ High-Density English Research Monograph82 Chapters200 Research GatesGoogle Books Living Interactive PDF Starli Research Institute XVEvolutionary Research Mode - Civilization Systems Volume IIIWhite Rainbow Era

View source

Similar papers

#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Oct 7, 2026

Discovering the value of humanistic inquiry

Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.