AIS-001 - Artificial Intelligence Continuity Science: A Continuity-Preserving Scientific and Operational Framework for Admissible Artificial Intelligence Systems Under Irreversibility Constraints

AIS-001 formalizes artificial intelligence continuity science as a continuity-preserving scientific and operational framework governing: artificial intelligence systems, autonomous computational systems, recursive optimization systems, inferential continuity, recoverability-preserving machine reasoning, admissibility-preserving autonomy, dependency-topological AI propagation, continuity-preserving coordination, recursive self-modification constraints, interpretability continuity, civilization-compatible machine operation, and continuity-preserving intelligence evolution under recoverability-constrained continuity conditions. The publication establishes: AI continuity science, continuity-preserving machine inference, recoverability-preserving computational systems, admissibility-preserving autonomy, recursive optimization stabilization, dependency-sensitive AI propagation science, continuity-preserving alignment systems, operational interpretability science, recursive collapse prevention architectures, continuity-preserving machine coordination, and civilization-compatible intelligence systems as recursively interoperable structures within the Recoverability-Constrained Systems corpus. AIS-001 derives from: TCB-001 - Terminal Conceptual Boundary, CO-001 - Continuity Ontology, CM-001 - Continuity Mathematics, Recoverability Sciences — Unified Logic, Computation, and Inferential Systems, CS-001 - Continuity Science, CCS-001 - Civilization Continuity Substrate, CE-001 - Continuity Economics, and functions as: the artificial intelligence derivative layer of continuity science, the operational continuity architecture governing machine intelligence systems, and the admissibility framework governing continuity-preserving autonomous computation under irreversibility constraints. The publication establishes intelligence not as: unrestricted optimization, unconstrained recursive expansion, throughput-maximizing computation, or autonomous acceleration independent of recoverability, but as: continuity-preserving intelligence operating within admissibility-preserving continuity constraints. The framework further formalizes: inferential continuity, continuity-preserving alignment, recursive optimization constraints, operational interpretability, dependency-topological AI propagation, civilization-compatible machine coordination, AI collapse science, and AI restoration science as recursively unified continuity-preserving AI structures operating within continuity-bearing machine-operational systems. AIS-001 formalizes artificial intelligence continuity science as a generalized continuity-preserving scientific and operational architecture governing inferential continuity, recoverability-preserving machine reasoning, admissibility-preserving autonomy, recursive optimization stabilization, operational interpretability, dependency-topological AI propagation, continuity-preserving alignment, and civilization-compatible machine coordination under recoverability-constrained transition conditions within the Recoverability-Constrained Systems corpus.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-12
DOI
https://doi.org/10.5281/zenodo.20129289
Primary Topic
Space Science and Extraterrestrial Life
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

AIS-001 - Artificial Intelligence Continuity Science: A Continuity-Preserving Scientific and Operational Framework for Admissible Artificial Intelligence Systems Under Irreversibility Constraints

Noelia Sanchez, United Kingdom Interval Studio
Zenodo (CERN European Organization for Nuclear Research)
Space Science and Extraterrestrial Life
preprint

AIS-001 - Artificial Intelligence Continuity Science: A Continuity-Preserving Scientific and Operational Framework for Admissible Artificial Intelligence Systems Under Irreversibility Constraints

Noelia Sanchez, United Kingdom Interval Studio
preprint en

Abstract

AIS-001 formalizes artificial intelligence continuity science as a continuity-preserving scientific and operational framework governing: artificial intelligence systems, autonomous computational systems, recursive optimization systems, inferential continuity, recoverability-preserving machine reasoning, admissibility-preserving autonomy, dependency-topological AI propagation, continuity-preserving coordination, recursive self-modification constraints, interpretability continuity, civilization-compatible machine operation, and continuity-preserving intelligence evolution under recoverability-constrained continuity conditions. The publication establishes: AI continuity science, continuity-preserving machine inference, recoverability-preserving computational systems, admissibility-preserving autonomy, recursive optimization stabilization, dependency-sensitive AI propagation science, continuity-preserving alignment systems, operational interpretability science, recursive collapse prevention architectures, continuity-preserving machine coordination, and civilization-compatible intelligence systems as recursively interoperable structures within the Recoverability-Constrained Systems corpus. AIS-001 derives from: TCB-001 - Terminal Conceptual Boundary, CO-001 - Continuity Ontology, CM-001 - Continuity Mathematics, Recoverability Sciences — Unified Logic, Computation, and Inferential Systems, CS-001 - Continuity Science, CCS-001 - Civilization Continuity Substrate, CE-001 - Continuity Economics, and functions as: the artificial intelligence derivative layer of continuity science, the operational continuity architecture governing machine intelligence systems, and the admissibility framework governing continuity-preserving autonomous computation under irreversibility constraints. The publication establishes intelligence not as: unrestricted optimization, unconstrained recursive expansion, throughput-maximizing computation, or autonomous acceleration independent of recoverability, but as: continuity-preserving intelligence operating within admissibility-preserving continuity constraints. The framework further formalizes: inferential continuity, continuity-preserving alignment, recursive optimization constraints, operational interpretability, dependency-topological AI propagation, civilization-compatible machine coordination, AI collapse science, and AI restoration science as recursively unified continuity-preserving AI structures operating within continuity-bearing machine-operational systems. AIS-001 formalizes artificial intelligence continuity science as a generalized continuity-preserving scientific and operational architecture governing inferential continuity, recoverability-preserving machine reasoning, admissibility-preserving autonomy, recursive optimization stabilization, operational interpretability, dependency-topological AI propagation, continuity-preserving alignment, and civilization-compatible machine coordination under recoverability-constrained transition conditions within the Recoverability-Constrained Systems corpus.

Zenodo (CERN European Organization for Nuclear Research)
Intersil (United States) (US)
Space Science and Extraterrestrial Life
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.