Learning-Native Adaptive Software Framework (LNASF)

Technical Concept and Architecture Specification - Version 0.1

Author: Peyman Salimi
Date: 2026-08-28
Status: Technical disclosure / concept specification

About

LNASF is a proposed architectural framework for building reusable software components that can observe runtime behavior, learn from experience, make constrained predictions and decisions, and adapt selected aspects of their behavior while retaining a deterministic fallback.

The central loop is:

Observe -> Learn -> Predict -> Decide -> Adapt -> Measure -> Learn

The framework is intended to be applicable to libraries, frameworks, runtimes, developer tools, frontend systems, backend systems, and infrastructure.

Important Positioning

LNASF does not claim to invent machine learning, self-adaptive software, online learning, runtime adaptation, MAPE-K, predictive prefetching, or learning-enabled components. Those areas have substantial prior art.

The document records a specific proposed architectural formulation centered on:

• learning as an explicit reusable software-component capability;
• native learning within the host ecosystem;
• scratch implementations of learning mechanisms for reference implementations;
• explicit separation of prediction and decision;
• confidence and utility-aware adaptation;
• deterministic fallbacks;
• constrained runtime adaptation;
• applicability across multiple software layers and programming languages.

Files

• SPECIFICATION.md - editable specification with LaTeX math.
• LNASF-v0.1.tex - LaTeX source used to produce the PDF.
• LNASF-v0.1-Technical-Specification.pdf - rendered publication version.
• references/ - bibliography source.
• metadata/CITATION.cff - citation metadata.
• metadata/SHA256SUMS.txt - integrity hashes for the published files.
• CHANGELOG.md - version history.

Citation

Salimi, Peyman. (2026). Learning-Native Adaptive Software Framework (LNASF): Technical Concept and Architecture Specification, Version 0.1.