Research Themes

Executive Summary

The research map exists to answer a practical question: what technical capabilities are needed if AI is to become more reliable, more local, and more computationally disciplined than current model-centric systems usually allow? Our answer is that MRP-VM needs support from several adjacent families at once: efficient execution, explicit representations, symbolic and hybrid reasoning, verification, and better architectural decompositions.

That is why these notes are organized as a build-oriented map rather than a general literature survey. Some articles matter because they improve CPU-friendly deployment. Others matter because they suggest better ways to represent knowledge, select execution regimes, verify results, or reduce dependence on permanently expensive large-model inference.

Read as a whole, the section argues that MRP-VM can become a serious runtime only if it is supported by a broader ecosystem of tractable methods, symbolic fragments, compact learned modules, constrained languages, and validation technologies.

Published Articles

The published notes are grouped below by their main technical contribution.

Execution and Deployment

1. Execution and Hardware

   Beyond the GPU Barrier: CPU-Centric Machine Learning

   Analysis of algorithmic shifts toward CPU-efficient AI architectures.

   Execution and hardware track. Published March 19, 2026.
2. Execution and Hardware

   The Algorithmic Revolution

   Transitioning from dense matrix multiplication to probabilistic sparsity.

   Execution and hardware track. Published March 19, 2026.
3. Execution and Hardware

   High-Fidelity Runtimes

   Execution strategies for high-precision inference on commodity CPU hardware.

   Execution and hardware track. Published March 19, 2026.
4. Execution and Hardware

   Democratization through Quantization

   Technical analysis of local LLM inference via the llama.cpp ecosystem.

   Execution and hardware track. Published March 19, 2026.
5. Execution and Hardware

   Systems Languages for ML

   Technical evaluation of Rust-based frameworks for high-performance inference.

   Execution and hardware track. Published March 19, 2026.
6. Execution and Hardware

   Silicon Giants: Native Optimizations

   How Intel and AMD are baking AI acceleration into the processor.

   Execution and hardware track. Published March 19, 2026.
7. Execution and Hardware

   Efficient ML Execution Trends

   A technical survey of optimized runtimes, compilers, and libraries for CPU-centric inference.

   Execution and hardware track. Published March 19, 2026.
8. Compact Models

   BitNet & 1.58-bit LLMs

   Implementation of ternary weight systems to replace floating-point multiplication.

   Compact model track. Published March 19, 2026.
9. Compact Models

   Small Language Models (SLMs)

   Analysis of high-parameter efficiency and specialized reasoning kernels.

   Compact model track. Published March 19, 2026.

Representation and Symbolic Structure

1. Representation and Reasoning

   VSA & HDC: Algebraic Reasoning

   Neuro-symbolic computing through algebraic operations in high-dimensional spaces.

   Representation track. Published March 19, 2026.
2. Representation and Reasoning

   CNL & Formal Semantics

   Addressing ambiguity in human-agent interaction through structured language subsets.

   Representation track. Published March 19, 2026.
3. Representation and Reasoning

   Languages for the AI Era

   Evolution of programming paradigms for probabilistic and agentic systems.

   Representation track. Published March 19, 2026.
4. Representation and Reasoning

   Neuro-Symbolic AI

   Integration of connectionist pattern recognition and symbolic logical reasoning.

   Representation track. Published March 19, 2026.
5. Representation and Reasoning

   Conceptual Spaces

   A geometric framework for knowledge representation and semantic reasoning.

   Representation track. Published March 19, 2026.
6. Representation and Reasoning

   Ontologies & Knowledge Graphs

   Formal definitions and structured representations for machine-interpretable knowledge.

   Representation track. Published March 19, 2026.
7. Representation and Reasoning

   Formal Logic & Automated Reasoning

   Deterministic frameworks for verifiable machine decision-making.

   Representation track. Published March 19, 2026.
8. Representation and Reasoning

   Automated Theorem Proving (ATP)

   Formal verification of mathematical claims and reasoning chains.

   Representation track. Published March 19, 2026.
9. Representation and Reasoning

   Formal Verification Languages

   Mathematical modeling for the verification of system specifications and protocols.

   Representation track. Published March 19, 2026.
10. Representation and Reasoning

    Causal Inference

    Theoretical frameworks for modeling structural mechanisms and counterfactual reasoning.

    Representation track. Published March 19, 2026.
11. Representation and Reasoning

    Probabilistic Programming

    Formal modeling of uncertainty via Bayesian inference and automated statistical computation.

    Representation track. Published March 19, 2026.
12. Representation and Reasoning

    Program Synthesis

    Automated construction of executable code from formal high-level specifications.

    Representation track. Published March 19, 2026.
13. Representation and Reasoning

    Differentiable Logic

    Mathematical integration of gradient-based learning with formal logical operators.

    Representation track. Published March 19, 2026.
14. Representation and Reasoning

    Geometric Deep Learning

    Theoretical unification of AI architectures through symmetry and invariance.

    Representation track. Published March 19, 2026.

Learning Architectures and Cognitive Models

1. Architectures and Learning

   Brain-Inspired Computing

   Theoretical frameworks for biological intelligence modeling and continuous learning.

   Architecture track. Published March 19, 2026.
2. Architectures and Learning

   Liquid Neural Networks

   Continuous-time AI systems for stable and adaptive intelligence.

   Architecture track. Published March 19, 2026.
3. Architectures and Learning

   Neuromorphic Computing

   Event-driven AI architectures for energy-efficient, real-time processing.

   Architecture track. Published March 19, 2026.
4. Architectures and Learning

   Active Inference

   Theoretical foundations for purposeful behavior via the Free Energy Principle.

   Architecture track. Published March 19, 2026.
5. Architectures and Learning

   Energy-Based Models (EBMs)

   Mathematical frameworks for dependency modeling through scalar energy minimization.

   Architecture track. Published March 19, 2026.
6. Architectures and Learning

   World Models & JEPA

   Predictive architectures for autonomous agent planning and latent representation learning.

   Architecture track. Published March 19, 2026.
7. Architectures and Learning

   Kolmogorov-Arnold Networks (KAN)

   Mathematical foundations for interpretable neural architectures via spline-based activations.

   Architecture track. Published March 19, 2026.
8. Architectures and Learning

   Cognitive Architectures

   Integration of symbolic and sub-symbolic modules into unified intelligence models.

   Architecture track. Published March 19, 2026.
9. Architectures and Learning

   Physics-Informed AI

   Integration of domain-specific physical laws as hard constraints in neural network training.

   Architecture track. Published March 19, 2026.
10. Architectures and Learning

    Decentralized AI (DeAI)

    Architectures for distributed model training and inference on peer-to-peer networks.

    Architecture track. Published March 19, 2026.
11. Architectures and Learning

    Multi-Agent Systems (MAS)

    Coordination and consensus protocols for distributed intelligent agents.

    Architecture track. Published March 19, 2026.

Verification, Safety, and Strategic Landscape

1. Verification and Governance

   Explainable AI (XAI)

   Methodologies for transparency and auditable logic in autonomous systems.

   Verification and governance track. Published March 19, 2026.
2. Verification and Governance

   Verifiable Computing & ZKPs

   Cryptographic protocols for verifying AI computation integrity and privacy.

   Verification and governance track. Published March 19, 2026.
3. Verification and Governance

   Formal AI Safety

   Mathematical frameworks for verifying safety and alignment properties in autonomous agents.

   Verification and governance track. Published March 19, 2026.
4. Strategic Foundations

   Algorithmic Information Theory (AIT)

   Mathematical foundations of computation, information content, and complexity.

   Strategic foundations track. Published March 19, 2026.
5. Strategic Foundations

   European Research Initiatives

   A survey of key research groups and projects in Switzerland and Europe focused on efficient and neuro-symbolic AI.

   Strategic foundations track. Published March 19, 2026.

Connection To MRP-VM

The notes in this section support MRP-VM in four distinct ways. The execution articles clarify how more AI workloads can move toward ordinary hardware and lower-cost inference. The representation and symbolic articles point toward stronger intermediates, constrained languages, and executable structure. The architecture articles broaden the space of candidate local regimes, including world models, compact modules, graph-oriented systems, and mixed symbolic-neural designs. The verification and governance articles define how runtime outputs can become more auditable and trustworthy.

Research Horizon

This research map is meant to remain open and expandable, but its center of gravity is now clear. The purpose of the surrounding notes is to help turn MRP-VM into a concrete technology program: something that can support new research projects, attract funding, and make AI more usable on ordinary machines and in organizations that need reliable behavior.

In that sense, the section is both a reading path and a technical backlog. It shows where the next layers of work can come from as MRP-VM moves from conceptual clarity toward runtime kernels, symbolic execution, compact learned routers, and more disciplined AI systems.