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Human-Inspired Continuous Learning of Internal Reasoning Processes: Learning How to Think for Adaptive AI Systems

Hong Su

TL;DR

This work addresses the need for AI systems that continuously refine their internal reasoning architectures, not just outputs. It introduces a human-inspired continuous learning framework that unifies reasoning, action, reflection, and verification, recording internal trajectories and enabling on-line structural evolution via parallel learning. Key contributions include treating internal reasoning processes as learnable objects, enabling replacement of fixed logic with learned procedures, and implementing learning-to-learn for meta-adaptation, all demonstrated on a temperature sensor abnormality task with a 23.9% reduction in average runtime. The findings highlight the practical potential of adaptive cognitive architectures to improve efficiency and robustness in dynamic environments, with implications for autonomous agents and robotic systems.

Abstract

Learning internal reasoning processes is crucial for developing AI systems capable of sustained adaptation in dynamic real-world environments. However, most existing approaches primarily emphasize learning task-specific outputs or static knowledge representations, while overlooking the continuous refinement of internal reasoning structures, action scheduling policies, and learning mechanisms themselves. In this paper, we propose a human-inspired continuous learning framework that unifies reasoning, action, reflection, and verification within a sequential reasoning model enhanced by parallel learning. The framework explicitly treats internal thinking processes as primary learning objects. It systematically records internal reasoning trajectories and environmental interactions as structured learning material, enabling the system to optimize not only task-level content but also the organization, scheduling, and evolution of reasoning activities. This design realizes learning alongside processing, allowing cognitive structures to improve during execution. Furthermore, the framework supports controlled replacement of predefined logic with learned procedures and introduces a hierarchical learning-to-learn mechanism that jointly adapts task-level parameters and learning strategies. As a result, the system progressively evolves its internal cognitive architecture while preserving operational stability. Experimental results on a temperature sensor abnormality detection task show that incorporating internal-process learning reduces average runtime by 23.9%.

Human-Inspired Continuous Learning of Internal Reasoning Processes: Learning How to Think for Adaptive AI Systems

TL;DR

This work addresses the need for AI systems that continuously refine their internal reasoning architectures, not just outputs. It introduces a human-inspired continuous learning framework that unifies reasoning, action, reflection, and verification, recording internal trajectories and enabling on-line structural evolution via parallel learning. Key contributions include treating internal reasoning processes as learnable objects, enabling replacement of fixed logic with learned procedures, and implementing learning-to-learn for meta-adaptation, all demonstrated on a temperature sensor abnormality task with a 23.9% reduction in average runtime. The findings highlight the practical potential of adaptive cognitive architectures to improve efficiency and robustness in dynamic environments, with implications for autonomous agents and robotic systems.

Abstract

Learning internal reasoning processes is crucial for developing AI systems capable of sustained adaptation in dynamic real-world environments. However, most existing approaches primarily emphasize learning task-specific outputs or static knowledge representations, while overlooking the continuous refinement of internal reasoning structures, action scheduling policies, and learning mechanisms themselves. In this paper, we propose a human-inspired continuous learning framework that unifies reasoning, action, reflection, and verification within a sequential reasoning model enhanced by parallel learning. The framework explicitly treats internal thinking processes as primary learning objects. It systematically records internal reasoning trajectories and environmental interactions as structured learning material, enabling the system to optimize not only task-level content but also the organization, scheduling, and evolution of reasoning activities. This design realizes learning alongside processing, allowing cognitive structures to improve during execution. Furthermore, the framework supports controlled replacement of predefined logic with learned procedures and introduces a hierarchical learning-to-learn mechanism that jointly adapts task-level parameters and learning strategies. As a result, the system progressively evolves its internal cognitive architecture while preserving operational stability. Experimental results on a temperature sensor abnormality detection task show that incorporating internal-process learning reduces average runtime by 23.9%.
Paper Structure (27 sections, 5 equations, 2 figures)

This paper contains 27 sections, 5 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM Agents with Reasoning--Action Loops and Reflection
  4. Continual Learning for Large Language Models
  5. Process supervision and reasoning-trace evaluation.
  6. General Continuous Learning with Internal Human-Simulation Processes
  7. Learning Along with Processing: On-Time Acquisition of Reasoning and Actions
  8. Sequential Reasoning with Parallel Learning
  9. Procedure
  10. Learning from Outer Sources via Simulation
  11. Replacement of Fixed Code through Learning
  12. Learning Scope: Internal and External Information Acquisition
  13. Internal Processes
  14. Learning from External Environment and Other AI Systems
  15. Interaction with the Outer Environment
  16. ...and 12 more sections

Figures (2)

  • Figure 1: Learning integrated with the full human-like processing pipeline
  • Figure 2: Performance comparison of different reasoning strategies. (a) Per-run runtime with dashed curves. (b) Average runtime across runs. (c) Average number of LLM interactions.