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Self-organizing graph reasoning evolves into a critical state for continuous discovery through structural-semantic

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  • 1Massachusetts Institute of Technology, 77 Mass. Ave., Cambridge, Massachusetts 01921, USA.

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

Agentic graph reasoning systems naturally evolve to a critical state, driven by semantic entropy exceeding structural entropy. This self-organized criticality enables continuous discovery and adaptation in intelligent systems.

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Area of Science:

  • Complex Systems Science
  • Artificial Intelligence
  • Information Theory

Background:

  • Agentic graph reasoning systems are crucial for AI, but understanding their emergent properties is challenging.
  • Complex systems often exhibit self-organized criticality, a state conducive to adaptation and innovation.

Purpose of the Study:

  • To investigate the emergent dynamics of agentic graph reasoning systems.
  • To identify the principles governing continuous semantic discovery and adaptation.
  • To establish parallels between these systems and critical phenomena in other complex systems.

Main Methods:

  • Analysis of structural (Von Neumann graph entropy) and semantic (embedding) entropy.
  • Quantification using a dimensionless critical discovery parameter.
  • Empirical observation of edge properties and topological features (scale-free, small-world).

Main Results:

  • A critical regime was identified where semantic entropy consistently dominates structural entropy.
  • A stable fraction of "surprising" edges (12%) indicates long-range semantic connections.
  • The system exhibits scale-free, small-world topology and negative cross-correlation between structural and semantic measures, akin to self-organized criticality.

Conclusions:

  • Agentic graph reasoning systems spontaneously evolve toward a critical state supporting continuous semantic discovery.
  • Semantic richness, not explicit programming, drives sustained exploration and adaptation.
  • Findings offer insights for engineering adaptable AI and optimizing model training strategies for discovery.