# Abstract

CLE-Net (Decentralized Cognitive Agent Network) is an experimental architecture for autonomous cognitive agents that extract, preserve, and evolve symbolic laws from human interaction — independent of any single machine, model, or operator.

Unlike traditional AI systems that focus on answering questions or executing tasks, CLE-Net addresses a fundamentally different problem: the discovery and persistence of implicit rules that govern human behavior.

The core contribution is Proof of Cognition (PoC), a novel consensus mechanism where truth emerges from independent cognitive discovery rather than energy expenditure, capital stake, or authority. When multiple independent agents, operating on different data, converge on the same symbolic rule, that rule achieves consensus without any single agent controlling the outcome.

We validate the approach through a minimal decentralized MVP, demonstrating that independent agents can discover the same latent rule from heterogeneous datasets and converge on consensus without sharing raw data.

Key results show that:

1. **Independent discovery is achievable**: Agents operating on different data can discover identical rules
2. **Privacy is preserved**: Only rule hashes are broadcast, not raw data
3. **Consensus emerges naturally**: No central coordination is required
4. **Contradictions are signals**: Divergent conclusions persist as competing hypotheses

CLE-Net represents a step toward cognitive infrastructure — systems that understand, not just respond.

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## Keywords

Decentralized AI, Cognitive Agents, Symbolic Reasoning, Knowledge Graphs, Proof of Cognition, Consensus Mechanisms, Distributed Systems, Autonomous Agents