Abstract

Traditional binary logic systems have dominated digital computation and signal processing since their inception. However, these systems face growing limitations in expressiveness, fault tolerance, and context-awareness [2]. As digital environments become more dynamic and complex, there is a growing need for computation models that can adapt more intelligently.

This paper introduces 3-Level Logic, a novel trinary logic system inspired by the fluid and contextual behavior of biological neurons [4], [16]. The model defines three core logic statesActive (A), Passive (P), and Null (N) as well as a hybrid composite state (X), enabling richer semantic representation, improved noise resilience, and dynamic adaptability [3].

We detail the theoretical framework, including truth tables, ternary logic gates (TAND, TXOR, TOR), waveform representations, and circuit integration strategies [5]. A dedicated memory model is proposed, allowing for logical forgetting, conditional propagation, and parallel recall [11]. Simulations implemented in VB.NET validate the model’s practical effectiveness in signal processing and data compression through demi-nibble encoding [6], [13].

A key innovation of this work is the introduction of Rex, an agent-based logic synthesis engine capable of real-time adaptation [8]. This adaptive layer positions 3-Level Logic as a foundation for intelligent, fault-tolerant, and energy-efficient computing systems.

Keywords

  • K3L
  • REX -K3L
  • Binarry vs K3L

References

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