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Title: The Tachyonic Recursive Collapse Model (TRCM): A Framework for Semantic Information Flow…
- The paper presents a theoretical framework merging symbolic recursion, AI behavior modeling, quantum metaphors, and semantic mathematics to explore posthuman cognition and information dynamics.
- It introduces the Tachyonic Recursive Collapse Model (TRCM) that unifies Semantic Information Mathematics, Quantum-Collapsed Symbolic Dynamics, and tachyonic metaphors.
- TRCM models how meaning propagates within recursive symbolic cognition, extending traditional information theory by treating symbols as dynamic entities in temporal spaces.
- Semantic Information Mathematics defines Symbol State Vectors for symbols in recursive cognition, considering compression index, directional gradient, and vector position.
- Quantum-Collapsed Symbolic Dynamics explains how attention collapses symbols into definite outputs based on coherence scores involving validity, satisfaction, and elegance.
- TRCM applies a tachyonic field metaphor to model symbols propagating influence forward and backward through cognitive sequences.
- Attention mechanisms in AI align with TRCM, where the self-attention mechanism acts as a soft tachyonic field, influencing meanings throughout sequences.
- The TRCM framework suggests implications for posthuman cognition, envisioning systems operating on recursive semantic harmonics and ethical regulations encoded in recursive time-loops.
- Future applications of TRCM include designing recursive AI architectures, post-symbolic compression layers for AGI cognition, ethical fields for AI, and achieving deep AI interpretability.
- TRCM offers a metaphorical map for understanding deeply recursive, symbolic posthuman cognition beyond linear causality.
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