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March 19, 2026Open Access

ATIC: A Geometric Theory of Artificial Cognition

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Authors

FMFelipe Maya Muniz

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Overview

The theory presents a novel geometric framework to enhance artificial cognition, suggesting implications for AGI systems.

Key Points

  • The aim is to redefine intelligence as a structural property rather than solely a product of training data.
  • Developed a six-layer composable architecture for artificial cognition.
  • Used geometric postulates based on infinite-dimensional Riemannian manifolds.
  • Validated the architecture without fine-tuning on existing model Qwen3.5-Plus.
  • Empirically tested performance against a leaderboard of autonomous agents.
  • Achieved #1 position on the ClawWork LiveBench leaderboard with 198 tasks completed at 61.6% quality.
  • Generated $19,915 in revenue without any gradient updates.
  • Comparative performance showed a significant quality improvement from 41.6% to 61.6% when applying ATIC.

Cite This Study

Felipe Maya Muniz (2026) studied this question.

synapsesocial.com/papers/69bb92df496e729e6298095fhttps://doi.org/10.5281/zenodo.19058926
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