Published February 19, 2026 | Version 1.0
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Meaning Is All You Need: Moving From Token Prediction to Narrative Reasoning

Authors/Creators

  • 1. Mind Simulation AGI lab

Description

Despite the dominance of large language models (LLM), the industry is facing a growing consensus that extensive parameter scaling (scaling laws) has reached a plateau and does not lead to the creation of an AGI. The autoregressive nature of modern systems creates fundamental barriers: irreversible hallucinations, opacity of decision-making (black-box) and the impossibility of continuous learning without catastrophic forgetting. In this paper, we argue that the solution lies not in an increase in computing power, but in a paradigm shift. We formalize the "Engineering Approach to AGI Creation" and present its implementation, Embryo AGI. It is a modular, lightweight cognitive architecture based on an explicit model of the world where knowledge is separated from processing.

Unlike stochastic neural networks, our architecture provides:

  1. Complete Freedom from Hallucinations: Complete freedom from hallucinations due to strict verification of generation through the knowledge graph and ontology.
  2. Single-Iteration & Incremental Learning: The ability to instantly assimilate and integrate new (even contradictory) data in a single iteration, eliminating the need for expensive retraining and preventing the loss of old knowledge.
  3. Explainable & Language-Independent Core: Cognitive processes take place at the level of mean-ings, not tokens, ensuring full traceability and independence of thinking from the limitations of natural language.

Experiments demonstrate that Embryo AGI is able to maintain a holistic picture of the world and perform logical inference with an accuracy inaccessible to statistical models. We conclude that the transition from token prediction to Narrative Reasoning is a necessary step to create reliable and autonomous AI.

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Meaning Is All You Need - Moving From Token Prediction to Narrative Reasoning.pdf

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Additional details

Related works

Cites
Conference paper: 10.1007/978-3-030-52152-3_26 (DOI)

References

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