The Symbolat and the Cognitive Metronome: A Hypergraph Framework for Temporally-Anchored Trilaminar Knowledge Representation in Agentic AI
Year: 2026
Type: article
Abstract: Current approaches to AI hallucination are reactive — they attempt to detect or mitigate errors after generation. This preprint proposes a different path: a structural, preventive layer that operates above any large language model.The core idea is that knowledge should not be stored as flat, undifferentiated data. Instead, every piece of information is separated into three epistemic layers — intention, fact, and inference — with weights computed automatically. This makes hallucination identifiable before it reaches the output: a knowledge unit built entirely on inference with no factual grounding has a measurable structural signature.Connections between knowledge units inherit this layered structure, forming multi-channel contact surfaces. The system can then navigate its own knowledge selectively — choosing to follow only fact-grounded chains, only inferential paths, or only intentional lineage — without modifying the underlying data.This framework is proposed as a complementary addition to existing language models, not a replacement. It addresses the problem from the architectural side: instead of asking “how do we catch errors in generated text,” it asks “how do we structure knowledge so that errors become structurally visible.”The preprint presents principles and mechanisms. Implementation parameters are intentionally omitted. Kontakt: [email protected]
Author: Kwaśniewski, Grzegorz
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7142853975
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