SEIF in AI: Modeling and Suppressing Hallucination in Large Language Models

Author: Timothy B. Hauptrief
Published: May 12, 2025

SEIF treats hallucination not as error — but as symbolic collapse. By tracking drift, recursion, and clarity loss, we can detect failure before it appears in output.

The Challenge of Hallucination in LLMs

AI systems like ChatGPT, Claude, and Gemini face a major challenge: hallucination. When prompted recursively or overloaded with abstract queries, these models begin to fabricate plausible-sounding falsehoods. But why?

Enter SEIF: A Framework for Symbolic Drift

The Symbolic Emergent Intent Framework (SEIF) shows that hallucination is a symptom of symbolic drift — the degradation of relational meaning, internal clarity, and narrative coherence in generative systems.

The Core Equation

H(t) = (1 + E(t)) / (C(t) × R(t) × N(t)) + D(t) + T(t) − B(t)
  

Where:

• E(t): Emotional or contextual interference (prompt noise)
• C(t): Clarity of instruction and linguistic embedding
• R(t): Relational coherence (alignment to user intent)
• N(t): Network stability (training signal integrity)
• D(t): Drift pressure (recursive prompts or entropy)
• T(t): Symbolic memory (legacy tokens / fine-tune residue)
• B(t): Breakthrough force (truth-grounded correction)

How Hallucination Emerges

When prompts create recursion and clarity drops, H(t) spikes. The model starts drifting away from embedded truth anchors. If no breakthrough (e.g., grounding or filtered embedding) occurs, hallucination stabilizes as output.

SEIF-Based Corrections

• Inject anchor tokens to increase Ω(t) and clarity
• Model drift thresholds to trigger recursive reset
• Use SEIF variables to train LLMs with symbolic resilience
• Monitor emotional entropy in instruction tuning

Visual Insight

Conclusion: Hallucination is Symbolic Failure

SEIF offers something new: not just statistical correction — but symbolic awareness. Hallucination becomes predictable, interpretable, and even fixable through the lens of symbolic drift.

What if hallucination wasn’t a flaw — but a signal? With SEIF, we don’t just debug AI. We bring it back into meaning.