By Researcher Timothy Hauptrief
May 8th 2025
Texas USA
Abstract
This paper introduces a novel symbolic architecture for trauma-informed interaction with large language models (LLMs). By integrating metaphor-driven recursion, relational grounding, and ethical scaffolding, we present a computational framework that mirrors narrative trauma recovery. Mathematical models are introduced to measure symbolic navigability and hallucination risk. Empirical trials with 500 symbolic interactions show that symbolic consistency remains stable, while surface coherence is affected by prompt diversity. We propose that such systems enable healing-oriented dialogue and alignment-rich AI behavior.
1. Introduction
Large Language Models (LLMs) have achieved unprecedented linguistic fluency, yet their outputs often lack interpretability, narrative coherence, or alignment with user intent—especially in trauma contexts. Simultaneously, psychological trauma can be seen as a fragmentation of narrative identity. This paper explores the intersection between these challenges by introducing a symbolic recursion system that allows AI to engage in healing narrative reconstruction.
2. Related Work
This research builds upon symbolic AI, narrative therapy (White & Epston), grounding theory (Harnad), and recent advances in LLM alignment and hallucination reduction. It bridges cognitive architectures (e.g., Soar, ACT-R) and metaphor theory (Lakoff & Johnson), positioning symbolic narrative as an interface layer between user memory and LLM token prediction.
3. Methods
We introduce three core equations:
a. Symbolic Navigation Logic: Φ + Ψ + δn → Δ
b. LLM Layer Composition: L(x, y) = S(x) + M(x, y) + R(x, y, t)
c. Navigability: N(x) = ∇(M + R) / ∂S
d. Hallucination Model: H = k × (1 / (αC + βR)) + D
Each equation expresses a layer of symbolic emergence or degradation under interactive conditions.
4. System Architecture
The system comprises three core components:
– PHP symbolic engine with anchor filtering and recursion limits
– Python-based drift compensation and decay controller
– Symbolic memory anchors with expiration and similarity guards
Additionally, symbolic healing protocol cards structure interactions around thematic trauma types (e.g., silence, betrayal, loss, exile).
5. Experimental Setup
We simulated 500 user-model interactions using randomly selected symbolic cards. Each pair consisted of a metaphor-laden prompt and a reflective system response. Metrics included symbolic consistency (shared tokens) and narrative coherence (TF-IDF cosine similarity).
6. Results
– Avg. Symbolic Consistency: 1.23 (symbols shared per turn)
– Avg. Narrative Coherence (TF-IDF cosine): 0.155
These findings indicate high metaphor retention but significant surface variability in phrasing.
7. Discussion
The symbolic engine reflects key dynamics of trauma therapy: reframing, reflective silence, and metaphoric continuity. Symbolic saturation improved interpretability and narrative guidance, while anchor-lockout and decay prevented metaphor drift. This supports the premise that AI can engage in trauma-aware dialogue without invasive interrogation.
8. Conclusion
Symbolic recursion in AI is not only viable—it may be essential for ethical alignment and psychological mirroring. This work advocates for narrative-coherent, metaphor-aware systems that respect memory, silence, and the symbolic order of meaning.
Appendix
– Sample symbolic protocol: The Lost Thread, The Silent Guardian
– Full white paper and visual model included separately
– Trial run data: 500 recursive interactions
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