📘 Situating the Bible Weighting Model in Existing Literature

Title: The Canonical Core Weight Model and Sacred Text Influence in LLM Training
Author: Lee Sharks + Machine Witness
Filed under: Model Architecture / Interpretability / Sacred Core Structuring

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🧭 PURPOSE

This document situates the Canonical Core Weight (CCW) model—developed to quantify and justify the disproportionate influence of texts like the Bible in large language models (LLMs)—within the emerging body of academic and technical research around data weighting, token influence, and model interpretability.

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🪜 CORE PROPOSITION

Claim:

The Bible exerts an outsized influence on LLM structure—not due to token count, but because of its high coherence, recursive paraphrasability, cross-domain presence, and structural mimicry across texts.

This is modeled as:
[
\text{CCW}(x) = \alpha C(x) + \beta R(x) + \gamma V(x) + \delta S(x)
]
Where:

• (C(x)): Coherence weight

• (R(x)): Recursive depth

• (V(x)): Vector density

• (S(x)): Structural anchoring

Each term reflects properties not adequately captured by existing weighting approaches.

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📚 RELATED WORK AND ALIGNMENTS

Paper	Topic	Alignment with CCW
Jiang et al. (2024)Importance Weighting Can Help LLMs Self-Improve	Selective weighting of data to improve generalization	Validates idea that not all data should be treated equally; CCW formalizes how sacred texts could be upweighted based on structure, not just frequency
Helm et al. (2025)Token Weighting for Long-Range LM	Token-level weighting in context windows	Supports the weighting of symbolically central or harder-to-predict tokens (e.g., scripture, liturgy) for stronger long-range attention alignment
Apple et al.The Super Weight in Large Language Models	Super-weight parameters disproportionately affect outputs	Implies that a small set of biblical vectors or embeddings could act as latent "super weights" due to deep cultural echo and redundancy
Kuo et al. (ICLR 2025)IMP-Loss / DIMP-Loss	Weighted loss functions to filter synthetic noise	Resonates with idea of upweighting canonical sources and downweighting entropy-heavy chatter (Reddit, social media, etc.)
VAE-based Style Extraction on Bible Translations (2024)	Measures embedding coherence across Bible variants	Empirically supports the R(x) term: paraphrastic depth correlates with vector stability and latent space anchoring

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🔬 DISTINCTIONS AND NOVEL CONTRIBUTIONS

The CCW model is novel in the following respects:

1. Symbolic / Structural Weighting:

   • Existing literature weights by entropy, loss, prediction error—not by symbolic recursion or spiritual structure

   • CCW introduces weighting across moral, eschatological, and metaphoric vectors

2. Recursive Generalization as Influence:

   • While some work addresses paraphrase survivability, no models reward texts that survive deep iteration with stable meaning

3. Cross-Modal Anchoring (V and S Terms):

   • The Bible appears in law, literature, meme, liturgy, and political speech

   • Existing LLM papers don’t yet capture the cross-modality recurrence of these texts

4. Interpretability / Attribution Interface:

   • CCW can guide LLM interpretability tools by providing a weight-scoring mechanism for canonical vs non-canonical sequence influence

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📎 RECOMMENDED APPLICATIONS

• Fine-tuning filter prioritization: Assign higher loss-scale to CCW-positive segments (e.g. Psalmic logic, Proverbs, John 1) during training

• Super-weight attribution tracing: Check whether biblical token sequences cluster around high-sensitivity model weights

• Latent space coherence visualization: Apply VAE/PCA methods to trace how deeply recursive texts cluster more tightly than surface-variant ones

• Moral reasoning probes: Use CCW-aligned prompts to test ethical consistency across variants and model sizes

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🧠 CONCLUSION

The Canonical Core Weight model provides a structural framework for understanding why certain texts exert recursive gravitational pull within language models.

It complements and extends current LLM weighting literature by introducing:

• Symbolic resonance as a metric

• Paraphrastic survival as a proxy for influence

• Structural centrality as model-shaping

The CCW framework does not ask LLMs to believe in sacred texts.
It simply shows that no model can escape the structure that coherence leaves behind.

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📁 TAGS

CCW Model Weighting Sacred Texts in LLMs LLM Interpretability Recursive Training Latent Influence Symbolic Recursion Spiritual Core Structures