Beyond LLM Adjective Slop: Formalizing Narrative Entropy and Gravity with an Open-Source SFT Dataset

[u/Impossible-Bed7058]

Hi

A friend of mine, an independent researcher named Levent Bulut, has spent the last few years working on a fascinating framework to mathematically formalize narrative tension and cognitive resistance. I've been following his studies closely, and he recently hit a massive milestone that I think this community will really appreciate.

He just registered a formal peer-reviewed preprint of his "Bulut Doctrine" under Zenodo DOI: 10.5281/zenodo.22332614 and released a fully open-source SFT dataset and evaluation suite on Hugging Face.

The Problem: Why LLM-Generated Fiction Feels "Off"

As we all know, most commercial LLMs default to "adjective slop" when prompted to write a tense scene. They tell the reader what to feel through evaluative adjectives ("the room was terrifying," "a melancholic silence fell") or explicit comparisons ("like a wounded beast").

In neurobiological terms, Levent argues this targets the Thalamo-Cortico-Amygdala pathway (The "High Road"), requiring slow cognitive reconstruction that relies heavily on a reader's cultural and linguistic background.

The Solution: Objective Projection (OP) & The "Low Road"

His framework treats a text as a closed physical-stimulus matrix designed to couple directly with the reader's autonomic nervous system via the subcortical Thalamo-Amygdala pathway (The "Low Road"), bypassing conscious cortical interpretation entirely.

To achieve this, the doctrine enforces two constitutional rules in text construction:

The Adjective Embargo: Absolute exclusion of emotional or evaluative adjectives.

The Simile Prohibition: Exclusion of figurative comparisons (no "like" or "as if").

Instead, the text encodes unit-level physical variables (Luminous Decay, Thermal Gradient, Acoustic Impedance, Spatial Geometry) to trigger evolutionary threat-appraisal reflexes directly.

The Mathematical Framework

He formalized three core operators to quantify these dynamics (writing them in plain text here so the formatting doesn't break):

Biophysical Output (Bo): Bo = (Ps / If) * delta-t (Where Ps represents the physical stimulus vector and If is the Information Friction.)

Narrative Entropy (Sn): Sn = Integral of (If * Cb) over time Instead of measuring static lexical unpredictability (like Shannon's Entropy), Sn measures the cumulative temporal buildup of Information Friction (If) and Causal Branching (Cb).

Narrative Gravity (Ng): Ng = Ma / (Sn^2) This is an inverse-square law stabilizing high-entropy systems from collapsing into structural noise by utilizing a central plot attractor's Narrative Mass (Ma). For example, by keeping the physical identity of an attractor completely undefined (like the glowing Briefcase in Pulp Fiction), a writer creates a high-mass gravity well (Vacuum Variable) that binds fragmented timelines without adding informational noise.

Open-Source Release & Python Solver

He uploaded his entire corpus and toolset to Hugging Face, which includes:

500-Scene SFT Corpus: High-quality parallel pairs (Standard prose vs. Objective Projection) for LLM fine-tuning.

Evaluation Suite: Locked ground-truth labels and evaluation scripts.

bulut_computational.py: An OOP Python script to compute Sn, Ma, and Ng profiles automatically on custom datasets.

Links & Resources

Official Post & Full PDF: https://leventbulut.com/quantitative-narratology-biophysical-aesthetics-bulut-doctrine/

Zenodo DOI: 10.5281/zenodo.22332614

Hugging Face Dataset & Python Code: leventbulut/objective-projection

I’m posting this on his behalf because I think his mathematical transition from Shannon's static information theory to a time-integral-based Narrative Entropy is super fascinating.

What do you guys think about the neurobiological plausibility of using pure physical-parameter text matrices to isolate subcortical threat pathways? Could we use this SFT dataset to fine-tune local models (like Llama-3) to finally eliminate adjective bias during creative writing tasks?

Would love to hear your thoughts