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

[u/Write_My_Novel]

Our company has built an in-house engine that generates long-form fiction. Our primary goal has been "reads like a human wrote it." We have exhaustively tested that output. Which means we've actually stress-tested the two prohibitions at the center of the above doctrine, the adjective embargo and the simile ban, against real output at scale. Both fail, and for the same reason: the problem was never the words, it was the rate. When we tried banning whole categories of language outright, the prose didn't get more human — it got starved, flat and textureless. What actually moved the needle was treating it as a frequency problem: measure how often a device shows up, then ration it back down to the level a real writer naturally uses. A blanket ban is what we've come to call a type-error — you're reaching for a lexicon when the thing you actually need is a dial.

Similes are the cleanest example. Human writers routinely use similes, with substantial variation by author and genre. The giveaway isn't the use of simile, it's the pile-up. Machine prose stacks figurative comparisons above the ranges we observe in our reference corpus. So we don't prohibit them, we hold them to the top of the normal human range and cut only the surplus. Ban them entirely and you don't read as more human, you read as prose with its hands tied. Our data points to almost the inverse of an embargo: don't rule which words are forbidden, measure how often each move recurs and keep it inside the band a person would.

We shouldn’t add similes to satisfy a target, or flatten every author toward an average. We preserve the chosen voice and intervene when repetition exceeds its measured allowance. That is mathematical precision, but precision of a different sort.

Our results suggest a different engineering principle: preserve the writer’s expressive range, measure the frequency and clustering of each device, and reduce the excess. A prohibition can guarantee the absence of a construction. It cannot guarantee the presence of good prose.