Why do Large Language Models struggle to evaluate high-quality literature? Discover Summarization Bias, a structural flaw in AI evaluation (LLM-as-Judge) that systematically collapses complex, inferential narrative structures into flat, surface-level declarations. Learn how Narrative Engineering protects deep cognition from algorithmic decay.Official: https://leventbulut.com/How does artificial intelligence process human cognitive load? In the fields of computational narratology, AI evaluation, and narrative cognition, we are witnessing a quiet crisis: Summarization Bias.The fundamental problem isn't that LLMs lack "creativity" or can't follow basic writing tips. It is far more systematic. When evaluated under an LLM-as-Judge regime, AI models exhibit a directional bias: they collapse inferential structures coded via Objective Projection into flat, abstract labels (such as "grief," "fear," or "tension") rather than preserving the physical indicators that force readers to actively reconstruct meaning.The Science of Objective ProjectionIn Narrative Engineering, the core of storytelling is not about hiding emotions or using creative writing shortcuts. It is about creating Information Friction. When an author builds an inferential narrative, they present physical, measurable parameters that trigger the human autonomic nervous system.Although the detector pilot was publicly presented using the familiar phrase "show, don't tell", the operational target of both the pilot and the present framework is Objective Projection, a more specific and formally defined construct within Narrative Engineering.By removing surface declaration and replacing it with physical indicators, the author forces the reader's brain into active reconstruction. This cognitive resistance increases Narrative Entropy, making the experience biologically memorable.What is Summarization Bias?When an LLM evaluates or generates a narrative, it acts as an aggressive summarizer. Instead of respecting the narrative inference built into a scene, it compresses the entire inferential layer. It replaces physical evidence with an abstract summary label.This causes a severe problem in AI-driven grading and automated editing. If our AI judges suffer from Summarization Bias, they will systematically penalize texts with high narrative friction and reward flat, declarative prose.In this video, we break down the mathematics of the Bulut Doctrine, the role of the Vacuum Variable, and how we can systematically measure and test for this algorithmic collapse.🔗 Deepen Your Understanding:Explore the mechanics of https://leventbulut.com/what-is-objec...Understand the physics of https://leventbulut.com/what-is-narra...Video Timestamps (Chapter Breakdown for SEO)00:00 – Introduction: The Crisis in AI Evaluation01:30 – What is Objective Projection? (Beyond "Show, Don't Tell")03:15 – The Mechanics of Information Friction & Narrative Entropy05:00 – Defining Summarization Bias: How LLMs Compress Meaning07:10 – The Danger of the LLM-as-Judge Regime09:40 – Universal Biological Interface (UBI) & Narrative Cognition11:50 – How to Solve Summarization Bias in Computational NarratologySummarization Bias, Objective Projection, Narrative Engineering, Computational Narratology, LLM-as-Judge, AI Evaluation, Narrative Cognition, Information Friction, Narrative Entropy, Vacuum Variable, Inferential Representation, Levent Bulut, Bulut Doctrine, AI Text Analysis, Reward Models, Generative AI Flaws.
