Year: 2026
Type: conference-paper
Abstract: We present a structured, parameter-free system for SemEval-2026 Task 4 on Narrative Story Similarity.Instead of treating similarity as scalar embedding proximity, we align model reasoning with the task ontology by decomposing each story into abstract theme, course of action, and outcome, and performing contrastive comparison over these dimensions.Our primary contribution is a closedloop, failure-driven prompt optimization procedure that iteratively refines concise guideline documents while keeping model parameters fixed and reverting updates that degrade performance, thereby isolating improvements attributable to structured reasoning rather than representation learning.Ontology-aligned decomposition alone achieves 70% accuracy on both train and test sets; with controlled guideline evolution, performance improves to 76% on train and 73% on test without additional supervision or fine-tuning.These results demonstrate that structured prompt optimization can meaningfully enhance contrastive narrative reasoning in a fully parameter-free setting.
Authors: Yen Yee Yam, Hong Meng Yam
Institutions: National University of Singapore, Stanford University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166868047