CICL26 at SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning

AbstractThis paper describes our submission to SemEval-2026 Task 4 (Track A) on narrative similarity.The task requires systems to determine which of two candidate stories is more narratively similar to a given anchor story. While large language models (LLMs) demonstrate strong semantic reasoning abilities, their predictions in comparative settings can be sensitive to stochastic decoding and input order.We propose a lightweight inference-time cascade strategy that improves robustness without modifying the underlying model. Our approach combines self-consistency voting to reduce sampling variance,a swap-based symmetry test to mitigate positional bias, and a margin-based deterministic decision rule to resolve disagreements. This design explicitly leverages model uncertainty while maintaining reproducibility and simplicity.