This paper describes VerbaNex AI’s participation in SemEval-2026 Task 4: Narrative Similarity, a shared task on assessing semantic relatedness between short narrative texts. The task comprises two tracks: Track A requires selecting which of two candidate stories is more similar to an anchor, and Track B requires producing fixed-size story embeddings whose cosine similarity reflects narrative relatedness. We propose a unified two-stage system built on Qwen3-Embedding-0.6B. The first stage fine tunes the encoder as a bi-encoder with a 512 dimensional projection head using a composite loss combining margin ranking, pairwise softmax, and multiple negatives ranking objectives. The second stage trains a lightweight MLP head over frozen bi-encoder embeddings using pairwise interaction features, with k-foldcross-validation and logit-averaging ensemble inference. The system was trained exclusively on the official supervised data without leveraging the additional 1,900 synthetic triples generated by LLM released by the organizers. Al though the system ranked first on both tracks in the development phase, its performance did not transfer to the official test set, where it ranked 47 on Track A and 22 on Track B.