CascadeMind at SemEval-2026 Task 4: A Hybrid Neuro-Symbolic Cascade for Narrative Similarity

arXiv:2601.19931v2 Announce Type: replace Abstract: How should a system handle uncertainty when comparing narratives? We present CascadeMind, a hybrid neuro-symbolic system for SemEval-2026 Task 4 (Narrative Story Similarity) built around a core finding: an LLM's internal vote distribution is a reliable proxy for task difficulty, and confidence-aware routing outperforms uniform treatment of all cases. Our cascade samples eight parallel votes from Gemini 2.5 Flash, applying a supermajority threshold to resolve confident cases immediately (74% of instances at 85% development accuracy). Uncertain cases escalate to additional voting rounds (21%), and only perfect ties (5%) are deferred to a symbolic ensemble of five narrative signals grounded in classical narrative theory. The resulting difficulty gradient (85% -> 67% -> 61% by pathway) confirms that vote consensus tracks genuine ambiguity. In official Track A evaluation, CascadeMind placed 11th of 47 teams with 72.75% test accuracy (Hatzel et al., 2026), outperforming several systems built on larger and more expensive models. Gains are driven primarily by routing strategy rather than symbolic reasoning, suggesting that for narrative similarity, knowing when you don't know matters more than adding auxiliary representations.