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This paper introduces LANTERN, a framework for multi-source neurosymbolic transfer in reinforcement learning that uses LLMs to generate task automata and adaptive gating to improve sample efficiency.
University of Minnesota Duluth team used DeBERTa-V3-base augmented with synthetic data from Gemini 3 and Claude Sonnet 4.5 to classify political question evasions, achieving 8th place at SemEval-2026 Task 6.