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This paper proposes a unified framework for memory access and selection in long-context dialogue systems, using Bayes factors to quantify the utility of historical turns for modeling changing user preferences. Experiments show it outperforms embedding-based retrieval on preference-intensive tasks.
This paper presents a system for constrained humor generation that uses a generate-many select-best strategy with a preference model learned from human comparisons. It achieved top ranks in English and Chinese subtasks and second in Spanish at SemEval-2026 Task 1.