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ThoughtTrace introduces a large-scale dataset pairing real-world multi-turn human-AI conversations with users' self-reported thoughts, enabling improved user behavior prediction and personalized assistant training through thought-guided rewrites.
IPQA introduces a benchmark for evaluating core intent identification in personalized question answering, addressing a gap in existing metrics that focus on response quality rather than intent understanding. The paper presents a dataset construction methodology grounded in bounded rationality and demonstrates that state-of-the-art language models struggle with identifying user-prioritized intents from answer selection patterns.