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CREDO reads the confidence from a dedicated token pair in the model's output distribution and trains it by differentiable regression, turning the trained confidence into a signal for accuracy, weighting rollouts by how far confidence and outcome disagree, so that accuracy and calibration improve together.
A self-evolving memory paradigm for generative recommendation is proposed, aiming to enable effective evolution across heterogeneous behavioral patterns, and three key principles for effective self-evolving recommendation systems are identified, including isolated memorization, reinforced evolution, and scalable applic...
The workshop brings together researchers and practitioners from data mining, LLMs, NLP, NLP, IR, human-centered AI, and AI safety to position personalization as a central research direction for next-generation AI systems at KDD.
Experiments on the LongLaMP dataset show that PrefReward outperforms non-personalized and retrieval-based baselines in both generation quality and personalization interpretability.
Large language models (LLMs) and agentic AI systems are rapidly moving into user-facing applications, yet most remain fundamentally generic, optimized for population-level objectives under the assumption that one model can serve all users. This assumption is increasingly misaligned with real-world deployment, where AI...
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