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arXiv:2609.36672v1 Announce Type: new Abstract: Efficient exploration in high-dimensional decision spaces remains a central challenge for decision-making systems. Humans, in contrast, can navigate la...
arXiv:2607. 14192v1 Announce Type: new Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention.
arXiv:2609.36473v1 Announce Type: new Abstract: Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target...
SteerGR is a framework that enables concept-level control in generative recommendation for game discovery. It uses sparse autoencoders to define play concepts with an LLM, discover and select features associated with those concepts, and then intervene on the selected features to steer recommendations without retraining the model. Experiments on two industrial generative recommendation models demonstrate that the selected features can successfully steer recommendations toward or away from target concepts.
arXiv:2511. 22130v2 Announce Type: replace Abstract: To navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their strategies through experience.
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.