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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2604. 09549v2 Announce Type: replace-cross Abstract: Recommender systems are central to online services, enabling users to navigate through massive amounts of content across various domains.
arXiv:2606. 30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified.
arXiv:2606. 15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions.
arXiv:2603. 29247v3 Announce Type: replace-cross Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.
arXiv:2608. 04625v1 Announce Type: new Abstract: Industrial recommendation strategy iteration heavily relies on large-scale A/B experimentation.
arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.