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cs.AI2026
Item Response Theory for AI Safety
Joshua Fonseca Rivera, Neil Shah, David Demitri Africa +1
Language models differ in how safely they behave and these differences are measured by safety benchmarks. But aggregated benchmark scores are hard to trust and interpret, because b…
cs.AI2026
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
Jingzhe Liu, Liam Collins, Jiliang Tang +3
Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to un…
cs.AI2026
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Hansi Zeng, Liam Collins, Bhuvesh Kumar +2
Agentic search -- the task of training agents that iteratively reason, issue queries, and synthesize retrieved information to answer complex questions -- has achieved remarkable pr…