τ-Rec is a benchmark for agentic recommender systems with verifiable rewards, RTE mechanism, and pass^k metrics that shows top models reach only ~57% at pass^1 and ~35% at pass^4.
Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations.ACM Transactions on Recommender Systems, 2025
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PaperFlow proposes a Profiling-Recommending-Adapting framework for longitudinal scientific paper recommendation and evaluates it on a new user-day benchmark with 24 simulated users, outperforming five baselines in ranking, behavioral alignment, and blind human evaluation.
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$\tau$-Rec: A Verifiable Benchmark for Agentic Recommender Systems
τ-Rec is a benchmark for agentic recommender systems with verifiable rewards, RTE mechanism, and pass^k metrics that shows top models reach only ~57% at pass^1 and ~35% at pass^4.
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PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
PaperFlow proposes a Profiling-Recommending-Adapting framework for longitudinal scientific paper recommendation and evaluates it on a new user-day benchmark with 24 simulated users, outperforming five baselines in ranking, behavioral alignment, and blind human evaluation.