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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Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
APG4RecSim automatically generates realistic user profiles for LLM-based recommendation simulations, outperforming manual baselines by up to 7% in nDCG@10 and 8% in JSD on three benchmark datasets.
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