Derives player-optimal regret O(K log T (1/Δ)^{2/α}) for CPT-weighted matching market bandits, improves to K-independent dominant term when K ≫ N via active arm selection, and gives logarithmic regret under known/unknown corruption budgets.
URL http://www.jstor.org/stable/2975974
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Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
A convex KMM-based valuation method that accounts for both target-task alignment and inter-dataset redundancy in gradient space outperforms standard gradient-alignment baselines for LLM post-training data selection.
AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
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Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
Derives player-optimal regret O(K log T (1/Δ)^{2/α}) for CPT-weighted matching market bandits, improves to K-independent dominant term when K ≫ N via active arm selection, and gives logarithmic regret under known/unknown corruption budgets.
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Evaluation of Agents under Simulated AI Marketplace Dynamics
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
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