AstroAlertBench evaluates multimodal LLMs on astronomical classification accuracy, reasoning, and honesty using real ZTF alerts, revealing that high accuracy often diverges from self-assessed reasoning quality.
International Conference on Machine Learning , pages=
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Proposes OPMD algorithm achieving accelerated O(1/n) rates for offline Nash equilibrium learning in alpha-potential games via reference-anchored data coverage.
KL regularization enables pessimism-free offline learning in general-sum games, recovering regularized Nash equilibria at accelerated rate O(1/n) via GANE and converging to coarse correlated equilibria at standard rate O(1/sqrt(n)+1/T) via GAMD.
New algorithms for joint contextual MNL assortment and pricing deliver improved online regret bounds of order W sqrt(d T log N)/L0 and local suboptimality guarantees offline.
citing papers explorer
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AstroAlertBench: Evaluating the Accuracy, Reasoning, and Honesty of Multimodal LLMs in Astronomical Classification
AstroAlertBench evaluates multimodal LLMs on astronomical classification accuracy, reasoning, and honesty using real ZTF alerts, revealing that high accuracy often diverges from self-assessed reasoning quality.
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Fast Rates in $\alpha$-Potential Games via Regularized Mirror Descent
Proposes OPMD algorithm achieving accelerated O(1/n) rates for offline Nash equilibrium learning in alpha-potential games via reference-anchored data coverage.
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Pessimism-Free Offline Learning in General-Sum Games via KL Regularization
KL regularization enables pessimism-free offline learning in general-sum games, recovering regularized Nash equilibria at accelerated rate O(1/n) via GANE and converging to coarse correlated equilibria at standard rate O(1/sqrt(n)+1/T) via GAMD.
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Optimal Online and Offline Algorithms for Contextual MNL with Applications to Assortment and Pricing
New algorithms for joint contextual MNL assortment and pricing deliver improved online regret bounds of order W sqrt(d T log N)/L0 and local suboptimality guarantees offline.