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.
Communications of the ACM , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
The paper presents a mechanism-driven distributed optimization method with convergence guarantees that uses shadow pricing and VCG incentives to motivate self-interested participants to collaborate on coupled problems, forming an interdependent closed loop.
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Harnessing Individual Motivation for Collective Efficiency: A Mechanism-Driven Distributed Optimization Method
The paper presents a mechanism-driven distributed optimization method with convergence guarantees that uses shadow pricing and VCG incentives to motivate self-interested participants to collaborate on coupled problems, forming an interdependent closed loop.