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.
Mathematics of operations research , volume=
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Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
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.
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
Pricing, matching, and bundling act as complementary levers that platforms can adjust to balance their own profitability against overall market welfare in equilibrium.
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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The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
Online learning algorithms for bidding in repeated second-price auctions achieve rate-optimal regret by modeling ad value as a causal treatment effect and exploiting second-price payment information.
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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.
-
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.
-
Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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Pricing, Matching, and Bundling: an Equilibrium Analysis of Online Platforms
Pricing, matching, and bundling act as complementary levers that platforms can adjust to balance their own profitability against overall market welfare in equilibrium.