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The University of Texas at Austin
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Even the strongest LLM agent workflows top out at just 25.4% proof coverage on STOC and COLT theorems, exposing a massive capability overhang between competition math performance and genuine theoretical computer science research.
Achieving optimal recalibration rates in online prediction could redefine performance benchmarks in adaptive learning systems.
Forget single-objective optimization—this work cracks omniprediction in multiclass settings, opening the door to algorithms that are robust across diverse loss functions and comparator classes.