A chess benchmark built from 1,118 Lichess puzzles is introduced to study APO for frozen LLMs: it is used to evaluate six APO algorithms on eight target models, measuring not only baseline strength but also how much each model responds to optimization and whether optimized prompts transfer across models and to game pla...
Timothée Lesort, Alejandra López de Aberasturi-Gómez, Tristan Karch et al.· 0 citations
KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads, can broaden exploration and reach stronger incumbents within a fixed candidate budget, while the value of individual coordination features depends on the kernel and optimization stage.
Joyjit Kundu, Ben Stoffelen, Kaili Wang et al.· 1 citation
This work introduces future querying, a paradigm that probes whether large language models can function as implicit medical world models by evaluating their ability to answer time-indexed clinical queries about a patient's future, and shows that small, locally fine-tuned open-weight models can match or approach larger...
Siri Willems, James Butterworth, L. Goetschalckx et al.· 0 citations
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