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Large language models (LLMs) have reached expert-level performance on competition mathematics largely through the volume of search placed around them: candidate solutions are sampled in quantity and retained only when an external criterion accepts them. Such a procedure improves the outcome that survives it while leavi...
ANTMAN is introduced, an adaptive coordination framework that treats evolving unresolved information needs as the unit of runtime coordination and maintains a revisable Need Graph that tracks unresolved requirements, accumulated evidence, prior attempts, and search progress.
A synthetic player population is constructed whose traits are ground truth by construction, and an opportunity-aware decision-moment representation is introduced that disentangles preference from the chance to express it; ablating it selectively degrades opportunity-dependent traits.
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