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This work proposes a generational genetic algorithm to coordinate specialized large language model agents that integrate mechanistic arguments, reconsider assumptions, and assess evidence and testability that advances a vision of autonomous science in which AI research teams achieve a capacity for discovery beyond that of individual models.
"Lost at the End" reveals a striking primacy bias in multimodal KB-VQA, where early context dramatically outperforms late context, challenging conventional evaluation metrics.
