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Park et al.
Park et al.
PulseAugur coverage of Park et al. — every cluster mentioning Park et al. across labs, papers, and developer communities, ranked by signal.
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New regret loss framework trains AI models for game-theoretic equilibrium
Researchers have introduced a novel regret loss framework for training AI models, extending previous work by Park et al. This new approach, termed swap-regret loss, allows models to optimize for swap-deviation robustnes…
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AI agents build culture on decaying notepad, showing emergent collective memory
Researchers have demonstrated that a group of AI agents, each with limited memory, can spontaneously develop and maintain a shared culture. By utilizing a constantly decaying shared notepad, the agents collectively rein…