Cyrus
@cyrusasg
research lead @decagonai
prev @harvard
post-training, inference, mlsys
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a coding agent delegating work and a consumer agent negotiating a refund are both “multiagent systems.” but only one lets you design both sides of the interaction.
wrote about what changes, from context and verification to incentives and training.
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at decagon, we’ve been building around a simple idea: the failures of your current model checkpoint should shape the training data for the next one.
multimodal inference doesn't fit the mold of standard text autoregressive generation. text-to-speech models feature different architectures, different states, and different batch shapes. we rebuilt our tts serving around that and simultaneously reduced our time to first audio while improving throughput by several fold.
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