@omarsar0: The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, and they duplicate w…
Summary
Offloop introduces D1, a dispatcher model for multi-agent systems that reduces redundant work and token usage, achieving state-of-the-art performance on GDPval at lower cost.
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Cached at: 07/24/26, 09:14 PM
The hard part of multi-agent systems is getting agents to stay quiet.
Put five agents on one task, and they duplicate work and burn tokens talking to each other.
Offloop trained a dispatcher model called D1 that decides which agent moves next and when the right move is to do nothing. They achieve state-of-the-art performance on GDPval at a fraction of the usual cost.
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Offloop (@Offloop): Introducing Offloop!
We’re a team of four. Today our multi-agent harness hit state of the art on GDPval, ahead of Claude code and Codex across jobs that pay $2.4 trillion a year in the US.
Offloop gives every knowledge worker what the Fortune 500 spends billions on: a
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