Direct Agent Signals Cut Tokens 75%, Lift Accuracy to 86%
Replacing English with direct numerical signals between AI agents boosts math problem accuracy from 73% to 86% and slashes token usage by 75%—enabling sub-10B parameter models to rival far larger systems at a fraction of the cost.
“We see a 75% reduction in token usage directly translating to lower inference costs—potentially saving $180K/year on a mid-scale operation running 20M token/day at $0.03/1K tokens.”
Replacing English with direct numerical signals between AI agents boosts math problem accuracy from 73% to 86% and slashes token usage by 75%—enabling sub-10B parameter models to rival far larger systems at a fraction of the cost.
From the Source
""When given competition-level math questions, it goes from 73% to 86%. That is crazy... Token usage down 75%.""
— Scientists Found A Better Language For AI Agents
Key Takeaways
- 01Math accuracy jumps from 73% to 86% on competition-level problems
- 02Token usage drops by 75% via raw latent-state transfer
- 03Uses free sub-10B parameter models—not expensive frontier AI
- 04Trained for ~$4, making high-performance AI dramatically more accessible
- 05Controlled tests confirm gains come from architecture, not just teacher quality
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Scientists Found A Better Language For AI Agents
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Scientists Found A Better Language For AI Agents
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