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LLM win rates in multi-agent games can nearly double (from 25% to 50%) simply by optimizing the context provided during inference.
Forget discrete search and trial-and-error prompting: nabla-Reasoner uses gradient descent in the LLM's latent space to boost reasoning accuracy by 20% while cutting model calls by up to 40%.