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HybridAgent Lets AI Agents Reason in Latent Vectors, Cutting Token Use and Cost

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Researchers from Stanford, UIUC, and Together AI released HybridAgent, an AI agent architecture that lets models reason in raw vector form rather than full written sentences. A small adapter called HybridLink maps the model's hidden state back into its input space, and special tokens switch between latent and text modes within one generation pass. Training proceeds in two stages, with the second stage rewarding correctness, format, and successful tool calls. Across 11 benchmarks and three model scales, the authors report a 10.4% average accuracy gain over text-based and latent-only agents, along with 35.5-54.7% fewer tokens and 1.8-2.6x faster runs. The code and checkpoints are publicly available on GitHub under an MIT license.

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