StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
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TL;DR - StateBridge enables LLM agents to communicate through continuous hidden states using a training-free, closed-form alignment method. It avoids text’s information bottleneck and learned projectors while improving performance across reasoning, coding, and question-answering tasks.
- Aligns sender final-layer states with the receiver’s input space via an orthogonal transformation.
- Uses norm calibration and vocabulary anchoring to maintain compatibility with pretrained input distributions.
- Prepends aligned states to the receiver’s input as a continuous prefix.
- Achieved best or tied-best results on 22 of 26 model-task pairs across four models from two families.
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StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
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Semantic Scholar citations 1 · Semantic Scholar influential citations 0
TL;DR - StateBridge enables LLM agents to communicate through continuous hidden states using a training-free, closed-form alignment method. It avoids text’s information bottleneck and learned projectors while improving performance across reasoning, coding, and question-answering tasks.
- Aligns sender final-layer states with the receiver’s input space via an orthogonal transformation.
- Uses norm calibration and vocabulary anchoring to maintain compatibility with pretrained input distributions.
- Prepends aligned states to the receiver’s input as a continuous prefix.
- Achieved best or tied-best results on 22 of 26 model-task pairs across four models from two families.