StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck. Converting the sender's continuous hidden states into discrete tokens discards information that token identities alone cannot capture. Recent work proposes latent communication as an alternative, where agents transmit hidden representations directly without converting them to text. However, existing latent methods either inject working memory layer by layer across the transformers, or require trained projectors that limit portability. We propose StateBridge, a training-free latent communication approach that aligns the sender's final-laye...
| Authors | Yanwen Peng, Delvin Ce Zhang, Xi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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