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 la
Record details
Published: 13 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 14 August 2026
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ethics.ai (13 August 2026), “StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems,” evidence record 19170, https://ethics.ai/record/19170 (originally published by arXiv).
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