SLC: Constructing Semantic-Aware Latent Communication for LLM-based Multi-Agent Collaboration

Latent communication has shown promising potential for efficient inter-agent communication by exchanging continuous model-internal representations. Yet bypassing natural language alone does not suffice to enable effective multi-agent collaboration. Existing approaches either share prefill-derived representations or propagate each latent response along with accumulated context. Our analysis shows that decoupling latent information flow from the underlying coordination topology alters how intermediate reasoning is propagated and composed across agents, limiting effective collaboration along the multi-agent topology. To bridge this gap, we propose Semantic-aware Latent Communication (SLC), a latent communication protocol that supports effective collaboration along the multi-agent topology. Specifically, SLC employs a reusable semantic-aware hidden input-output alignment for latent response generation and transfers only Context-Augmented KV caches with Boundary-Mediated Latent Integration. Extensive experiments across seven benchmarks demonstrate that SLC achieves competitive accuracy with TextMAS, while enabling up to 4.1$\times$ faster inference and reducing input and output token usage by up to 82.6% and 79.6%, respectively.