XBRIDGE: Entity-Grounded Latent Bridge for Heterogeneous LLM Communication

Heterogeneous multi-agent LLM systems, where agents are powered by different model families, consistently outperform homogeneous configurations. Yet existing communication protocols either operate through text, discarding the sender’s internal representations, or require architectural homogeneity for latent-level transfer. We identify the entity grounding problem in cross-architecture communication: cross-attention bridges that transfer continuous representations across different LLM families suffer from rare-token compression collapse, where entity identity is lost in the continuous bottleneck (bridge-only F1 ∼30%). We propose XBRIDGE, a decode-free communication protocol that resolves this through two mechanisms. Lexical Anchor Mapping (LAM) maps the sender’s original context tokens to the receiver’s vocabulary, providing discrete entity anchors. A Latent Enrichment Bridge (LEB) lets the receiver query the sender’s hidden states for contextual enrichment. The entity anchors ground the bridge’s relational signals to specific entities through the receiver’s own self-attention. Across three model families (Llama, Qwen, Mistral), seven benchmarks, and both communication directions, XBRIDGE outperforms text-based communication (NLComm) on all 7 tasks per model pair with 11×lower latency, and exceeds the same-architecture KVComm ceiling on 6 of 7 tasks. LEB requires only 264M trainable parameters (3.8% of the receiver), trains on 587 samples in under 10 minutes, and adds negligible inference overhead.