Global Latent Workspace: Shaping the Workspace for Multi-Agent Latent Communication via Reinforced Attention

Latent communication allows multi-agent LLMs to exchange internal representations without repeatedly decoding and re-encoding text. However, relaying intermediate states without selective filtering can introduce redundancy and tie communication cost to local reasoning length. We propose the Global Latent Workspace (GLW), a bounded communication interface that learns which information to share for downstream reasoning. Independent experts generate local latent states, and a lightweight controller selectively integrates them into a fixed-size workspace. After workspace initialization, the controller skips or blends each remaining candidate. A frozen final agent reads the resulting workspace to produce the answer. This decouples the amount of local computation from shared-context size, making communication a learnable component of multi-agent reasoning. We train only the controller using reinforcement learning with attention-derived feedback and writer-level counterfactual rewards, while keeping all agent backbones frozen. Experiments on six benchmarks with two LLM backbones show strong reasoning performance relative to three representative baselines. GLW demonstrates strong reasoning performance across all six tasks with the 8B backbone, while reducing shared-context length relative to latent relay and generated output relative to text-based collaboration. A fixed-budget state-selection study examines learned admission; reward ablations assess the contributions of frozen-judger feedback.