AI

Receiver-Conditioned Latent Communication gives 94% CacheBack

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 05/10/2026, 14:18:24 UTC.

Briefings

Multi-agent systems use receiver-conditioned latent communication to transfer KV caches instead of text, achieving 94% CacheBack. On FanOutQA, this method removes 75% of state, improving accuracy by 14.7 percentage points and reducing latency by 3.2x compared to text communication. The approach filters sender agents' KV caches using the receiver's attention weights, applicable across dense Transformers, Mamba-attention hybrids, and sliding-window attention models.

Key points

• Receiver-conditioned communication filters sender agents' KV caches using the receiver's attention weights to remove 75% of state on FanOutQA.
• This method improves accuracy by 14.7 percentage points and reduces median task-completion latency by 3.2x relative to text communication.

Context and limitations

The study covers only FanOutQA and reports median latency reductions; full KV cache growth constraints and agent coordination costs remain unaddressed.

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