When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration
arXiv:2604.13349v2 Announce Type: replace Abstract: Communication in Large Language Model (LLM)-based multi-agent systems is moving beyond discrete tokens to preserve richer context. Recent work such as LatentMAS enables agents to exchange latent messages through full key-value (KV) caches. However, full KV relay incurs high memory and communication cost. We adapt KV-cache eviction methods to this setting and introduce \textbf{Orthogonal BackFill (OBF)} to mitigate information loss from hard eviction. OBF injects a low-rank orthogonal residual from discarded KV states into the retained KV stat
Ongoing research into multi-agent LLM systems is pushing the boundaries of inter-agent communication, necessitating solutions for efficient information exchange as these systems grow in complexity and context. The adaptation of KV-cache eviction methods signifies a natural progression in optimizing LLM-based architectures for collaborative tasks.
Efficient information-preserving compression for multi-agent LLM collaboration directly addresses the computational and memory bottlenecks hindering scalable AI agent systems. This advancement could accelerate the development and deployment of sophisticated autonomous AI agents by enabling richer, yet more cost-effective, communication.
Current multi-agent LLM communication often struggles with high memory and communication costs when exchanging full latent context. The introduction of Orthogonal BackFill (OBF) changes this by offering a method to significantly reduce these costs while mitigating information loss, paving the way for more complex and resource-efficient agent interactions.
- · AI agent developers
- · Cloud computing providers
- · LLM infrastructure providers
- · Inefficient multi-agent LLM architectures
- · High-latency AI applications
Multi-agent LLM systems will become more efficient and scalable due to reduced communication overhead.
This efficiency gain could enable more complex and deeply integrated AI agent applications across various industries, accelerating automation.
The reduced computational demands for collaborative AI could broaden access to advanced agentic systems, fostering innovation and potentially shifting market dominance in AI services.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG