Heterogeneous agents achieve dense latent KV-cache communication via lightweight cross-model transformation and two-phase training, outperforming text at lower compute in context-aware settings and enabling context-unaware transfer.
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4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Introduces a 3-axis taxonomy (what info, alignment, fusion) for latent communication in multi-agent LLMs and identifies five design patterns from 18 methods.
Interlat lets LLM agents exchange last hidden states in latent space for communication, outperforming CoT baselines across models while enabling up to 24x faster inference via compression.
Introduces PACT protocol that projects agent outputs into action-state records, yielding comparable or better task performance with substantially fewer tokens in multi-agent LLM systems and production harnesses.
citing papers explorer
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What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
Introduces PACT protocol that projects agent outputs into action-state records, yielding comparable or better task performance with substantially fewer tokens in multi-agent LLM systems and production harnesses.