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Draft-KV enables latent communication between frozen language models

Researchers have introduced Draft-KV, a novel method for enabling language models to communicate useful information through their internal states, rather than just decoded text. This approach allows a receiver model to directly utilize the key-value (KV) states generated by a sharer model as it drafts a response. Unlike previous methods where communication gains might not be content-dependent, Draft-KV facilitates information-based collaboration and shows significant improvements, with a frozen Qwen2.5-0.5B-Instruct receiver achieving a 78.04% score on MMLU-Redux when paired with a Qwen3-8B sharer. AI

IMPACT This method could lead to more efficient and effective collaboration between specialized AI models.

RANK_REASON The cluster contains a research paper detailing a new method for language model communication. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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Draft-KV enables latent communication between frozen language models

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The cluster contains a research paper detailing a new method for language model communication. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Draft-KV: Learning Useful Latent Communication Between Language Models

    Latent communication passes internal states between language models instead of decoded text, but higher receiver accuracy does not show that the receiver used the message content. Across five method-dataset pairs, replacing each message with one from an unrelated question changes…