# On the Opportunities and Challenges of Prefill-to-Autoregressive Cross-Model Transfer

**Authors:** Zhenyi Shen, Junru Lu, Wei Liu, Jiangnan Ye, Jiazheng Li, Di Yin, Xing Sun, Tameem Adel, Yali Du, Lin Gui, Yulan He  
**Lab:** King's College London · **Published:** 4 Nov 2026 · **Upvotes:** 15  
**Links:** [arXiv](https://arxiv.org/abs/2611.06976) · [PDF](https://arxiv.org/pdf/2611.06976) · [Code](https://github.com/zhenyi4/platour) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.06976.html)

## Abstract

Prefill-to-Autoregressive Cross-model Transfer (PACT), a form of latent communication between language models, combines a larger model's prompt representations with a smaller model's efficient generation, offering a promising way to balance capability and inference cost. However, existing systems provide limited support for independently evaluating large-small model collaboration, and improved task performance alone does not establish whether the receiver benefits from the sender's information. We introduce PLatour, an open-source PACT framework that connects frozen models through lightweight, zero-initialized linear bridges trained directly with task rewards and supports hybrid-attention architectures. PLatour significantly outperforms open-source baselines on generation tasks and surpasses Mostik's reported results on shared mathematics and coding benchmarks using a substantially smaller sender. However, controlled experiments show that the gains on some tasks can be reproduced by uninformative sender signals, self-communication, or receiver-only training. We find that trainable bridges can act as parameter-efficient adaptation mechanisms without exploiting useful sender information. We therefore propose PACT-Check to quantify how much of a trained PACT system's gain genuinely comes from communication. Finally, we discuss the opportunities and challenges of PACT systems. Code is available at https://github.com/zhenyi4/platour.

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