Unlocking Multimodal Protein Language Models at Inference Time
Multimodal protein language models (pLMs) learn joint protein sequence-structure distributions, and their generation performance should also depend critically on inference-time sampling strategies. Yet prior work has focused more on model training than on how inference-time strategies behave. In this paper, we establish a three-stage investigation framework to empirically study the inference design space of multimodal pLMs across three representative pLMs and four fundamental tasks. We evaluate vanilla sampling, task-specific classifier-free guidance, and reward-guided beam search on multimodal pLMs, corresponding to controls over sampling distributions, per-step logits, and parallel traj...
| Authors | Yi Zhou, Qipeng Wang, Yunqing Liu et al. |
|---|---|
| Primary category | cs.CE |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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