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    <abstract>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 trajectories. Throughout the complementary advancements centered on exploration-exploitation trade-off, we (1) reveal the suboptimality of default inference protocols and identify task-oriented sampling preferences; (2) observe substantial quantitative gains across tasks, consistently boosting the upper bound performance of multimodal pLMs without updating model parameters; (3) derive conclusions about base models that differ from prior consensus. Our code is available at: https://github.com/EchoChou990919/mplm_inference.</abstract>
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%0 Conference Proceedings
%T Unlocking Multimodal Protein Language Models at Inference Time
%A Zhou, Yi
%A Qipeng, Wang
%A Liu, Yunqing
%A Xia, Jun
%A Qing, Li
%A Fan, Wenqi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhou-etal-2026-unlocking
%X 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 trajectories. Throughout the complementary advancements centered on exploration-exploitation trade-off, we (1) reveal the suboptimality of default inference protocols and identify task-oriented sampling preferences; (2) observe substantial quantitative gains across tasks, consistently boosting the upper bound performance of multimodal pLMs without updating model parameters; (3) derive conclusions about base models that differ from prior consensus. Our code is available at: https://github.com/EchoChou990919/mplm_inference.
%U https://aclanthology.org/2026.emnlp-main.1539/
%P 35132-35151

Markdown (Informal)

[Unlocking Multimodal Protein Language Models at Inference Time](https://aclanthology.org/2026.emnlp-main.1539/) (Zhou et al., EMNLP 2026)

• Unlocking Multimodal Protein Language Models at Inference Time (Zhou et al., EMNLP 2026)

ACL

• Yi Zhou, Wang Qipeng, Yunqing Liu, Jun Xia, Li Qing, and Wenqi Fan. 2026. Unlocking Multimodal Protein Language Models at Inference Time. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 35132–35151, Budapest, Hungary. Association for Computational Linguistics.