Year: 2026
Type: conference-paper
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 explorationexploitation 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: github.com/EchoChou990919/mplm_inference.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Awards: G4078110728, G8341220247
OpenAlex ID: W7204479990