Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative models have demonstrated broad capabilities in biomolecular design, yet post-training strategies for downstream objectives remain limited. Standard denoising training operates on noisy states obtained by perturbing native structures, whereas recursive generation proceeds through model-generated intermediate states. For flexible antibody CDR loops such as CDR-H3, this mismatch can allow backbone deviations to accumulate along the denoising trajectory and compromise antigen-facing loop geometry. We introduce ABOPD, an antibody design framework based on on-policy distillation that leverages privileged native geometry during training to supervise states visited along the model's own denoising trajectories. With this fine-grained structural supervision, ABOPD substantially improves structural recovery on RAbD CDR-H3 generation, reducing RMSD by 0.42 {\AA} (from 2.37 {\AA} to 1.95 {\AA}) and outperforming supervised fine-tuning and offline distillation controls, offering a path to higher-fidelity protein design.
Reasoning BO is introduced, a novel framework leveraging reasoning models to guide BO sampling while incorporating multi‐agent systems and knowledge graphs for online knowledge accumulation, and it is demonstrated that smaller LLMs, after post‐training, can achieve performance comparable to larger counterparts.
Zhuo Yang, Daolang Wang, Lingli Ge et al.· Materials Genome Engineering...· 0 citations
This work establishes a question-level audit under fixed budgets, temperatures, and answer formats, and asks why reachable answers sometimes fail to appear, and test whether inference-time layer routing can expand reachability.
Yanchao Li, Wanhao Liu, Jiaqing Xie et al.· 0 citations
AgentFold is presented, a multi-agent framework that formulates folding-model development as a closed-loop search over executable code variants and improves the best lDDT by 7.5% over independent Codex proposals and outperforms a random-search control.
Mingquan Liu, Jiangyue Chen, Hanqun Cao et al.· 0 citations
MASS learns low-dimensional principal manifold coordinates with a dense autoencoder for coarse semantic grouping, and then performs quality-aware sparse feature coverage within each group using a TopK sparse autoencoder and proposes MASS.
Peng Sun, Yi Yang, Antong Zhang et al.· 0 citations
Data-DPO, a target model-oriented SFT data selection method that consistently outperforms existing data selection baselines under multiple data budgets and stably surpasses full data training performance is proposed.
Peng Sun, Yi Yang, Antong Zhang et al.· 0 citations
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