Author

Tian-Fan Fu

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Preprint Aug 2026

Disagree to Accelerate: Closing the Loop on Diffusion Feature Forecasts

Training-free feature forecasting accelerates diffusion sampling by predicting features at skipped denoising steps. Recent work has mainly focused on designing stronger forecasters. Yet forecast error varies sharply across steps, and open-loop caches trust the forecast in full at every skipped step. This fixed trust is...

Yan-Chao Li, Jiaqing Xie, Ben Gao et al. · 0 citations

Jul 2026

ABOPD: Antibody CDR Design via On-Policy Distillation

ABOPD is introduced, 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, offering a path to higher-fidelity protein design.

Zhuo Yang, Jiaying He, Jiaqing Xie et al. · arXiv.org · 0 citations

Open access Aug 2026

Reasoning BO: Enhancing Bayesian Optimization With the Reasoning Power of LLMs

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

Preprint Aug 2026

Reachability Is Not Realization: Tracing the Sources of LLM Benchmark Gains

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.

Yan-Chao Li, Wan-Hao Liu, Jia-Qing Xie et al. · 0 citations

#protein folding Preprint Aug 2026

AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design

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

#machine learning Preprint Aug 2026

Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training

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

#machine learning Preprint Aug 2026

Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training

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