arXiv AI June 9, 2026 By Changze Lv, Jiang Zhou, Siyu Long, Lihao Wang, Jiangtao Feng, Dongyu Xue, Yu Pei, Hao Wang, Zherui Zhang, Yuchen Cai, Zhiqiang Gao, Ziyuan Ma, Jiakai Hu, Chaochen Gao, Jingjing Gong, Yuxuan Song, Shuyi Zhang, Xiaoqing Zheng, Deyi Xiong, Lei Bai, Wanli Ouyang, Ya-Qin Zhang, Wei-Ying Ma, Bowen Zhou, Hao Zhou

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

Read the original on arXiv AI →

arXiv:2507. 08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

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llms safety

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arXiv AI

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TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering

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safety

arXiv Machine Learning

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ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation

Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention

Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

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