arXiv AI

AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering

arXiv:2411. 04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive.

arXiv AI

1d ago

ML-AutoResearch: Training Machine Learning Research Agents with Automatically Generated Environments

arXiv:2603. 17216v2 Announce Type: replace Abstract: With the advent of AI agents, automated scientific discovery is becoming an increasingly plausible goal.

By Ziyang Cai, Amir Saeidi, Harkirat Behl

agents fine-tuning benchmarks

arXiv AI

Jul 13

TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology

arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.

By Hyunjin Seo, Hyeon Hwang, Gyubok Lee, Jay Shin, Jimin Park, Taesoo Kim, Sanghoon Lee, Hongjoon Ahn, Sungjun Han, Sangwon Jung

llms

arXiv AI

Aug 3

Can Large Language Models Derive New Knowledge? A Dynamic Benchmark for Biological Knowledge Discovery

arXiv:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.

By Chaoqun Yang, Xinyu Lin, Shulin Li, Wenjie Wang, Ruihan Guo, Fuli Feng, Tat-Seng Chua

llms agents benchmarks

arXiv AI

Aug 6

TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering

arXiv:2411. 06024v1 Announce Type: cross Abstract: The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research.

By Yungeng Liu, Zan Chen, Yu Guang Wang, Yiqing Shen

llms agents multimodal

arXiv AI

Aug 12

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.

By Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao, Heng-Jui Chang, Zeqi Zhou, Haokai Zhao, Ma Jian, Carl Ma, Yu-Chi Cheng, Kuan Pang, Xiangru Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng-Ann Heng, Naoto Yokoya, Masashi Sugiyama, Li Erran Li, Jure Leskovec, Yejin Choi

llms diffusion multimodal benchmarks safety

arXiv AI

Jun 16

Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents

arXiv:2603. 15952v2 Announce Type: replace Abstract: Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks.

By Jacopo Teneggi, S. M. Bargeen A. Turzo, Tanya Marwah, Alberto Bietti, P. Douglas Renfrew, Vikram Khipple Mulligan, Siavash Golkar

llms agents

arXiv AI

Jul 24

Monkey King Bang: A Unified Scientific Multimodal Foundation Model

arXiv:2607. 20557v1 Announce Type: cross Abstract: Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition.

By Hesen Chen, Xinyu Su, Xiaomeng Yang, Yuetan Lin, Zixiong Yang, Junyi An, Fenglei Cao, Yifeng Jiao, Yunqi Zhang, Yuan Cheng, Zhiyu Tan, Hao Li, Libo Wu, Yuan Qi

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 16

Circuit Tracing in Autoregressive Protein Language Models

arXiv:2606. 16044v1 Announce Type: new Abstract: Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood.

By Darin Tsui, William Deinzer, Daniel Saeedi, Amirali Aghazadeh

llms safety

arXiv Machine Learning

Jul 15

Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

arXiv:2512. 15748v2 Announce Type: replace Abstract: Visual Species Recognition (VSR) is a fundamental task in scientific disciplines that require species-level identification, including ecology, palynology, evolutionary biology, systematics, and phylogenetics.

By Tian Liu, Anwesha Basu, James Caverlee, Shu Kong

llms multimodal benchmarks

arXiv Machine Learning

Jun 11

DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics

arXiv:2606. 11651v1 Announce Type: new Abstract: Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials.

By Shuni Li, Zhiyuan Ruan, Andy Shen, Ivan Jayapurna, Ting Xu, Haiyan Huang

diffusion

arXiv Machine Learning

6d ago

Task- and dataset-specific information in protein language models

arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.

By Roman Joeres, Ilya Senatorov, Olga V. Kalinina

llms rag nlp

arXiv AI

5d ago

EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction

arXiv:2608. 12906v1 Announce Type: cross Abstract: RNA-Protein Interactions (RPIs) are critical for regulating cellular functions.

By Danyu Li, Ling Zhou, Rubing Huang, Xian Zhong, Bin Zou, Kui Jiang

efficiency benchmarks

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