The Year in AI Papers
The year/Topics/Science and medicine

Research collection

Science and medicine

AI applied to scientific and medical domains: scientific foundation models, discovery and hypothesis generation, clinical reasoning, and medical imaging.

Papers
8
Research labs
0
Official code
7

1–8 of 8 papers in this collection

01

Independent research

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

SciReasoner is a multimodal scientific foundation model for native structural reasoning across proteins, small molecules, and inorganic crystals. It discretizes coordinates, topologies, and periodic connectivities into a unified structure-aware vocabulary, treating structural tokens as addressable evidence units within autoregressive reasoning…

Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, et al.
Published
Jul 2026
Citations
0
Code
24 stars
Read summary Original
02

arXiv.org

Heterogeneous Scientific Foundation Model Collaboration

The paper introduces Eywa, a heterogeneous agentic framework that integrates domain-specific foundation models (FMs) with large language model (LLM) agents to solve scientific tasks involving non-linguistic data like time series and tabular data. Eywa uses an FM-LLM 'Tsaheylu' interface, implemented via the Model Context Protocol, allowing LLMs to…

Zihao Li, Jiaru Zou, Feihao Fang, Xuying Ning, et al.
Published
Apr 2026
Citations
1
Code
23 stars
Read summary Original
03

arXiv.org

FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios

FORGE is a benchmark for evaluating Multimodal Large Language Models (MLLMs) in manufacturing scenarios. It introduces a dataset combining real-world 2D images and 3D point clouds with fine-grained annotations like exact model numbers. The benchmark includes three tasks: Workpiece Verification (WORKVERI), Structural Surface Inspection (SURFINSP), and…

Xiangru Jian, Hao Xu, Wei Pang, Xinjian Zhao, et al.
Published
Apr 2026
Citations
2
Code
13 stars
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04

arXiv.org

Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale

Intern-S1-Pro, developed by Shanghai AI Laboratory, is the first one-trillion-parameter scientific multimodal foundation model, built on the SAGE framework. It scales from Intern-S1 via expert expansion with Grouped Routing to ensure load balance and training stability, and uses a Straight-Through Estimator for efficient router updates. The model…

Yicheng Zou, Dongsheng Zhu, Lin Zhu, Tong Zhu, et al.
Published
Mar 2026
Citations
14
Code
Not linked
Read summary Original
05

arXiv.org

Innovator-VL: A Multimodal Large Language Model for Scientific Discovery

Innovator-VL is a scientific multimodal large language model (MLLM) designed for scientific understanding and reasoning while maintaining general vision performance. It uses a transparent, reproducible pipeline with RICE-ViT vision encoder, PatchMerger projector, and Qwen3-8B-Base language model. Training includes language-image alignment (LLaVA-1.5 558k),…

Zichen Wen, Boxue Yang, Shuang Chen, Yaojie Zhang, et al.
Published
Jan 2026
Citations
10
Code
167 stars
Read summary Original
06

Independent research

Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows

This paper introduces SGI-Bench, a benchmark for evaluating Scientific General Intelligence (SGI) in large language models (LLMs). SGI is defined as an AI's ability to autonomously navigate the complete, iterative cycle of scientific inquiry, grounded in the Practical Inquiry Model (PIM) with four quadrants: Deliberation, Conception, Action, and…

Wanghan Xu, Yuhao Zhou, Yifan Zhou, Qinglong Cao, et al.
Published
Dec 2025
Citations
21
Code
167 stars
Read summary Original
07

arXiv.org

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines

SciReasoner is a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. It is pretrained on a 206B-token corpus (scientific text, pure sequences, sequence-text pairs) and post-trained via SFT on 40M instructions, annealed cold-start bootstrapping for chain-of-thought, and reinforcement learning…

Yizhou Wang, Chen Tang, Han Deng, Jiabei Xiao, et al.
Published
Sep 2025
Citations
9
Code
90 stars
Read summary Original
08

arXiv.org

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

This survey reframes the development of Scientific Large Language Models (Sci-LLMs) as a co-evolution between models and their data substrate, providing a data-centric synthesis across six scientific domains (physics, chemistry, materials science, life sciences, astronomy, and Earth science). It introduces a unified taxonomy of scientific data and a…

Ming Hu, Chenglong Ma, Wei Li, Wanghan Xu, et al.
Published
Aug 2025
Citations
26
Code
458 stars
Read summary Original