2 citations · 4 across the 16 of their papers we have counts for
21 papers
BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems
Encheng Su, Pengze Li, Ziheng Hua +11
Multi-agent collaboration has emerged as a critical paradigm for tackling complex, long-horizon tasks; however, while coordination protocols continue to advance, systemic failures…
PROTEAN: Beyond De Novo Generation - Can LLM Agents Perform Text-Guided Protein Structure Editing?
Encheng Su, Xinping Liu, Yizhou Wang +11
Generative AI has achieved remarkable success in de novo protein design, yet protein engineering typically begins with existing, characterized proteins. The primary demand is targe…
Coupled Harness--Model Evolution for Scientific Equation Discovery
Pengze Li, Jiaquan Zhang, Encheng Su +8
Large language models (LLMs) are increasingly used to discover scientific equations from experimental data. Such a discovery system has two parts that must work together: a library…
AGENT-O: A Semantic Agent Card Framework for Interoperable and Governed Healthcare AI Agents
Pengze Li, Cui Tao
AGENT-O is a modular ontology framework that defines a semantic Agent Card for representing health-oriented AI agent systems and supports assessment of reporting completeness in sc…
MorphoGP: A Nonparametric Framework for Predicting Equilibrium Beach Profiles Under Tidal Influence
Xi Wu, Yanqing Wei, Hang Yin +3
The prediction of equilibrium beach profiles under tidal influence is of fundamental importance for sustainable coastal development, informing shoreline protection strategies and m…
PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
Bohan Su, Pengze Li, Yuchen Lu +1
Scientific Reasoning Graph Extraction (SRGE) aims to recover explicit links among observations, evidence, intermediate claims, and paper-level conclusions. LLMs can produce graph-l…
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
Chen Tang, Yizhou Wang, Jianyu Wu +26
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and pe…
S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering
Encheng Su, Jianyu Wu, Jinouwen Zhang +9
Long-horizon memory question answering often requires sparse evidence from heterogeneous histories, including events, object states, visual observations, temporal relations, and ca…
LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation
Jiabei Xiao, Yizhou Wang, Chen Tang +3
AI Scientists have shown promising progress across multiple stages of the research pipeline, among which automatic scientific paper writing remains a formidable challenge. The Intr…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations
Jiyao Liu, Junzhi Ning, Chenglong Ma +14
Despite impressive performance on standard benchmarks, multimodal large language models (MLLMs) face critical challenges in real-world clinical environments where medical images in…
AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework
Zihang Zeng, Jiaquan Zhang, Pengze Li +2
Large Language Models (LLMs) demonstrate potentials for automating scientific code generation but face challenges in reliability, error propagation in multi-agent workflows, and ev…
SciIF: Benchmarking Scientific Instruction Following Towards Rigorous Scientific Intelligence
Encheng Su, Jianyu Wu, Chen Tang +9
As large language models (LLMs) transition from general knowledge retrieval to complex scientific discovery, their evaluation standards must also incorporate the rigorous norms of…
ARCHE: A Novel Task to Evaluate LLMs on Latent Reasoning Chain Extraction
Pengze Li, Jiaqi Liu, Junchi Yu +5
Large language models (LLMs) are increasingly used in scientific domains. While they can produce reasoning-like content via methods such as chain-of-thought prompting, these output…
Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults
Dong Hyun Jeon, Lijing Zhu, Haifang Li +6
Temporal Graph Neural Networks (TGNNs) have become indispensable for analyzing dynamic graphs in critical applications such as social networks, communication systems, and financial…
SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines
Yizhou Wang, Chen Tang, Han Deng +29
We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanni…
Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery
Jiaqi Liu, Songning Lai, Pengze Li +12
Automated discovery of physical laws from observational data in the real world is a grand challenge in AI. Current methods, relying on symbolic regression or LLMs, are limited to u…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
PhysUniBench: A Multi-Modal Physics Reasoning Benchmark at Undergraduate Level
Lintao Wang, Encheng Su, Jiaqi Liu +11
Physics problem-solving is a challenging domain for AI models, requiring integration of conceptual understanding, mathematical reasoning, and interpretation of physical diagrams. E…
Optimised ProPainter for Video Diminished Reality Inpainting
Pengze Li, Lihao Liu, Carola-Bibiane Schönlieb +1
In this paper, part of the DREAMING Challenge - Diminished Reality for Emerging Applications in Medicine through Inpainting, we introduce a refined video inpainting technique optim…
Comprehensive Deadlock Prevention for GPU Collective Communication
Lichen Pan, Juncheng Liu, Yongquan Fu +4
Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily…