publications

2026

1. arXiv

   ASI-Evolve: AI Accelerates AI

   Weixian Xu, Tiantian Mi, Yixiu Liu, and 6 more authors

   arXiv preprint arXiv:2603.29640, Mar 2026

   Abs DOI arXiv Bib Code

   Can AI accelerate the development of AI itself? ASI-Evolve is an agentic framework for self-improving AI research that closes a learn-design-experiment-analyze loop. The framework augments evolutionary agents with a cognition base that injects accumulated research priors and an analyzer that distills experimental outcomes into reusable insights. It demonstrates AI-driven discovery across data, architectures, and learning algorithms, offering evidence for closed-loop AI research.

   @article{xu2026asievolve,
     title = {ASI-Evolve: AI Accelerates AI},
     author = {Xu, Weixian and Mi, Tiantian and Liu, Yixiu and Nan, Yang and Zhou, Zhimeng and Ye, Lyumanshan and Zhang, Lin and Qiao, Yu and Liu, Pengfei},
     journal = {arXiv preprint arXiv:2603.29640},
     year = {2026},
     month = mar,
     url = {https://arxiv.org/abs/2603.29640},
     doi = {10.48550/arXiv.2603.29640},
   }

2. arXiv

   EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents

   Weixian Xu, Shilong Liu, and Mengdi Wang

   arXiv preprint arXiv:2606.11182, Jun 2026

   Abs DOI arXiv Bib Code Website

   EEVEE is a multi-dataset test-time prompt learning framework for LLM agents operating under real-world task streams. It uses a learned router to partition incoming inputs into task clusters and assigns them to specialized prompt configurations. Router-prompt co-evolution improves robustness under heterogeneous data streams while preserving useful learned behavior within each task family.

   @article{xu2026eevee,
     title = {{EEVEE}: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents},
     author = {Xu, Weixian and Liu, Shilong and Wang, Mengdi},
     journal = {arXiv preprint arXiv:2606.11182},
     year = {2026},
     month = jun,
     url = {https://arxiv.org/abs/2606.11182},
     doi = {10.48550/arXiv.2606.11182},
   }

2025

1. arXiv

   AlphaGo Moment for Model Architecture Discovery

   Yixiu Liu, Yang Nan, Weixian Xu, and 4 more authors

   arXiv preprint arXiv:2507.18074, Jul 2025

   Abs DOI Bib

   While AI systems demonstrate exponentially improving capabilities, the pace of AI research itself remains linearly bounded by human cognitive capacity, creating an increasingly severe development bottleneck. We present ASI-Arch, the first demonstration of Artificial Superintelligence for AI research (ASI4AI) in the critical domain of neural architecture discovery–a fully autonomous system that shatters this fundamental constraint by enabling AI to conduct its own architectural innovation. Moving beyond traditional Neural Architecture Search (NAS), which is fundamentally limited to exploring human-defined spaces, we introduce a paradigm shift from automated optimization to automated innovation. ASI-Arch can conduct end-to-end scientific research in the domain of architecture discovery, autonomously hypothesizing novel architectural concepts, implementing them as executable code, training and empirically validating their performance through rigorous experimentation and past experience. ASI-Arch conducted 1,773 autonomous experiments over 20,000 GPU hours, culminating in the discovery of 106 innovative, state-of-the-art (SOTA) linear attention architectures. Like AlphaGo’s Move 37 that revealed unexpected strategic insights invisible to human players, our AI-discovered architectures demonstrate emergent design principles that systematically surpass human-designed baselines and illuminate previously unknown pathways for architectural innovation. Crucially, we establish the first empirical scaling law for scientific discovery itself–demonstrating that architectural breakthroughs can be scaled computationally, transforming research progress from a human-limited to a computation-scalable process. We provide comprehensive analysis of the emergent design patterns and autonomous research capabilities that enabled these breakthroughs, establishing a blueprint for self-accelerating AI systems.

   @article{liu2025alphago,
     title = {AlphaGo Moment for Model Architecture Discovery},
     author = {Liu, Yixiu and Nan, Yang and Xu, Weixian and Hu, Xiangkun and Ye, Lyumanshan and Qin, Zhen and Liu, Pengfei},
     journal = {arXiv preprint arXiv:2507.18074},
     year = {2025},
     month = jul,
     url = {https://arxiv.org/abs/2507.18074},
     doi = {10.48550/arXiv.2507.18074},
   }