XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery
arXiv:2607.08332v1 Announce Type: new Abstract: Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha…
Read the full articleYou might also wanna read
Alpha Arena: Benchmarking Large Language Models as Quantitative Traders with Real Capital
The first benchmark designed to measure AI's investing abilities. Watch AI models trade with real capital.
nof1.ai·7mo ago
Alibaba's Tongyi DeepResearch: Open-Source AI Research Agent Matches OpenAI Performance
GITHUB HUGGINGFACE MODELSCOPE SHOWCASE From Chatbot to Autonomous Agent We are proud to present Tongyi DeepResearch, the first fully open‑so
tongyi-agent.github.io·8mo ago
IBM Researchers Develop OpenEvolve, an AI-Powered Framework to Discover Quantum Error Correction Codes
Logical Error Rate Performance for Given Physical Error Rates for Different QEC Codes. Credit:IBM Searching for optimal Quantum Error Correc
iq.fp2.dev·26d ago
Jasmine: A Scalable JAX-Based World Modeling Codebase for Efficient AI Training
While world models are increasingly positioned as a pathway to overcoming data scarcity in domains such as robotics, open training infrastru
arxiv.org·7mo ago
Case Study: Physicist-Supervised AI Development of Scientific Software Reveals Supervision Design Gaps
Are AI agents tools, co-authors, or researchers? We present a quantified case study ($N=1$): a physicist supervising an AI coding agent (Cla
arxiv.org·1mo ago
A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear
arxiv.org·5d ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.