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The explosion of large language model capabilities has sparked intense interest in building AI agents—systems that can autonomously use tools, search for information, and execute multi-step plans to solve complex problems. Yet a fundamental challenge has held back progress: how do you train these agents when you can't verify whether their answers are correct? Research from Tencent's WeChat AI team introduces a solution that sidesteps this problem entirely by focusing on proce

The intersection of quantum computing and financial optimization has taken a significant leap forward with recent evidence that the Quantum Approximate Optimization Algorithm (QAOA) can outperform classical methods on certain complex problems. While this might seem like an abstract academic achievement, the implications for capital markets are profound and immediate. Stock market display board illustrating market trends and fluctuations. The Computational Challenge in Finance

Relation extraction stands as one of the fundamental challenges in natural language processing for financial markets. The ability to automatically identify and classify relationships between entities—whether determining that "Goldman Sachs" has an "investor" relationship with a startup, or that "JPMorgan" maintains "operations in" Singapore—directly impacts knowledge graph construction, automated research, and trading signal generation. However, a critical flaw has plagued ev

The challenge of simulating financial market microstructure has long frustrated quantitative researchers. While deep learning models excel at image generation and natural language processing, they struggle with the extreme noise, complexity, and high-frequency dynamics that characterize limit order book data. Now, researchers from the University of Oxford have introduced a paradigm shift: treating order books as images and applying state-of-the-art diffusion models to generat