Synthetic Data

If you’re tired of classical GANs hallucinating financial time series that look right but behave wrong, you’re not alone. Markets aren’t just stochastic — they’re structured, memory-laced, and irrational in predictable ways. A recent paper, Quantum Generative Modeling for Financial Time Series with Temporal Correlations, dives into whether quantum GANs (QGANs) — once considered an esoteric fantasy — might actually be better suited for this synthetic financial choreography. ...

When large language models (LLMs) learned to think step-by-step, the world took notice. Chain-of-Thought (CoT) reasoning breathed new life into multi-step arithmetic, logic, and even moral decision-making. But as multimodal AI evolved, researchers tried to bring this paradigm into the visual world — by editing images step-by-step instead of all at once. And it failed. In the recent benchmark study Complex-Edit: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark1, the authors show that CoT-style image editing — what they call sequential editing — not only fails to improve results, but often worsens them. Compared to applying a single, complex instruction all at once, breaking it into sub-instructions causes notable drops in instruction-following, identity preservation, and perceptual quality. ...