Akshat Gupta

LLM-driven evolutionary search is emerging as a powerful approach for discovering algorithms and designs, but existing frameworks are difficult to reuse, extend, and compare. We present SkyDiscover, a flexible, adaptive framework for AI-driven scientific and algorithmic discovery. SkyDiscover decomposes the discovery loop into four reusable components: Context Builder, Solution Generator, Evaluator, and Solution Selector, while exposing the control logic above them as a programmable interface. This modular design enables rapid experimentation and even supports adaptive designs where AI can adapt or even optimize the optimization process itself during search. We demonstrate SkyDiscover across more than 200 optimization tasks spanning mathematical optimization, systems design, algorithmic programming, and constrained image generation. Under fixed budgets and shared models, the adaptive algorithms implemented on top of SkyDiscover achieves the strongest open-source performance compared to OpenEvolve, ShinkaEvolve, and GEPA, and matches or exceeds AlphaEvolve on many tasks. A live demo further showcases end-to-end discovery with real-time monitoring, human-in-the-loop steering, and meta-optimization of the search process.

Knowledge discovery from unstructured data, including business documents, web content, and news articles, has been a key AI challenge for the financial services industry. Comprehending these corpora and discovering knowledge from them, which could be textual, tabular, or graphic, are the cornerstone of supporting business decisions in the financial services domain, where information retrieval and content analysis techniques are of fundamental importance. We propose a workshop on knowledge discovery from unstructured data in financial services at SIGIR 2023 to highlight the current and emerging opportunities, invite original research, and prompt success sharing between researchers.