Autonomous Agents for Scientific Tasks - Sina Shahandeh, Radicait
Jul 18, 2026 · 19:23
Sina Shahandeh (Radicait) presents an approach to improve autonomous agents for scientific tasks by focusing on hypothesis generation rather than just implementation. He argues that standard coding agents saturate because they run out of ideas, and introduces a hierarchical decomposition method that breaks problems into subcomponents (e.g., data, architecture, training loss) and prompts LLMs to generate radical changes—like switching from 2.5D to 3D convolutions for CT-to-PET translation. He also shows how adversarial/collaborative loops with multimodal models (e.g., Gemini) can critique intermediate results, and uses Peter Steinberger's Oracle CLI to invoke GPT-5 Pro for better reasoning. The talk demonstrates these techniques on real-world medical imaging tasks, including image registration and lung nodule analysis, and identifies multimodal model limitations as a key bottleneck for fully autonomous scientific discovery.
