Graph-Based Scientific Reasoning Agent

Inspired by OpenAI's FrontierScience benchmark, this challenge focuses on developing an advanced agent system capable of tackling expert-level scientific reasoning problems. Participants will design and implement a graph-based workflow that simulates the scientific method – from hypothesis generation and experimental design (simulated) to data analysis and conclusion formulation. The system will leverage state-of-the-art LLMs for complex problem-solving and incorporate MCP-enabled tools for integrating with scientific databases and symbolic computation engines. The emphasis is on building a robust, verifiable reasoning pipeline that can explain its steps and adapt its approach based on intermediate results, showcasing extended thinking capabilities and hybrid reasoning. The core of this challenge involves using LangGraph to define a Directed Acyclic Graph (DAG) that represents stages of scientific inquiry. Agents, powered by GPT-5.2 and potentially DeepSeek-V3 for specialized tasks, will interact within this graph, using DSPy for optimizing prompts to achieve scientific accuracy and minimize hallucinations. Developers will integrate MCP tools for accessing external knowledge (e.g., ArXiv, PubMed) and computational resources, and implement adaptive thinking budgets to allow for deeper analysis on critical scientific junctures. The final system should not only solve problems but also explain its reasoning process transparently.