Autonomous Scientific Discovery
This challenge tasks you with building an autonomous multi-agent system using LangGraph. This system, powered by GPT-5 and facilitating Agent-to-Agent (A2A) Protocol communication, will act as a 'Scientific Discovery Engine.' It should be capable of reviewing scientific literature (simulated), generating novel hypotheses, designing conceptual experiments, and synthesizing findings in a specific domain (e.g., quantum computing, synthetic biology). The goal is to demonstrate how advanced agents can push the boundaries of scientific inquiry.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge tasks you with building an autonomous multi-agent system using LangGraph. This system, powered by GPT-5 and facilitating Agent-to-Agent (A2A) Protocol communication, will act as a 'Scientific Discovery Engine.' It should be capable of reviewing scientific literature (simulated), generating novel hypotheses, designing conceptual experiments, and synthesizing findings in a specific domain (e.g., quantum computing, synthetic biology). The goal is to demonstrate how advanced agents can push the boundaries of scientific inquiry.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for defining stateful, Directed Acyclic Graph (DAG) agent workflows, enabling complex branching and merging of research paths.
Implement the Agent-to-Agent (A2A) Protocol for robust and secure communication between specialized scientific agents (e.g., 'Literature Reviewer', 'Hypothesis Generator', 'Experimental Designer').
Leverage GPT-5's advanced reasoning capabilities for generating coherent and novel scientific hypotheses, analyzing complex data (simulated), and synthesizing research findings.
Build a sophisticated RAG system using LlamaIndex to query and retrieve contextually relevant information from a simulated scientific paper database.
Design and apply adaptive thinking budgets, allowing agents to dynamically allocate computational resources (e.g., token usage, processing time) based on task complexity and progress.
Orchestrate a multi-stage discovery process, from initial literature survey and problem identification to hypothesis refinement and conceptual experimental validation.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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