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.
Develop a graph-based multi-agent system with LangGraph and GPT-5, using A2A Protocol for collaborative scientific discovery.
How work is evaluated
The evaluation will assess the scientific discovery agent system's coherence, depth of reasoning, and the novelty/plausibility of generated hypotheses and experimental designs. Key aspects include effective LangGraph workflow execution, successful A2A communication, and intelligent use of adaptive thinking budgets.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Utilize GPT-5 for advanced hypothesis generation and scientific reasoning.
Implement adaptive thinking budgets for agents to manage computational resources during complex problem-solving.
Integrate RAG using LlamaIndex over a corpus of scientific papers.
Enable secure and efficient agent-to-agent (A2A) communication for collaborative tasks.
Design and implement a graph-based multi-agent workflow using LangGraph for scientific research.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the scientific discovery agent system's coherence, depth of reasoning, and the novelty/plausibility of generated hypotheses and experimental designs. Key aspects include effective LangGraph...
Challenge input
JSON object with 'research_problem' and 'simulated_literature_keywords'
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON object with 'hypothesis_statement', 'supporting_evidence_citations', 'knowledge_gaps_identified'
- Verify that the LangGraph workflow executes correctly, navigating through defined nodes and states without...
- Confirm that agents successfully communicate and exchange information using the A2A Protocol, ensuring data...
- Ensure the system demonstrates dynamic adjustment of computational resources based on task complexity (e.g....
- Hypothesis Plausibility & Novelty target: 0.8
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Verify that the LangGraph workflow executes correctly, navigating through defined nodes and states without errors.
- Confirm that agents successfully communicate and exchange information using the A2A Protocol, ensuring data integrity and correct routing.
- Ensure the system demonstrates dynamic adjustment of computational resources based on task complexity (e.g., evidence of 'deep think' for complex problem-solving).
Metrics
- Hypothesis Plausibility & Novelty · Target 0.8
- Experimental Design Coherence · Target 0.85
- RAG Effectiveness · Target 0.9
Workflow tasks
- Task 1
Input: JSON object with 'research_problem' and 'simulated_literature_keywords'
Output: JSON object with 'hypothesis_statement', 'supporting_evidence_citations', 'knowledge_gaps_identified'
Execution harness: Python
- Task 2
Input: JSON object containing 'hypothesis_statement' from 'Hypothesis Generation' task.
Output: JSON object with 'experiment_title', 'methodology', 'expected_results', 'resource_requirements', 'ethical_considerations'
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.