implementation

DSPy for Prompt Optimization in Scientific Contexts

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Graph-Based Scientific Reasoning Agent

Format

Text-first

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Linked challenge

Graph-Based Scientific Reasoning Agent

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Apply DSPy to optimize the prompts used by your agents for a specific scientific task (e.g., extracting key findings from research abstracts or formulating experimental parameters). Demonstrate how DSPy's declarative approach improves the accuracy and relevance of agent outputs compared to a baseline without optimization.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

Variables

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Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

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.

Open challenge

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