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GPT-5 for Hypothesis & Experimental Design with Adaptive Budgets

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Linked challenge: Autonomous Scientific Discovery

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Linked challenge
Autonomous Scientific Discovery

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Develop the 'Hypothesis Engine' agent to leverage GPT-5 for generating novel scientific hypotheses based on synthesized knowledge. Crucially, implement an adaptive thinking budget for this agent, allowing it to engage in 'deep think' mode (e.g., consuming more tokens, performing more iterations) when faced with a complex problem or when a high degree of novelty is required. Design the 'Experimental Designer' agent to then formulate conceptual experiments based on these hypotheses.

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.