# OpenMacs

- **Event:** [OpenEnv Hackathon SF](https://cerebralvalley.ai/e/openenv-hackathon-sf)
- **When:** Mar 7 at 9:00 AM – Mar 8 at 12:00 AM (PST)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Sameer Kashyap](https://cerebralvalley.ai/u/sameeerkashyap), [Abhinav Sudhakar Dubey](https://cerebralvalley.ai/u/AbhinavDubey30)
- **GitHub:** https://github.com/AbhinavDubey30/OpenMax
- **Website:** https://colab.research.google.com/drive/1uvjYLgoLkbgC7mVl6kFXxDH6jodtY6zJ?usp=sharing
- **Demo video:** https://youtu.be/4uYkE9tYUGU
- **Hugging Face:** https://huggingface.co/spaces/AbhinavSDubey30/hypothesis-engine
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/39

🔬 HYPOTHESIS ENGINE
Teaching AI to Reason Like a Scientist

THE GAP

Existing RL environments treat reasoning as retrieval. But science requires strategic intervention, causal inference, and generalization to unseen cases. No RL environment has operationalized the full scientific method. Until now.

WHAT WE BUILT

Hypothesis Engine is a procedurally-generated RL environment where an LLM agent investigates a black-box system with a new hidden rule set every episode. The agent must design experiments under a budget, form mathematical hypotheses, and predict outcomes on unseen test cases.

The reward is sparse and grounded: nothing for plausible hypotheses — everything for correct predictions on unseen data.

WHY IT MATTERS

Rollouts target what benchmarks measure but post-training neglects: causal reasoning, structured exploration, and mathematical abstraction. Fully procedural, infinitely varied, and verifiable — built for scalable GRPO-style post-training.

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Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/39. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt