OpSafari/hypoarena
Scientific hypothesis-discovery workbench: grounded hypothesis-evidence graphs, synthetic literature with planted causal chains, generate-debate-evolve loops over pluggable offline adapters, Elo tournaments recovering planted skill order, paraphrase dedup, Bayesian evidence accumulation, reproducible reports. NumPy core, CPU-only torch extra.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
hypoarena is a fully offline scientific hypothesis-discovery workbench. It decomposes the mechanism layer of generate–debate–evolve "AI co-scientist" pipelines into individually testable components: a citation-bearing hypothesis/evidence graph, a synthetic literature factory, span-level grounding verification, pluggable agent adapters, Bradley–Terry / Elo tournaments, paraphrase deduplication (TF-IDF, MinHash LSH), hypothesis evolution operators, and Bayesian evidence accumulation. At runtime the package depends only on NumPy. PyTorch (CPU-only) is an optional extra used solely to demonstrate a small trainable ranker. Every default test and example runs without network access, without calling real models, and without downloading weights. This runs the complete pipeline offline over a synthetic corpus, prints whether each planted causal chain was recovered, and writes report.md / report.html. It is a mechanism demonstration and makes no claim about real scientific discovery capability. A typical run reports every planted link (e.g. planted links: 6 recovered: 6 rate: 1.0 on the demo corpus) and ends with…
Read the full README ↗ · Preview checked 2026-09-30T13:02:15.988Z
Inside the original README — Document outline
- hypoarena
- Installation
- Quick start
- Common commands
- CLI
- What each stage does
- Artifact stability
- Examples and documentation
- License
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: MIT. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
Scientific hypothesis-discovery workbench: grounded hypothesis-evidence graphs, synthetic literature with planted causal chains, generate-debate-evolve loops over pluggable offline adapters, Elo tournaments recovering planted skill order, paraphrase dedup, Bayesian evidence accumulation, reproducible reports. NumPy core, CPU-only torch extra.
Repository facts
- Owner
- OpSafari
- Primary language
- Python
- Stars
- 545
- Forks
- 27
- Open issues + pull requests
- 15
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2026-09-30T03:35:45.000Z
- Last push
- 2026-09-30T03:39:30.000Z
Topics and intended use
No topics were included in the latest source metadata.
Review the README for scope, installation, examples and limitations. We do not run repository code or certify it.
Evaluate before installing
Review licensing and dependencies, inspect recent commits and unresolved issues, and test in an isolated environment before production use. Stars and forks alone cannot answer these questions.
Source and freshness
Source: GitHub. Metadata observed 2026-09-30T12:05:16.119Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
Related repositories
EbookFoundation/free-programming-books
donnemartin/system-design-primer