Gen-Verse / DiscoveryRSI

DiscoveryRSI 
 Recursively learning to discover valuable hypotheses 
## 🔬 DiscoveryRSI-27B: an open 27B model that proposes research hypotheses
In this comparison, **DiscoveryRSI-27B achieves the highest actionable-reference rate at 6.3%**, followed by DeepSeek (**5.4%**), GLM (**2.8%**), GPT (**2.5%**), and Claude (**2.3%**).
## 📺 Watch the live research loop
PhAI Labs is running a technical livestream that shows a scientific agent move from an open question to a testable research plan, then learn from evidence, critique, and revision.
**Click the animation to jump directly to the live website.**
The arena makes the process inspectable: model comparisons, research domains, candidate hypotheses, compiled experiments, and the growing hypothesis tree update as the run progresses.
## 🧭 DiscoveryRSI at a glance
**1. A new paradigm for scientific discovery.** **DiscoveryRSI** turns research into a recursive learning process centered on hypotheses. Each proposal is a versioned research object combining an evidence-grounded claim, an investigation plan, explicit decision criteria, and alternative explanations. Multi-agent evolutionary search develops a research graph, selected computational investigations are compiled into executable Harbor tasks, and the evidence, decisions, and artifacts behind each revision are preserved as reusable experience.
**2. A 27B model trained from research experience.** **DiscoveryRSI-27B** learns through Hypothesis Credit Optimization, which links assessments of scientific quality to documented contributions such as finding evidence, adding a comparator, clarifying a measurement, or repairing a falsification rule. In matched runs, DiscoveryRSI-27B selected **16.7% more unique hypotheses** and successfully compiled **50% more Harbor tasks** than its base model.
**3. DFM-Bench, a benchmark for cumulative research progress.** **DFM-Bench** evaluates how research develops across a run in life science, with a battery-science extension. It scores individual hypotheses, revisions against their actual predecessors, ordered development paths, complete research runs, and deduplicated scientific contributions. This separates a good proposal from a productive research process, and an executable package from a scientifically validated result.
*DiscoveryRSI couples multi-agent hypothesis development, contribution attribution, recursive model and workflow improvement, and process-level evaluation.*
## 📦 Resources
| **Type** | **Name** | **Link** |
| --- | --- | --- |
| 📄 Paper | **DiscoveryRSI** | arXiv:2610.23839 |
| 🤖 Model | **DiscoveryRSI-27B** | 🤗 Hugging Face |
| 📊 Dataset | **Compiled hypothesis task examples** | 🤗 Hugging Face |
| 🧪 Benchmark | **DFM-Bench** | GitHub |
| 📺 Demo | **DiscoveryRSI live arena** | Open arena |
## 🚀 Quick Start
Clone the repository and install the public harness:
 [code] 
Run the deterministic local demo and tests:
 [code] 
## 🤖 Connect DiscoveryRSI-27B
The released checkpoint is `Gen-Verse/DiscoveryRS