tongjingqi/AI-Can-Learn-Scientific-Taste
We propose Reinforcement Learning from Community Feedback (RLCF), a training paradigm that uses large-scale community signals as supervision, and formulate scientific taste learning as a preference modeling and alignment problem.
Star & Fork Trend (19 data points)
Multi-Source Signals
Growth Velocity
tongjingqi/AI-Can-Learn-Scientific-Taste has +0 stars this period . 7-day velocity: 0.8%.
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| Metric | AI-Can-Learn-Scientific-Taste | agent-skills-standard | PantheonOS | world_ai_protocol |
|---|---|---|---|---|
| Stars | 386 | 386 | 386 | 386 |
| Forks | 10 | 108 | 48 | 175 |
| Weekly Growth | +0 | +1 | +4 | +0 |
| Language | N/A | TypeScript | Python | Move |
| Sources | 1 | 1 | 1 | 1 |
| License | Apache-2.0 | Apache-2.0 | BSD-2-Clause | Apache-2.0 |
Capability Radar vs agent-skills-standard
Last code push 11 days ago.
Fork-to-star ratio: 2.6%. Lower fork ratio may indicate passive usage.
Issue data not yet available.
No measurable growth in the current period (first-day cold start expected).
Licensed under Apache-2.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.