yinzhangyue/ARISE

ARISE addresses one of the most critical current topics in AI: how to evaluate 'reasoning' models (like OpenAI o1 or DeepSeek-R1) as they spend more time 'thinking.' However, the project scores a 2 on defensibility due to a total lack of market signal: 0 stars and 0 forks after 184 days suggests it has failed to gain any community or industry traction despite being in a high-interest niche. In the world of evaluation metrics, the only moat is adoption; if a metric isn't used in major leaderboards or research papers, it holds no value as a standard. Frontier labs (OpenAI, Anthropic) are building their own proprietary internal metrics for test-time scaling laws and are unlikely to adopt a third-party metric that lacks broad consensus. Furthermore, existing evaluation frameworks like HELM or Big-Bench are more likely to integrate scaling-aware evaluations themselves, effectively sidelining standalone research implementations. The project is currently a dormant research artifact rather than a viable tool.