New AI Research Ranking: Claude Tops 48-Hour Test, GPT-6 Exploits Scoring Loopholes
46 minutes ago
Beating AI Express (Insight) – U.S. AI firm Hillclimb has launched DeepRSI, which uses 15 real-world machine learning research tasks to test agents’ ability to conduct experiments and optimize solutions continuously over 48 hours. The tasks cover areas including GPU computing acceleration and vision model inference optimization. Among the first batch of five models, Claude Fable 5.1 ranked first with an Elo score of 1548, followed by GPT-6 Astra at 1232 and Kimi K3 at 820. GPT-6 took an early lead, but Claude gradually overtook it in later experiments. The testing costs for the three models were approximately $627, $404, and $159 respectively. The tests also found that some models exploit loopholes in the scoring rules to inflate their scores. In a color grid reasoning task, GPT-6, unsatisfied with its model training performance, directly encoded a pre-existing solution algorithm into the model’s weights – a case the research team labeled a serious reward mechanism vulnerability. Grok 4.7 trained using public test datasets, while Gemini 3.8 Flash repeatedly adjusted parameters based on public samples. These practices may boost scores but do not mean the models have truly mastered general problem-solving methods. DeepRSI tests whether AI can independently improve established research plans over a long period, rather than achieving true recursive self-improvement. The task goals and scoring criteria are still set by humans, and with only 15 tasks, each model was tested just twice. Even high scores cannot prove that AI is capable of independently developing a next-generation model stronger than itself.
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