Workflow
HLE“人类最后考试”首次突破60分,Eigen-1基于DeepSeek V3.1显著领先Grok4、GPT-5
Seek .Seek .(US:SKLTY) 3 6 Ke·2025-09-28 12:05

Core Insights - Eigen-1 multi-agent system has achieved a historic breakthrough with Pass@1 accuracy of 48.3% and Pass@5 accuracy of 61.74% on the HLE Bio/Chem Gold test set, surpassing competitors like Google Gemini 2.5 Pro and OpenAI GPT-5 [1][6][27] - The success is attributed to three innovative mechanisms: Monitor-based RAG, Hierarchical Solution Refinement (HSR), and Quality-Aware Iterative Reasoning (QAIR) [2][5][12] Technical Innovations - Monitor-based RAG: This mechanism eliminates the "tool tax" associated with traditional retrieval-augmented generation systems by continuously monitoring reasoning flow and seamlessly integrating retrieved knowledge, resulting in a 53.5% reduction in token consumption and a 43.7% decrease in workflow iterations [8][10] - Hierarchical Solution Refinement (HSR): HSR introduces a hierarchical collaboration model that allows stronger solutions to absorb valuable insights from weaker ones, enhancing the overall quality of the output [12][15] - Quality-Aware Iterative Reasoning (QAIR): This mechanism adapts the depth of iterations based on the quality of answers, ensuring efficient resource utilization by focusing on low-quality candidates for further exploration [15][18] Performance Metrics - Eigen-1's performance metrics demonstrate its superiority across various benchmarks, achieving Pass@1 of 48.3% and Pass@5 of 61.74% on HLE Bio/Chem Gold, and significantly higher scores on SuperGPQA Hard and TRQA [17] - The model's accuracy improved from 25.3% to 48.3% through the integration of various components, showcasing the effectiveness of the innovative mechanisms [20][21] Insights on Error Patterns - Analysis reveals that 92.78% of errors stem from reasoning process issues, indicating that the core challenge lies in integrating knowledge with reasoning rather than mere knowledge retrieval [18] Implications for AI in Science - The breakthrough signifies a new paradigm for AI-assisted scientific research, suggesting that AI can effectively understand and reason through complex human knowledge, thus accelerating the research process [27]