MiroMind发布全球最强搜索智能体模型MiroThinker 1.5，以“发现式智能”挑战传统大模型路径

Core Insights - MiroMind team has launched its flagship search intelligence model, MiroThinker 1.5, which emphasizes "discovery intelligence" rather than merely increasing parameter size [1][2] - The model aims to reconstruct understanding of the world under unknown conditions, focusing on research, verification, and correction [1] - MiroThinker 1.5 demonstrates high performance with significantly fewer parameters compared to larger models, achieving results comparable to those with 1 trillion parameters [2][7] Performance Evaluation - MiroThinker-v1.5-30B operates with only 1/30 of the parameters yet matches the performance of many 1 trillion parameter models [2] - In key benchmark tests, MiroThinker-v1.5-235B ranks among the top globally, showcasing its efficiency [2][6] Competitive Analysis - MiroThinker-v1.5-30B shows competitive performance against Kimi-K2-Thinking, which has 30 times more parameters, with a significantly lower inference cost of $0.07 per call, just 1/20 of Kimi-K2-Thinking's cost [7] - The model's performance in the BrowseComp-ZH benchmark indicates that larger models do not necessarily equate to stronger performance [7] Technological Innovations - MiroThinker 1.5 introduces "Interactive Scaling," shifting focus from internal parameter expansion to external information interaction, enhancing model performance [8][9] - The model employs a "scientist mode" for reasoning, emphasizing evidence-seeking and iterative verification to avoid hallucinations and ensure accuracy [9][12] Training Methodology - The training process incorporates a time-sensitive sandbox, ensuring the model learns to make predictions based on past information without future leakage [14] - MiroThinker 1.5 is trained to actively seek evidence, verify hypotheses, and correct itself, fostering a robust reasoning process [12][13] Market Applications - MiroMind's predictive capabilities have been demonstrated in stock market scenarios, accurately forecasting stock performance amidst market fluctuations [15][19] - The model is positioned to impact major tech companies, with upcoming events likely to influence stock prices and market dynamics [25]