Core Insights - The article discusses the integration of AI capabilities into daily life and work, emphasizing the importance of robust search agents that can navigate complex environments effectively [1][2]. Group 1: RE-Searcher Framework - The RE-Searcher framework is introduced, which employs goal-oriented planning and self-reflection to enhance the robustness of search agents [3][6]. - This framework has achieved state-of-the-art (SOTA) performance across multiple open-domain question-answering and multi-hop reasoning tasks, demonstrating significant resilience against environmental noise and search vulnerabilities [3][22]. Group 2: Search Environment Challenges - The search environment can act as a double-edged sword, providing information gain while also amplifying errors, leading to instability in model performance [6][9]. - Analysis shows that the complexity of the search environment can significantly increase the inherent randomness of models, resulting in inconsistent outcomes for the same queries [9][11]. Group 3: Goal-Oriented Planning and Self-Reflection - The two key cognitive behaviors mimicked in the RE-Searcher framework are "goal-oriented planning" and "self-reflection," which help the AI to clarify its objectives before searching and to evaluate the relevance of the results afterward [16][17]. - The training mechanism involves specific instruction templates to guide the agent's thought processes, with a teacher model providing feedback to improve self-reflection accuracy [16][19]. Group 4: Experimental Results - RE-Searcher has shown superior performance on seven mainstream search question-answer datasets, outperforming existing baseline models and achieving new SOTA levels [22][25]. - The introduction of reflection rewards significantly enhances the model's self-reflection accuracy, reducing the random correctness rate from 17.09% to 8.74% for the 7B model, indicating improved problem-solving stability [25][30]. Group 5: Robustness Against Noise - In stress tests simulating real-world noise, RE-Searcher demonstrated strong robustness, with performance degradation significantly lower than baseline models, indicating its ability to maintain accuracy despite initial errors [27][30].