Group 1 - The first step to invest in US stocks is to open an account with a legally qualified intermediary, which can be a domestic broker with relevant business licenses or a compliant foreign broker [1] - Investors must complete identity verification and risk tolerance assessments during the account opening process, and intermediaries will review the appropriateness of investors according to regulatory requirements [1] - Legal regulations and compliance are fundamental for investing in US stocks, requiring adherence to current rules from the SEC and FINRA, as well as updates from the 2025 revised legislation regarding cross-border investment information reporting and investor protection [1] Group 2 - Familiarity with US stock trading rules is crucial to avoid operational risks, including the T+0 trading system and restrictions on day trading for accounts with net assets below $25,000 [1] - The trading hours differ between summer and winter time, and there are various levels of circuit breakers that trigger trading halts during significant market fluctuations [1] - Tax costs and exchange rate risks must be considered, as non-US residents are subject to withholding tax on dividends, and fluctuations in the RMB-USD exchange rate can directly impact actual returns [2] Group 3 - Information acquisition and risk awareness are equally important, as the information disclosure standards in the US market differ from those in the domestic market [2] - Investors should obtain company announcements and financial reports through legitimate channels to ensure accuracy and timeliness [2] - The US market also presents risks such as market volatility and industry cycles, necessitating that investors make decisions based on their risk tolerance [2]

Core Insights - The mobile internet market is entering a phase of stock competition in 2026, contrary to the belief that app user acquisition (UA) has reached its end. The demand for acquiring new users for new products and revitalizing existing products remains high [1] Group 1: Key Channels for User Acquisition - Core Channel One: Official Direct Business and Aggregation Service Platforms - Official channels are the top priority for project acquisition, as they provide the most stable settlement cycles and highest unit prices. Statistics show that profits from direct connections to major companies are typically over 30% higher than those from multi-layered subcontracting channels [2] - For small to medium-sized teams, aggregation service platforms are a more realistic choice, as they consolidate numerous UA project resources and offer unified settlement and management tools. Research indicates that over 60% of small studios rely on such platforms for survival [2] - Core Channel Two: Industry Vertical Business Resource Connection Communities - Vertical business resource platforms serve as "resource distribution centers" within the industry, gathering numerous parties. These platforms offer high information transparency, allowing for clear comparisons of different channels' deduction ratios and settlement efficiencies [3] - These specialized platforms provide access to various ground promotion projects, such as rapid version promotion and financial account openings, with the advantage of high flexibility to switch to high-conversion products based on market trends [3] Group 2: Identifying High-Quality Project Resources - A reliable UA project typically has three characteristics: 1. Pricing aligns with market fair value, as excessively high prices often indicate settlement traps 2. A closed-loop chain that can provide real-time or near-real-time data feedback 3. Possession of official authorization or clear compliance requirements [4] Group 3: Industry Perspective - From the perspective of 2026, UA has evolved from a simple manpower competition to a contest of resource integration and information acquisition capabilities. Finding a stable and efficient application promotion task acquisition channel is just the first step for studios [5] - The deeper competitive advantage lies in sensitivity to traffic trends and understanding compliance boundaries. Engaging with resource platforms that offer diversified business collaboration can help quickly identify core value in an information-overloaded environment, leading to sustainable growth [5]

Core Insights - The article discusses the evolution of Deep Research (DR) as a new direction in AI, moving from simple dialogue and creative writing applications to more complex research-oriented tasks. It highlights the limitations of traditional retrieval-augmented generation (RAG) methods and introduces DR as a solution for multi-step reasoning and long-term research processes [2][30]. Summary by Sections Definition of Deep Research - DR is not a specific model or technology but a progressive capability pathway for research-oriented agents, evolving from information retrieval to complete research workflows [5]. Stages of Capability Development - **Stage 1: Agentic Search** - Models gain the ability to actively search and retrieve information dynamically based on intermediate results, focusing on efficient information acquisition [5]. - **Stage 2: Integrated Research** - Models evolve to understand, filter, and integrate multi-source evidence, producing coherent reports [6]. - **Stage 3: Full-stack AI Scientist** - Models can propose research hypotheses, design and execute experiments, and reflect on results, emphasizing depth of reasoning and autonomy [6]. Core Components of Deep Research - **Query Planning** - Involves deciding what information to query next, incorporating dynamic adjustments in multi-round research [10]. - **Information Retrieval** - Focuses on when to retrieve, what to retrieve, and how to filter retrieved information to avoid redundancy and ensure relevance [12][13][14]. - **Memory Management** - Essential for long-term reasoning, involving memory consolidation, indexing, updating, and forgetting [15]. - **Answer Generation** - Stresses the logical consistency between conclusions and evidence, requiring integration of multi-source evidence [17]. Training and Optimization Methods - **Prompt Engineering** - Involves designing multi-step prompts to guide the model through research processes, though its effectiveness is highly dependent on prompt design [20]. - **Supervised Fine-tuning** - Utilizes high-quality reasoning trajectories for model training, though acquiring annotated data can be costly [21]. - **Reinforcement Learning for Agents** - Directly optimizes decision-making strategies in multi-step processes without complex annotations [22]. Challenges in Deep Research - **Coordination of Internal and External Knowledge** - Balancing reliance on internal reasoning versus external information retrieval is crucial [24]. - **Stability of Training Algorithms** - Long-term task training often faces issues like policy degradation, limiting exploration of diverse reasoning paths [24]. - **Evaluation Methodology** - Developing reliable evaluation methods for research-oriented agents remains an open question, with existing benchmarks needing further exploration [25][27]. - **Memory Module Construction** - Balancing memory capacity, retrieval efficiency, and information reliability is a significant challenge [28]. Conclusion - Deep Research represents a shift from single-turn answer generation to in-depth research addressing open-ended questions. The field is still in its early stages, with ongoing exploration needed to create autonomous and trustworthy DR agents [30].