Core Insights - The article emphasizes the shift in focus within the industry from "can the model do it" to "can the agent be deployed" as large model capabilities advance [2] - It highlights the importance of efficiency in deploying intelligent agents, as high performance at a high cost is not sustainable for large-scale production [2] Group 1: Memory of Intelligent Agents - Efficient memory systems are crucial for intelligent agents to handle long tasks without overwhelming token usage and degrading performance [6] - The paper outlines a three-step memory lifecycle: construction, management, and access, focusing on transforming long dialogues into usable memory while balancing cost and fidelity [7] - The trend of multi-agent memory is emerging, categorized into shared memory, local memory, and hybrid memory [8] Group 2: Tool Learning - Tools enable agents to transition from "speaking" to "doing," but costs can escalate quickly in the toolchain [9] - The paper identifies three main strategies for improving efficiency: tool selection, tool invocation, and tool fusion reasoning [11] Group 3: Planning of Intelligent Agents - Planning determines how agents act in multi-step decision spaces, with efficiency issues arising from either single-agent reasoning or multi-agent collaboration [15] - The paper discusses memory management strategies to prevent "memory explosion" and ensure efficient retrieval [12] - It emphasizes the need for effective tool selection and invocation to minimize delays and unnecessary calls [13] Group 4: Benchmarking and Evaluation - Establishing a clear benchmark for efficiency is essential, as efficiency must be built on effectiveness [16] - The paper reviews existing benchmarks that incorporate efficiency signals and summarizes common efficiency metrics used in agent methodologies [17] Group 5: Challenges and Future Directions - The paper outlines several challenges, including the need for a unified evaluation framework and the exploration of latent reasoning in intelligent agents [19] - It highlights the importance of considering deployment costs in multi-agent scenarios and the need for efficiency research in multi-modal agents [20] - Strategies for single-agent efficiency focus on adaptive budgeting and structured search, while multi-agent strategies aim to reduce communication costs without losing information [21]