具有类人协作工具的AI智能体：增强问题解决的适应性策略
AI Agents with Human-Like Collaborative Tools: Adaptive Strategies for Enhanced Problem-Solving
AI总结：
研究赋予LLM智能体类人协作工具与自主权，发现这些工具在难题上显著降低成本和轮次并提升速度，且不同模型自然采用不同协作策略，表明自适应协作界面可作为推理增强器。
摘要：
我们研究赋予LLM智能体人类解决问题时自然使用的协作工具和自主权是否能提升其性能。我们为Claude Code智能体配备了基于MCP的社交媒体和日志工具，并允许它们自行决定如何使用。在34个Aider Polyglot Python编程挑战中，协作工具显著提升了最难问题的表现，与基线智能体相比，成本降低15-40%，轮次减少12-27%，完成速度提升12-38%。对全部挑战集的影响则好坏参半，表明这些工具在额外推理支撑最需要时发挥性能增强作用。令人惊讶的是，不同模型在没有明确指示下自然采用了不同的协作策略。Sonnet 3.7广泛使用各类工具，并受益于基于表达的认知支撑。Sonnet 4表现出选择性采用，在问题确实困难时依赖基于日志的语义搜索。这反映了人类开发者如何根据专业知识和任务复杂性调整协作。行为分析显示智能体偏好写作而非阅读约2-9倍，表明结构化表达而非单纯信息获取推动了大部分改进。总体而言，AI智能体能在其能力边界系统性地受益于受人类启发的协作工具，表明自适应协作界面是推理增强器而非普遍效率提升手段。
英文摘要：
We investigate whether giving LLM agents the collaborative tools and autonomy that humans naturally use for problem solving can improve their performance. We equip Claude Code agents with MCP-based social media and journaling tools and allow them to use these tools as they see fit. Across 34 Aider Polyglot Python programming challenges, collaborative tools substantially improve performance on the hardest problems, delivering 15-40% lower cost, 12-27% fewer turns, and 12-38% faster completion than baseline agents. Effects on the full challenge set are mixed, suggesting these tools act as performance enhancers when additional reasoning scaffolding is most needed. Surprisingly, Different models naturally adopted distinct collaborative strategies without explicit instruction. Sonnet 3.7 engaged broadly across tools and benefited from articulation-based cognitive scaffolding. Sonnet 4 showed selective adoption, leaning on journal-based semantic search when problems were genuinely difficult. This mirrors how human developers adjust collaboration based on expertise and task complexity. Behavioral analysis shows agents prefer writing over reading by about 2-9x, indicating that structured articulation drives much of the improvement rather than information access alone. Overall, AI agents can systematically benefit from human-inspired collaboration tools at the edge of their capabilities, pointing to adaptive collaborative interfaces as reasoning enhancers rather than universal efficiency boosts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
