提出分层技能图框架，让LLM代理通过结构化技能更高效协作与决策。
arXiv:2607.25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse pa…
用原子任务图统一智能体的规划与执行，为复杂任务拆解提供新范式，值得研究 Agent 的开发者细读。
arXiv:2607.01942v1 Announce Type: new Abstract: LLM-based agents have shown strong potential for solving complex multi-step tasks, yet existing perfor…
用AST引导任务分解提升NL2SQL性能，ICLR'26最新论文提出LearNAT方法。
arXiv:2504.02327v2 Announce Type: replace Abstract: Natural Language to SQL (NL2SQL) aims to translate natural language queries into executable SQL st…
用提示词将Codex变身编排管理器，自动分解任务、分配工作线程并监控完成状态，提升AI工作流效率。
The mistake with coding agents is treating them like a single chat window. You paste a task. The agent writes a patch. You check it. Something is miss…
用代理式大模型巧妙分解长周期任务，突破上下文窗口限制，实现深度研究新范式。
arXiv:2606.09730v1 Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose…
融合行为树与大语言模型，实现组合任务的智能奖励塑形与动作掩码，提升多步推理效率。
arXiv:2605.05795v2 Announce Type: replace Abstract: Decomposing complex tasks into a sequence of simpler subtasks can improve learning efficiency for …
最新arXiv论文揭秘思维链如何一步步拆解复杂任务，深入剖析其内在分解机制。
arXiv:2604.08872v2 Announce Type: replace Abstract: Many language tasks can be modeled as classification problems where a large language model (LLM) i…
规范驱动开发让Claude Code效率翻倍：任务分解+上下文清理，榨干编码代理潜能
Spec Driven Development approach allows to squeeze more from coding agents thanks to few strong concepts: - decomposition across two dimensions. first…
OpenAI提出迭代放大技术，通过任务分解实现超人类复杂目标的AI安全方法，虽处早期但具可扩展潜力。
We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale,…
LLM驱动的编码系统常因单体提示而脆弱难调试，这篇论文提出运行时结构化任务分解，让失败诊断与重试更高效。
arXiv:2605.15425v1 Announce Type: cross Abstract: Agentic coding systems increasingly use large language models (LLMs) for software engineering tasks …