Project2Task：面向自主研究的图引导项目级规划
Project2Task: Graph-Guided Project-Level Planning for Autonomous Research
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中文总结 AI 辅助
该研究提出Project2Task图引导项目级规划层，可将研究项目转化为感知依赖的边界任务，在基准测试中其任务组合质量优于基线，与AutoResearchClaw整合后提升了下游任务准确率。
中文摘要 AI 辅助
研究智能体如今可从单一主题出发完成文献检索、假说提出、代码生成、实验运行及手稿撰写等工作。但研究项目并非简单的更大任务，而是需通过多个边界明确、目标各异却相互关联、存在并行替代方案且需遵循依赖关系序列的任务推进的长期议程。现有单任务系统常将项目视为单个超大任务，生成一组模糊或重叠的扁平任务，或把任务边界与执行顺序留给人工协调。本文提出Project2Task，这是一种面向自主研究的图引导项目级规划层：给定项目概要，它将候选贡献表示为创新原子，并将其组织为有向谱系图；轻量级伯努利块模型目标会在横向、纵向及混合组合分解中进行选择；Project2Task随后生成具有明确贡献归属的边界任务，修正重叠与缺失执行领域，并输出感知依赖的任务契约，该契约规定目标、输入、预期产物、评估要求、边界约束、依赖关系及执行顺序，且契约独立于任何特定下游研究执行器，支持将任务输出整合为连贯的项目级结果。在包含10个项目概要（产生约30个任务）的基准测试中，基于手稿的组合评估显示，Project2Task的平均质量得分为7.15，而Brief Baseline的得分为4.58，Topic-only Setting的得分为5.31；将其契约与AutoResearchClaw整合后，下游任务的平均准确率从0.536提升至0.759。这些结果表明，明确的项目到任务规划对于生成连贯、非冗余且可执行的研究任务组合具有重要价值。
英文摘要
Research agents can increasingly search literature, propose hypotheses, generate code, run experiments, and draft manuscripts from a single topic. However, a research project is not merely a larger task: it is a long-horizon agenda that must be advanced through multiple bounded tasks with distinct but related objectives, parallel alternatives, and dependency-aware sequences. Existing single-task systems often treat the project as one oversized task, produce a flat set of vague or overlapping tasks, or leave task boundaries and execution order to manual coordination. We introduce Project2Task, a graph-guided project-level planning layer for autonomous research. Given a project brief, it represents candidate contributions as innovation atoms and organizes them in a directed lineage graph. A lightweight Bernoulli block-model objective selects among horizontal, vertical, and hybrid portfolio decompositions. Project2Task then generates bounded tasks with explicit contribution ownership, repairs overlaps and missing execution fields, and emits dependency-aware task contracts that specify objectives, inputs, expected artifacts, evaluation requirements, boundary constraints, dependencies, and execution order. The contracts are independent of any particular downstream research executor and support integration of task outputs into a coherent project-level result. On a benchmark of ten project briefs yielding roughly 30 tasks, manuscript-based portfolio evaluation gives Project2Task an average quality score of 7.15, compared with 4.58 for the Brief Baseline and 5.31 for the Topic-only Setting. Integrating its contracts with AutoResearchClaw increases average downstream task accuracy from 0.536 to 0.759. These results demonstrate the value of explicit project-to-task planning for producing coherent, non-redundant, and executable research-task portfolios.
