并行AI智能体协作的图结构分布式块优化
原标题：Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
AI 摘要
该论文研究并行 AI 智能体协作中的协调问题：多个智能体各自对共享方案做出局部改进，但合并后可能相互干扰甚至导致整体退化。作者将问题建模为具有精确可评估目标和已知依赖图的离散块优化，利用依赖图的独立集来认证兼容的编辑集合，并证明基于最优分数着色的安全合并可保留一定比例的已验证局部改进，且该比例在仅从提交提案中选择子集进行合并的策略下是最优的。实验在代码修复和路网任务上验证了该方法能解决朴素合并失败的冲突，并在部分场景下减少完成时间。
正文节选
Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization Abstract. Parallel AI agents can each produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph. The graph certifies compatible sets of edits through its independent sets, and evaluating proposals against the same current solution yields verified gains tha