标题： 运行时通过元自我精炼竞争约束修复语言模型流水线 显示英文标题

标题： Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime

摘要： 语言模型（LM）流水线可以针对程序约束动态优化其输出。 然而，当面对相互竞争的软约束时，它们的效果会崩溃，导致满足一个约束会违反另一个约束的低效回溯循环。 我们引入了元自修正，这是一种框架，为LM流水线配备了一个元校正层，在运行时/推理时修复这些竞争。 我们的方法监控流水线的执行历史以检测振荡性故障。 一旦检测到，它会调用一个分析后退尝试整体状态并合成平衡竞争需求的战略指令的元修复器LM。 这个自修复指令引导原始LM走出失败的优化循环，达到成功的输出。 我们的结果表明，元自修正可以成功修复这些循环，从而提高LM程序的效率。 显示英文摘要

摘要： Language Model (LM) pipelines can dynamically refine their outputs against programmatic constraints. However, their effectiveness collapses when faced with competing soft constraints, leading to inefficient backtracking loops where satisfying one constraint violates another. We introduce Meta Self-Refining, a framework that equips LM pipelines with a meta-corrective layer to repair these competitions at runtime/inference-time. Our approach monitors the pipeline's execution history to detect oscillatory failures. Upon detection, it invokes a meta-repairer LM that analyzes the holistic state of the backtracking attempts and synthesizes a strategic instruction to balance the competing requirements. This self-repair instruction guides the original LM out of a failing refining loop towards a successful output. Our results show Meta Self-Refining can successfully repair these loops, leading to more efficient LM programs.