ReIn：基于推理初始化的对话错误恢复机制

摘要

基于大语言模型（LLM）并集成工具功能的对话智能体，在固定任务导向的对话数据集上表现优异，但仍易受用户引发的意外错误影响。与侧重错误预防的研究不同，本研究聚焦于错误恢复机制，其核心在于精准诊断错误对话语境并执行有效的恢复方案。在现实约束下（因高昂成本与时间消耗而无法进行模型微调或提示修改），我们探究智能体能否从存在语境缺陷的交互中恢复，以及如何在不改变模型参数与提示的前提下调整其行为。为此，我们提出推理植入（ReIn）方法——一种测试时干预技术，将初始推理逻辑植入智能体的决策流程。具体而言，外部植入模块会识别对话语境中预定义的错误并生成恢复方案，随后将该方案融入智能体的内部推理过程以引导纠错行动，且无需修改其参数或系统提示。我们通过系统化模拟阻碍用户目标达成的对话失败场景（用户模糊请求与未支持请求）来评估ReIn的效果。在不同智能体模型与植入模块的组合实验中，ReIn显著提升了任务成功率，并能泛化至未见错误类型。此外，其性能持续优于显式提示修改方法，凸显其作为高效实时解决方案的实用价值。对其运行机制（特别是与指令层级结构的关联）的深入分析表明，结合ReIn定义恢复工具可作为提升对话智能体鲁棒性的安全有效策略，且无需修改主干模型或系统提示。

English

Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses on error recovery, which necessitates the accurate diagnosis of erroneous dialogue contexts and execution of proper recovery plans. Under realistic constraints precluding model fine-tuning or prompt modification due to significant cost and time requirements, we explore whether agents can recover from contextually flawed interactions and how their behavior can be adapted without altering model parameters and prompts. To this end, we propose Reasoning Inception (ReIn), a test-time intervention method that plants an initial reasoning into the agent's decision-making process. Specifically, an external inception module identifies predefined errors within the dialogue context and generates recovery plans, which are subsequently integrated into the agent's internal reasoning process to guide corrective actions, without modifying its parameters or system prompts. We evaluate ReIn by systematically simulating conversational failure scenarios that directly hinder successful completion of user goals: user's ambiguous and unsupported requests. Across diverse combinations of agent models and inception modules, ReIn substantially improves task success and generalizes to unseen error types. Moreover, it consistently outperforms explicit prompt-modification approaches, underscoring its utility as an efficient, on-the-fly method. In-depth analysis of its operational mechanism, particularly in relation to instruction hierarchy, indicates that jointly defining recovery tools with ReIn can serve as a safe and effective strategy for improving the resilience of conversational agents without modifying the backbone models or system prompts.

ReIn：基于推理初始化的对话错误恢复机制

ReIn: Conversational Error Recovery with Reasoning Inception

摘要

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