ReIn：基于推理初始化的对话错误恢复机制

摘要

基於大型語言模型（LLM）並整合工具功能的對話代理，在固定任務型對話數據集上表現出色，但面對用戶誘發的意外錯誤時仍顯脆弱。與其專注於錯誤預防，本研究著眼於錯誤恢復——這需要精準診斷錯誤對話情境並執行適當的恢復方案。在現實條件限制下（因高昂成本與時間需求而無法進行模型微調或提示修改），我們探討代理能否從情境缺陷的互動中恢復，以及如何在不改變模型參數與提示的前提下調整其行為。為此，我們提出「推理植入」（ReIn），一種在測試階段介入的方法，將初始推理植入代理的決策流程。具體而言，外部植入模組會識別對話情境中的預定義錯誤並生成恢復方案，隨後將這些方案整合至代理的內部推理過程以引導修正行動，且無需修改其參數或系統提示。我們通過系統性模擬阻礙用戶目標達成的對話失敗情境（用戶模糊請求與未支持請求）來評估ReIn。在多種代理模型與植入模組的組合測試中，ReIn顯著提升任務成功率，並能泛化至未見過的錯誤類型。此外，其表現持續優於顯式提示修改方法，凸顯其作為高效即時方法的實用價值。針對其運行機制（特別是與指令層級結構的關聯性）的深入分析表明，結合ReIn定義恢復工具可成為提升對話代理韌性的安全有效策略，且無需修改骨幹模型或系統提示。

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

Support