ReIn: Conversational Error Recovery with Reasoning Inception

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.

💡 Research Summary

The paper tackles a practical problem in deploying large‑language‑model (LLM) based conversational agents: how to recover from user‑induced errors when the agent’s parameters and system prompt cannot be altered because of cost, time, or proprietary constraints. Existing work mainly focuses on error prevention (e.g., clarification questions) or on fine‑tuning, chain‑of‑thought prompting, or prompt engineering, all of which require changes to the model or its prompt. The authors therefore propose a test‑time intervention called Reasoning Inception (ReIn) that injects a single “reasoning block” into the agent’s internal decision‑making process without touching the model weights or the system prompt.

ReIn works as follows. An external inception module (implemented as an LLM) receives the surface dialogue context (the user utterance and prior turns) together with the list of available tools and a mapping Φ from a predefined set of error types E to concrete recovery plans. The module first decides whether any known error is present. If no error is detected, it returns “No” and the agent proceeds unchanged. If an error is detected, the module returns “Yes” plus a fully instantiated recovery plan ρ. This plan is wrapped in a “think