ReIn: Conversational Error Recovery with Reasoning Inception

2026-02-18

AI Summary

This work addresses the challenge that large language model–driven conversational agents struggle to recover from user-induced errors under real-world constraints where model fine-tuning or prompt modification is infeasible. To this end, the authors propose Reasoning Inception (ReIn), a test-time intervention framework that dynamically detects predefined error types via an external module, generates a recovery plan, and injects it into the agent’s reasoning process as a chain of thought—without altering model parameters or system prompts. ReIn represents the first approach to enable dynamic diagnosis and recovery from dialogue errors without any modifications to the underlying model or prompts. Experiments demonstrate that it significantly improves task success rates across diverse agent models and error categories, outperforming explicit prompt-editing baselines and exhibiting strong generalization to unseen error types.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• conversational error recovery
• user-induced errors
• dialogue context diagnosis
• task-oriented dialogue
• resilience of conversational agents

Innovation

• Reasoning Inception
• error recovery
• conversational agents
• test-time intervention
• tool integration